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Best Data Science and Machine Learning Platforms

Matthew Miller
MM
Researched and written by Matthew Miller

Data science and machine learning (DSML) platforms provide users with tools to build, deploy, and monitor machine learning algorithms. These software platforms combine intelligent, decision-making algorithms with data, thereby enabling developers to create a business solution. Some data science and machine learning platforms offer prebuilt algorithms and simplistic workflows with features such as drag-and-drop modeling and visual interfaces that easily connect necessary data to the end solution, while others require a greater knowledge of development and coding. These algorithms can include functionality for image recognition, natural language processing, voice recognition, and recommendation systems, in addition to other machine learning capabilities.

The nature of some DSML engineering platforms enables users without intensive data science skills to benefit from the platforms’ features. AI platforms are very similar to platforms as a service (PaaS), which allow for basic application development, but these products differ by offering machine learning options.

To qualify for inclusion in the Data Science and Machine Learning (DSML) Platforms category, a product must:

Present a way for developers to connect data to the algorithms for them to learn and adapt
Allow users to create machine learning algorithms and/or offer prebuilt machine learning algorithms for more novice users
Provide a platform for deploying AI at scale

Best Data Science and Machine Learning Platforms At A Glance

Best for Small Businesses:
Best for Mid-Market:
Best for Enterprise:
Highest User Satisfaction:
Best Free Software:
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Best for Enterprise:
Highest User Satisfaction:
Best Free Software:

G2 takes pride in showing unbiased reviews on user satisfaction in our ratings and reports. We do not allow paid placements in any of our ratings, rankings, or reports. Learn about our scoring methodologies.

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250 Listings in Data Science and Machine Learning Platforms Available
(511)4.3 out of 5
7th Easiest To Use in Data Science and Machine Learning Platforms software
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Entry Level Price:Pay As You Go
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and

    Users
    • Software Engineer
    • Data Scientist
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 38% Small-Business
    • 35% Enterprise
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Vertex AI Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    211
    Model Variety
    123
    Features
    121
    Machine Learning
    110
    Integrations
    85
    Cons
    Expensive
    59
    Performance Issues
    53
    Learning Curve
    50
    Complexity
    46
    Complexity Issues
    43
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Vertex AI features and usability ratings that predict user satisfaction
    8.3
    Application
    Average: 8.6
    8.3
    Managed Service
    Average: 8.3
    8.6
    Natural Language Understanding
    Average: 8.4
    7.9
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Company Website
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,520,271 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    301,875 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and

Users
  • Software Engineer
  • Data Scientist
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 38% Small-Business
  • 35% Enterprise
Vertex AI Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
211
Model Variety
123
Features
121
Machine Learning
110
Integrations
85
Cons
Expensive
59
Performance Issues
53
Learning Curve
50
Complexity
46
Complexity Issues
43
Vertex AI features and usability ratings that predict user satisfaction
8.3
Application
Average: 8.6
8.3
Managed Service
Average: 8.3
8.6
Natural Language Understanding
Average: 8.4
7.9
Ease of Admin
Average: 8.5
Seller Details
Seller
Google
Company Website
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,520,271 Twitter followers
LinkedIn® Page
www.linkedin.com
301,875 employees on LinkedIn®
(403)4.6 out of 5
Optimized for quick response
4th Easiest To Use in Data Science and Machine Learning Platforms software
View top Consulting Services for Databricks Data Intelligence Platform
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of the Fortune 500 — rely on the Databricks Data

    Users
    • Data Engineer
    • Data Scientist
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 47% Enterprise
    • 34% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Databricks Data Intelligence Platform Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    182
    Features
    165
    Integrations
    104
    Data Management
    91
    Easy Integrations
    88
    Cons
    Learning Curve
    55
    Missing Features
    52
    Steep Learning Curve
    52
    Expensive
    49
    Performance Issues
    36
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Databricks Data Intelligence Platform features and usability ratings that predict user satisfaction
    8.6
    Application
    Average: 8.6
    8.4
    Managed Service
    Average: 8.3
    8.3
    Natural Language Understanding
    Average: 8.4
    8.1
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Company Website
    Year Founded
    1999
    HQ Location
    San Francisco, CA
    Twitter
    @databricks
    75,952 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    9,769 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of the Fortune 500 — rely on the Databricks Data

Users
  • Data Engineer
  • Data Scientist
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 47% Enterprise
  • 34% Mid-Market
Databricks Data Intelligence Platform Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
182
Features
165
Integrations
104
Data Management
91
Easy Integrations
88
Cons
Learning Curve
55
Missing Features
52
Steep Learning Curve
52
Expensive
49
Performance Issues
36
Databricks Data Intelligence Platform features and usability ratings that predict user satisfaction
8.6
Application
Average: 8.6
8.4
Managed Service
Average: 8.3
8.3
Natural Language Understanding
Average: 8.4
8.1
Ease of Admin
Average: 8.5
Seller Details
Company Website
Year Founded
1999
HQ Location
San Francisco, CA
Twitter
@databricks
75,952 Twitter followers
LinkedIn® Page
www.linkedin.com
9,769 employees on LinkedIn®

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(244)4.5 out of 5
3rd Easiest To Use in Data Science and Machine Learning Platforms software
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Entry Level Price:Free
  • Overview
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  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and p

    Users
    • Student
    • Data Scientist
    Industries
    • Computer Software
    • Higher Education
    Market Segment
    • 71% Small-Business
    • 20% Mid-Market
    User Sentiment
    How are these determined?Information
    These insights, currently in beta, are compiled from user reviews and grouped to display a high-level overview of the software.
    • Deepnote is a platform that allows users to transform python notebooks into user-friendly apps and share them using a link, with features such as real-time collaboration, AI auto-complete, and integration with external data sources.
    • Users like the ease of usage, quick visualization capability, usefulness of the autocomplete, the simple and intuitive UI, the smooth integration of SQL and python code, and the ability for multiple people to edit the same Jupyter notebook.
    • Reviewers experienced minor difficulties with python, issues with the platform being slow for larger notebooks, challenges with onboarding as a non-admin user, and limitations with the CPU power provided with the free account.
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Deepnote Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    116
    Collaboration
    75
    Team Collaboration
    55
    Easy Integrations
    46
    Useful
    46
    Cons
    Slow Performance
    36
    Bugs
    21
    Lagging Performance
    18
    Limited Features
    18
    Data Management Issues
    17
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Deepnote features and usability ratings that predict user satisfaction
    8.1
    Application
    Average: 8.6
    8.0
    Managed Service
    Average: 8.3
    7.3
    Natural Language Understanding
    Average: 8.4
    8.8
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Deepnote
    Year Founded
    2019
    HQ Location
    San Francisco , US
    Twitter
    @DeepnoteHQ
    5,147 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    38 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and p

Users
  • Student
  • Data Scientist
Industries
  • Computer Software
  • Higher Education
Market Segment
  • 71% Small-Business
  • 20% Mid-Market
User Sentiment
How are these determined?Information
These insights, currently in beta, are compiled from user reviews and grouped to display a high-level overview of the software.
  • Deepnote is a platform that allows users to transform python notebooks into user-friendly apps and share them using a link, with features such as real-time collaboration, AI auto-complete, and integration with external data sources.
  • Users like the ease of usage, quick visualization capability, usefulness of the autocomplete, the simple and intuitive UI, the smooth integration of SQL and python code, and the ability for multiple people to edit the same Jupyter notebook.
  • Reviewers experienced minor difficulties with python, issues with the platform being slow for larger notebooks, challenges with onboarding as a non-admin user, and limitations with the CPU power provided with the free account.
Deepnote Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
116
Collaboration
75
Team Collaboration
55
Easy Integrations
46
Useful
46
Cons
Slow Performance
36
Bugs
21
Lagging Performance
18
Limited Features
18
Data Management Issues
17
Deepnote features and usability ratings that predict user satisfaction
8.1
Application
Average: 8.6
8.0
Managed Service
Average: 8.3
7.3
Natural Language Understanding
Average: 8.4
8.8
Ease of Admin
Average: 8.5
Seller Details
Seller
Deepnote
Year Founded
2019
HQ Location
San Francisco , US
Twitter
@DeepnoteHQ
5,147 Twitter followers
LinkedIn® Page
www.linkedin.com
38 employees on LinkedIn®
(294)4.8 out of 5
2nd Easiest To Use in Data Science and Machine Learning Platforms software
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Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack. Quickly spin up environments to test new idea

    Users
    • Data Scientist
    • Software Engineer
    Industries
    • Computer Software
    • Higher Education
    Market Segment
    • 82% Small-Business
    • 12% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Saturn Cloud Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    120
    Free Services
    42
    Setup Ease
    42
    GPU Performance
    38
    User Interface
    31
    Cons
    Limited Hours
    17
    Limited Free Access
    15
    Missing Features
    14
    Slow Startup
    13
    Expensive
    12
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Saturn Cloud features and usability ratings that predict user satisfaction
    9.2
    Application
    Average: 8.6
    9.2
    Managed Service
    Average: 8.3
    9.1
    Natural Language Understanding
    Average: 8.4
    9.2
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Company Website
    Year Founded
    2018
    HQ Location
    New York, US
    Twitter
    @saturn_cloud
    3,307 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    37 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack. Quickly spin up environments to test new idea

Users
  • Data Scientist
  • Software Engineer
Industries
  • Computer Software
  • Higher Education
Market Segment
  • 82% Small-Business
  • 12% Mid-Market
Saturn Cloud Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
120
Free Services
42
Setup Ease
42
GPU Performance
38
User Interface
31
Cons
Limited Hours
17
Limited Free Access
15
Missing Features
14
Slow Startup
13
Expensive
12
Saturn Cloud features and usability ratings that predict user satisfaction
9.2
Application
Average: 8.6
9.2
Managed Service
Average: 8.3
9.1
Natural Language Understanding
Average: 8.4
9.2
Ease of Admin
Average: 8.5
Seller Details
Company Website
Year Founded
2018
HQ Location
New York, US
Twitter
@saturn_cloud
3,307 Twitter followers
LinkedIn® Page
www.linkedin.com
37 employees on LinkedIn®
(625)4.6 out of 5
Optimized for quick response
1st Easiest To Use in Data Science and Machine Learning Platforms software
View top Consulting Services for Alteryx
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    The Alteryx AI Platform for Enterprise Analytics offers integrated generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities. The platform is powered

    Users
    • Data Analyst
    • Consultant
    Industries
    • Financial Services
    • Accounting
    Market Segment
    • 64% Enterprise
    • 22% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Alteryx Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    146
    Intuitive
    55
    Automation
    53
    Easy Learning
    46
    Ease of Learning
    43
    Cons
    Learning Curve
    40
    Expensive
    34
    Learning Difficulty
    25
    Complexity
    19
    Missing Features
    17
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Alteryx features and usability ratings that predict user satisfaction
    8.8
    Application
    Average: 8.6
    8.0
    Managed Service
    Average: 8.3
    7.9
    Natural Language Understanding
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Alteryx
    Company Website
    Year Founded
    1997
    HQ Location
    Irvine, CA
    Twitter
    @alteryx
    26,732 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    2,287 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

The Alteryx AI Platform for Enterprise Analytics offers integrated generative and conversational AI, data preparation, advanced analytics, and automated reporting capabilities. The platform is powered

Users
  • Data Analyst
  • Consultant
Industries
  • Financial Services
  • Accounting
Market Segment
  • 64% Enterprise
  • 22% Mid-Market
Alteryx Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
146
Intuitive
55
Automation
53
Easy Learning
46
Ease of Learning
43
Cons
Learning Curve
40
Expensive
34
Learning Difficulty
25
Complexity
19
Missing Features
17
Alteryx features and usability ratings that predict user satisfaction
8.8
Application
Average: 8.6
8.0
Managed Service
Average: 8.3
7.9
Natural Language Understanding
Average: 8.4
8.3
Ease of Admin
Average: 8.5
Seller Details
Seller
Alteryx
Company Website
Year Founded
1997
HQ Location
Irvine, CA
Twitter
@alteryx
26,732 Twitter followers
LinkedIn® Page
www.linkedin.com
2,287 employees on LinkedIn®
  • Overview
    Expand/Collapse Overview
  • Users
    No information available
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 53% Small-Business
    • 27% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Deep Learning VM Image Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    25
    Features
    15
    Setup Ease
    14
    Easy Setup
    11
    Cloud Computing
    10
    Cons
    Expensive
    14
    Learning Difficulty
    7
    Cost
    6
    Difficult Learning
    6
    Lagging Performance
    5
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Deep Learning VM Image features and usability ratings that predict user satisfaction
    8.8
    Application
    Average: 8.6
    8.5
    Managed Service
    Average: 8.3
    8.4
    Natural Language Understanding
    Average: 8.4
    8.8
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Google
    Year Founded
    1998
    HQ Location
    Mountain View, CA
    Twitter
    @google
    32,520,271 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    301,875 employees on LinkedIn®
    Ownership
    NASDAQ:GOOG
Users
No information available
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 53% Small-Business
  • 27% Mid-Market
Deep Learning VM Image Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
25
Features
15
Setup Ease
14
Easy Setup
11
Cloud Computing
10
Cons
Expensive
14
Learning Difficulty
7
Cost
6
Difficult Learning
6
Lagging Performance
5
Deep Learning VM Image features and usability ratings that predict user satisfaction
8.8
Application
Average: 8.6
8.5
Managed Service
Average: 8.3
8.4
Natural Language Understanding
Average: 8.4
8.8
Ease of Admin
Average: 8.5
Seller Details
Seller
Google
Year Founded
1998
HQ Location
Mountain View, CA
Twitter
@google
32,520,271 Twitter followers
LinkedIn® Page
www.linkedin.com
301,875 employees on LinkedIn®
Ownership
NASDAQ:GOOG
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    MATLAB is a programming, modeling and simulation tool developed by MathWorks.

    Users
    • Graduate Research Assistant
    • Student
    Industries
    • Higher Education
    • Research
    Market Segment
    • 43% Enterprise
    • 30% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • MATLAB Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    9
    Simulation
    6
    Features
    5
    Problem Solving
    5
    Insights
    4
    Cons
    Expensive
    3
    Cost
    2
    Difficult Learning
    2
    High System Requirements
    2
    Lack of Features
    2
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • MATLAB features and usability ratings that predict user satisfaction
    8.6
    Application
    Average: 8.6
    8.3
    Managed Service
    Average: 8.3
    8.5
    Natural Language Understanding
    Average: 8.4
    8.4
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    MathWorks
    Year Founded
    1984
    HQ Location
    Natick, MA
    Twitter
    @MATLAB
    99,670 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    7,496 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

MATLAB is a programming, modeling and simulation tool developed by MathWorks.

Users
  • Graduate Research Assistant
  • Student
Industries
  • Higher Education
  • Research
Market Segment
  • 43% Enterprise
  • 30% Small-Business
MATLAB Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
9
Simulation
6
Features
5
Problem Solving
5
Insights
4
Cons
Expensive
3
Cost
2
Difficult Learning
2
High System Requirements
2
Lack of Features
2
MATLAB features and usability ratings that predict user satisfaction
8.6
Application
Average: 8.6
8.3
Managed Service
Average: 8.3
8.5
Natural Language Understanding
Average: 8.4
8.4
Ease of Admin
Average: 8.5
Seller Details
Seller
MathWorks
Year Founded
1984
HQ Location
Natick, MA
Twitter
@MATLAB
99,670 Twitter followers
LinkedIn® Page
www.linkedin.com
7,496 employees on LinkedIn®
(194)4.5 out of 5
5th Easiest To Use in Data Science and Machine Learning Platforms software
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Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Hex is a platform for collaborative analytics and data science. It combines code notebooks, data apps, and knowledge management, making it easy to use data and share the results. Hex brings together

    Users
    • Data Scientist
    • Senior Data Analyst
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 61% Mid-Market
    • 28% Small-Business
    User Sentiment
    How are these determined?Information
    These insights, currently in beta, are compiled from user reviews and grouped to display a high-level overview of the software.
    • Hex is a data analysis tool that allows users to write SQL queries, create dashboards, and collaborate on data analytics projects.
    • Users frequently mention the ease of sharing notebooks, the ability to switch between SQL and Python, and the convenience of generating dashboards for broader teams.
    • Users reported limitations in dynamic calculated fields, outdated Python kernel, lack of a plugin ecosystem, and difficulties in handling large datasets.
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Hex Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    125
    SQL Queries
    74
    SQL Querying
    67
    Python Support
    59
    Data Analysis
    56
    Cons
    Missing Features
    40
    Lacking Features
    35
    Limited Visualization
    34
    Poor Visualization
    34
    Software Bugs
    33
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Hex features and usability ratings that predict user satisfaction
    7.4
    Application
    Average: 8.6
    6.9
    Managed Service
    Average: 8.3
    5.3
    Natural Language Understanding
    Average: 8.4
    8.9
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Hex Tech
    Company Website
    Year Founded
    2019
    HQ Location
    San Francisco, US
    Twitter
    @_hex_tech
    5,723 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    158 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Hex is a platform for collaborative analytics and data science. It combines code notebooks, data apps, and knowledge management, making it easy to use data and share the results. Hex brings together

Users
  • Data Scientist
  • Senior Data Analyst
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 61% Mid-Market
  • 28% Small-Business
User Sentiment
How are these determined?Information
These insights, currently in beta, are compiled from user reviews and grouped to display a high-level overview of the software.
  • Hex is a data analysis tool that allows users to write SQL queries, create dashboards, and collaborate on data analytics projects.
  • Users frequently mention the ease of sharing notebooks, the ability to switch between SQL and Python, and the convenience of generating dashboards for broader teams.
  • Users reported limitations in dynamic calculated fields, outdated Python kernel, lack of a plugin ecosystem, and difficulties in handling large datasets.
Hex Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
125
SQL Queries
74
SQL Querying
67
Python Support
59
Data Analysis
56
Cons
Missing Features
40
Lacking Features
35
Limited Visualization
34
Poor Visualization
34
Software Bugs
33
Hex features and usability ratings that predict user satisfaction
7.4
Application
Average: 8.6
6.9
Managed Service
Average: 8.3
5.3
Natural Language Understanding
Average: 8.4
8.9
Ease of Admin
Average: 8.5
Seller Details
Seller
Hex Tech
Company Website
Year Founded
2019
HQ Location
San Francisco, US
Twitter
@_hex_tech
5,723 Twitter followers
LinkedIn® Page
www.linkedin.com
158 employees on LinkedIn®
(87)4.3 out of 5
9th Easiest To Use in Data Science and Machine Learning Platforms software
View top Consulting Services for Azure Machine Learning
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  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.

    Users
    • Software Engineer
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 39% Enterprise
    • 33% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Azure Machine Learning Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    18
    Machine Learning
    10
    Training
    8
    Cloud Computing
    6
    Cloud Services
    6
    Cons
    Expensive
    8
    Learning Curve
    6
    Missing Features
    5
    Cost
    4
    Integration Issues
    4
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Azure Machine Learning features and usability ratings that predict user satisfaction
    8.8
    Application
    Average: 8.6
    8.8
    Managed Service
    Average: 8.3
    8.7
    Natural Language Understanding
    Average: 8.4
    8.3
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Microsoft
    Year Founded
    1975
    HQ Location
    Redmond, Washington
    Twitter
    @microsoft
    14,031,499 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    238,990 employees on LinkedIn®
    Ownership
    MSFT
Product Description
How are these determined?Information
This description is provided by the seller.

Azure Machine Learning Studio is a GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.

Users
  • Software Engineer
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 39% Enterprise
  • 33% Small-Business
Azure Machine Learning Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
18
Machine Learning
10
Training
8
Cloud Computing
6
Cloud Services
6
Cons
Expensive
8
Learning Curve
6
Missing Features
5
Cost
4
Integration Issues
4
Azure Machine Learning features and usability ratings that predict user satisfaction
8.8
Application
Average: 8.6
8.8
Managed Service
Average: 8.3
8.7
Natural Language Understanding
Average: 8.4
8.3
Ease of Admin
Average: 8.5
Seller Details
Seller
Microsoft
Year Founded
1975
HQ Location
Redmond, Washington
Twitter
@microsoft
14,031,499 Twitter followers
LinkedIn® Page
www.linkedin.com
238,990 employees on LinkedIn®
Ownership
MSFT
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Cloudera Data Science provides better access to Apache Hadoop data with familiar and performant tools that address all aspects of modern predictive analytics.

    Users
    No information available
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 36% Enterprise
    • 36% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Cloudera Data Engineering Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    11
    Security
    6
    Automation
    5
    Easy Integrations
    5
    Customer Support
    4
    Cons
    Access Issues
    3
    Data Management Issues
    2
    Expensive
    2
    Poor User Interface
    2
    Cost
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Cloudera Data Engineering features and usability ratings that predict user satisfaction
    9.4
    Application
    Average: 8.6
    9.1
    Managed Service
    Average: 8.3
    9.5
    Natural Language Understanding
    Average: 8.4
    9.4
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Cloudera
    Year Founded
    2008
    HQ Location
    Palo Alto, CA
    Twitter
    @cloudera
    109,180 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    3,226 employees on LinkedIn®
    Phone
    888-789-1488
Product Description
How are these determined?Information
This description is provided by the seller.

Cloudera Data Science provides better access to Apache Hadoop data with familiar and performant tools that address all aspects of modern predictive analytics.

Users
No information available
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 36% Enterprise
  • 36% Mid-Market
Cloudera Data Engineering Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
11
Security
6
Automation
5
Easy Integrations
5
Customer Support
4
Cons
Access Issues
3
Data Management Issues
2
Expensive
2
Poor User Interface
2
Cost
1
Cloudera Data Engineering features and usability ratings that predict user satisfaction
9.4
Application
Average: 8.6
9.1
Managed Service
Average: 8.3
9.5
Natural Language Understanding
Average: 8.4
9.4
Ease of Admin
Average: 8.5
Seller Details
Seller
Cloudera
Year Founded
2008
HQ Location
Palo Alto, CA
Twitter
@cloudera
109,180 Twitter followers
LinkedIn® Page
www.linkedin.com
3,226 employees on LinkedIn®
Phone
888-789-1488
(126)4.6 out of 5
Optimized for quick response
Save to My Lists
Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    With 45 million users, Anaconda is the world’s most popular data science platform and the foundation of modern AI development. We pioneered the use of Python for data science, champion its vibrant com

    Users
    • Software Engineer
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 39% Enterprise
    • 34% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Anaconda Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    4
    Tools Variety
    4
    Cloud Computing
    2
    Coding Ease
    2
    Model Variety
    2
    Cons
    Limited Features
    2
    Slow Startup
    2
    Complex Interface
    1
    Data Management Issues
    1
    Deployment Issues
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Anaconda features and usability ratings that predict user satisfaction
    8.9
    Application
    Average: 8.6
    8.6
    Managed Service
    Average: 8.3
    8.8
    Natural Language Understanding
    Average: 8.4
    9.1
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Company Website
    Year Founded
    2012
    HQ Location
    Austin, Texas
    Twitter
    @anacondainc
    85,001 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    445 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

With 45 million users, Anaconda is the world’s most popular data science platform and the foundation of modern AI development. We pioneered the use of Python for data science, champion its vibrant com

Users
  • Software Engineer
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 39% Enterprise
  • 34% Small-Business
Anaconda Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
4
Tools Variety
4
Cloud Computing
2
Coding Ease
2
Model Variety
2
Cons
Limited Features
2
Slow Startup
2
Complex Interface
1
Data Management Issues
1
Deployment Issues
1
Anaconda features and usability ratings that predict user satisfaction
8.9
Application
Average: 8.6
8.6
Managed Service
Average: 8.3
8.8
Natural Language Understanding
Average: 8.4
9.1
Ease of Admin
Average: 8.5
Seller Details
Company Website
Year Founded
2012
HQ Location
Austin, Texas
Twitter
@anacondainc
85,001 Twitter followers
LinkedIn® Page
www.linkedin.com
445 employees on LinkedIn®
By Qlik
(75)4.4 out of 5
10th Easiest To Use in Data Science and Machine Learning Platforms software
Save to My Lists
Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple

    Users
    • Data Analyst
    Industries
    • Information Technology and Services
    Market Segment
    • 39% Enterprise
    • 31% Small-Business
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Qlik AutoML Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    4
    Machine Learning
    4
    Easy Integrations
    2
    Implementation Ease
    2
    Speed
    2
    Cons
    Expensive
    1
    Expensive Licensing
    1
    Inadequate Tools
    1
    Learning Curve
    1
    Learning Difficulty
    1
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Qlik AutoML features and usability ratings that predict user satisfaction
    8.4
    Application
    Average: 8.6
    8.3
    Managed Service
    Average: 8.3
    7.8
    Natural Language Understanding
    Average: 8.4
    8.6
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Qlik
    Year Founded
    1993
    HQ Location
    Radnor, PA
    Twitter
    @qlik
    65,827 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    3,938 employees on LinkedIn®
    Phone
    1 (888) 994-9854
Product Description
How are these determined?Information
This description is provided by the seller.

Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple

Users
  • Data Analyst
Industries
  • Information Technology and Services
Market Segment
  • 39% Enterprise
  • 31% Small-Business
Qlik AutoML Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
4
Machine Learning
4
Easy Integrations
2
Implementation Ease
2
Speed
2
Cons
Expensive
1
Expensive Licensing
1
Inadequate Tools
1
Learning Curve
1
Learning Difficulty
1
Qlik AutoML features and usability ratings that predict user satisfaction
8.4
Application
Average: 8.6
8.3
Managed Service
Average: 8.3
7.8
Natural Language Understanding
Average: 8.4
8.6
Ease of Admin
Average: 8.5
Seller Details
Seller
Qlik
Year Founded
1993
HQ Location
Radnor, PA
Twitter
@qlik
65,827 Twitter followers
LinkedIn® Page
www.linkedin.com
3,938 employees on LinkedIn®
Phone
1 (888) 994-9854
(39)4.2 out of 5
12th Easiest To Use in Data Science and Machine Learning Platforms software
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes al

    Users
    No information available
    Industries
    • Computer Software
    • Information Technology and Services
    Market Segment
    • 36% Small-Business
    • 33% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Amazon SageMaker Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    5
    Easy Integrations
    4
    Features
    4
    Machine Learning
    4
    AI Capabilities
    3
    Cons
    Complexity Issues
    3
    Difficult Setup
    2
    Expensive
    2
    Limited Features
    2
    Limited Storage
    2
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Amazon SageMaker features and usability ratings that predict user satisfaction
    8.8
    Application
    Average: 8.6
    9.5
    Managed Service
    Average: 8.3
    9.2
    Natural Language Understanding
    Average: 8.4
    8.4
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Year Founded
    2006
    HQ Location
    Seattle, WA
    Twitter
    @awscloud
    2,230,610 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    136,383 employees on LinkedIn®
    Ownership
    NASDAQ: AMZN
Product Description
How are these determined?Information
This description is provided by the seller.

Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes al

Users
No information available
Industries
  • Computer Software
  • Information Technology and Services
Market Segment
  • 36% Small-Business
  • 33% Mid-Market
Amazon SageMaker Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
5
Easy Integrations
4
Features
4
Machine Learning
4
AI Capabilities
3
Cons
Complexity Issues
3
Difficult Setup
2
Expensive
2
Limited Features
2
Limited Storage
2
Amazon SageMaker features and usability ratings that predict user satisfaction
8.8
Application
Average: 8.6
9.5
Managed Service
Average: 8.3
9.2
Natural Language Understanding
Average: 8.4
8.4
Ease of Admin
Average: 8.5
Seller Details
Year Founded
2006
HQ Location
Seattle, WA
Twitter
@awscloud
2,230,610 Twitter followers
LinkedIn® Page
www.linkedin.com
136,383 employees on LinkedIn®
Ownership
NASDAQ: AMZN
By IBM
(71)4.5 out of 5
Optimized for quick response
14th Easiest To Use in Data Science and Machine Learning Platforms software
Save to My Lists
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI

    Users
    • Consultant
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 35% Small-Business
    • 35% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • IBM watsonx.ai Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    48
    Intuitive
    16
    Model Variety
    16
    Features
    13
    Efficiency
    12
    Cons
    Improvement Needed
    16
    Difficult Learning
    9
    Expensive
    9
    Poor User Interface
    9
    Performance Issues
    8
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • IBM watsonx.ai features and usability ratings that predict user satisfaction
    8.8
    Application
    Average: 8.6
    8.4
    Managed Service
    Average: 8.3
    8.4
    Natural Language Understanding
    Average: 8.4
    8.6
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    IBM
    Company Website
    Year Founded
    1911
    HQ Location
    Armonk, NY
    Twitter
    @IBM
    711,154 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    317,108 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI

Users
  • Consultant
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 35% Small-Business
  • 35% Mid-Market
IBM watsonx.ai Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
48
Intuitive
16
Model Variety
16
Features
13
Efficiency
12
Cons
Improvement Needed
16
Difficult Learning
9
Expensive
9
Poor User Interface
9
Performance Issues
8
IBM watsonx.ai features and usability ratings that predict user satisfaction
8.8
Application
Average: 8.6
8.4
Managed Service
Average: 8.3
8.4
Natural Language Understanding
Average: 8.4
8.6
Ease of Admin
Average: 8.5
Seller Details
Seller
IBM
Company Website
Year Founded
1911
HQ Location
Armonk, NY
Twitter
@IBM
711,154 Twitter followers
LinkedIn® Page
www.linkedin.com
317,108 employees on LinkedIn®
(48)4.3 out of 5
View top Consulting Services for Dataiku
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Entry Level Price:Free
  • Overview
    Expand/Collapse Overview
  • Product Description
    How are these determined?Information
    This description is provided by the seller.

    Dataiku is the platform for Everyday AI, enabling data experts and domain experts to work together to build data into their daily operations, from advanced analytics to Generative AI. Together, they d

    Users
    No information available
    Industries
    • Information Technology and Services
    • Computer Software
    Market Segment
    • 42% Enterprise
    • 27% Mid-Market
  • Pros and Cons
    Expand/Collapse Pros and Cons
  • Dataiku Pros and Cons
    How are these determined?Information
    Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
    Pros
    Ease of Use
    12
    Efficiency
    8
    Features
    8
    Data Analytics
    6
    Automation
    5
    Cons
    Learning Curve
    5
    Performance Issues
    4
    Complexity
    3
    Complexity Issues
    3
    Cost Issues
    3
  • User Satisfaction
    Expand/Collapse User Satisfaction
  • Dataiku features and usability ratings that predict user satisfaction
    8.5
    Application
    Average: 8.6
    8.6
    Managed Service
    Average: 8.3
    8.5
    Natural Language Understanding
    Average: 8.4
    7.5
    Ease of Admin
    Average: 8.5
  • Seller Details
    Expand/Collapse Seller Details
  • Seller Details
    Seller
    Dataiku
    Company Website
    Year Founded
    2013
    HQ Location
    New York, NY
    Twitter
    @dataiku
    23,087 Twitter followers
    LinkedIn® Page
    www.linkedin.com
    1,415 employees on LinkedIn®
Product Description
How are these determined?Information
This description is provided by the seller.

Dataiku is the platform for Everyday AI, enabling data experts and domain experts to work together to build data into their daily operations, from advanced analytics to Generative AI. Together, they d

Users
No information available
Industries
  • Information Technology and Services
  • Computer Software
Market Segment
  • 42% Enterprise
  • 27% Mid-Market
Dataiku Pros and Cons
How are these determined?Information
Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
Pros
Ease of Use
12
Efficiency
8
Features
8
Data Analytics
6
Automation
5
Cons
Learning Curve
5
Performance Issues
4
Complexity
3
Complexity Issues
3
Cost Issues
3
Dataiku features and usability ratings that predict user satisfaction
8.5
Application
Average: 8.6
8.6
Managed Service
Average: 8.3
8.5
Natural Language Understanding
Average: 8.4
7.5
Ease of Admin
Average: 8.5
Seller Details
Seller
Dataiku
Company Website
Year Founded
2013
HQ Location
New York, NY
Twitter
@dataiku
23,087 Twitter followers
LinkedIn® Page
www.linkedin.com
1,415 employees on LinkedIn®

Learn More About Data Science and Machine Learning Platforms

What are data science and machine learning (DSML) platforms?

The amount of data being produced within companies is increasing rapidly. Businesses are realizing its importance and are leveraging this accumulated data to gain a competitive advantage. Companies are turning their data into insights to drive business decisions and improve product offerings. With data science, of which artificial intelligence (AI) is a part, users can mine vast amounts of data. Whether structured or unstructured, it uncovers patterns and makes data-driven predictions.

One crucial aspect of data science is the development of machine learning models. Users leverage data science and machine learning engineering platforms that facilitate the entire process, from data integration to model management. With this single platform, data scientists, engineers, developers, and other business stakeholders collaborate to ensure that the data is appropriately managed and mined for meaning.

Types of DSML platforms

Not all data science and machine learning software platforms are designed equal. These tools allow developers and data scientists to build, train, and deploy machine learning models. However, they differ in terms of the data types supported and the method and manner of deployment. 

Cloud data science and machine learning platforms

With the ability to store data in remote servers and easily access it, businesses can focus less on building infrastructure and more on their data, both in terms of how to derive insight from it and to ensure its quality. Cloud-based DSML platforms afford them the ability to both train and deploy the models in the cloud. This also helps when these models are being built into various applications, as it provides easier access to change and tweak the models that have been deployed.

On-premises data science and machine learning platforms

Cloud is not always the answer, as it is not always a viable solution. Not all data experts have the luxury of working in the cloud for several reasons, including data security and issues related to latency. In cases like health care, strict regulations, such as HIPAA, require data to be secure. Therefore, on-premises DSML solutions can be vital for some professionals, such as those in the healthcare industry and government sector, where privacy compliance is stringent and sometimes necessary.

Edge platforms

Some DSML tools and software allow for spinning up algorithms on the edge, consisting of a mesh network of data centers that process and store data locally before being sent to a centralized storage center or cloud. Edge computing optimizes cloud computing systems to avoid disruptions or slowing in the sending and receiving of data. 

What are the common features of data science and machine learning solutions?

The following are some core features within data science and machine learning platforms that can help users prepare data and train, manage, and deploy models.

Data preparation: Data ingestion features allow users to integrate and ingest data from various internal or external sources, such as enterprise applications, databases, or Internet of Things (IoT) devices.

Dirty data (i.e., incomplete, inaccurate, or incoherent data) is a nonstarter for building machine learning models. Bad AI training begets bad models, which in turn begets bad predictions that may be useful at best and detrimental at worst. Therefore, data preparation capabilities allow for data cleansing and data augmentation (in which related datasets are brought to bear on company data) to ensure that the data journey gets off to a good start.

Model training: Feature engineering transforms raw data into features that better represent the underlying problem to the predictive models. It is a key step in building a model and improves model accuracy on unseen data.

Building a model requires training it by feeding it data. Training a model is the process of determining the proper values for all the weights and the bias from the inputted data. Two key methods used for this purpose are supervised learning and unsupervised learning. The former is a method in which the input is labeled, whereas the latter deals with unlabeled data.

Model management: The process does not end once the model is released. Businesses must monitor and manage their models to ensure that they remain accurate and updated. Model comparison allows users to quickly compare models to a baseline or to a previous result to determine the quality of the model built. Many of these platforms also have tools for tracking metrics, such as accuracy and loss.

Model deployment: The deployment of machine learning models is the process of making them available in production environments, where they provide predictions to other software systems. Methods of deployment include REST APIs, GUI for on-demand analysis, and more.

What are the benefits of using DSML engineering platforms?

Through the use of data science and machine learning platforms, data scientists can gain visibility into the entire data journey, from ingestion to inference. This helps them better understand what is and isn’t working and provides them with the tools necessary to fix problems if and when they arise. With these tools, experts prepare and enrich their data, leverage machine learning libraries, and deploy their algorithms into production.

Share data insights: Users can share data, models, dashboards, or other related information with collaboration-based tools to foster and facilitate teamwork.

Simplify and scale data science: Many platforms are opening up these tools to a broader audience with easy-to-use features and drag-and-drop capabilities. In addition, pre-trained models and out-of-the-box pipelines tailored to specific tasks help streamline the process. These platforms easily help scale up experiments across many nodes to perform distributed training on large datasets.

Experimentation: Before a model is pushed to production, data scientists spend a significant amount of time working with the data and experimenting to find an optimal solution. Data science and machine learning vendors facilitate this experimentation through data visualization, data augmentation, and data preparation tools. Different types of layers and optimizers for deep learning, which are algorithms or methods used to change the attributes of neural networks, such as weights and learning rate, to reduce losses, are also used in experimentation.

Who uses data science and machine learning products?

Data scientists are in high demand, but skilled professionals are in shortage. The skillset is varied and vast (for example, there is a need to understand various algorithms, advanced mathematics, programming skills, and more). Therefore, such professionals are difficult to come by and command high compensation. To tackle this issue, platforms increasingly include features that make it easier to develop AI solutions, such as drag-and-drop capabilities and prebuilt algorithms.

In addition, for data science projects to initiate, it is key that the broader business buys into them. The more robust platforms provide resources that help nontechnical users understand the models, the data involved, and the aspects of the business that have been impacted.

Data engineers: With robust data integration capabilities, data engineers tasked with the design, integration, and management of data use these platforms to collaborate with data scientists and other stakeholders within the organization.

Citizen data scientists: With the rise of more user-friendly features, citizen data scientists, who are not professionally trained but have developed data skills, are increasingly turning to data science and machine learning platforms to bring AI into their organizations.

Professional data scientists: Expert data scientists use these solutions to scale data science operations across the lifecycle, simplifying the process of experimentation to deployment and speeding up data exploration and preparation, as well as model development and training.

Business stakeholders: Business stakeholders use these tools to gain clarity into the machine learning models and better understand how they tie in with the broader business and its operations.

What are the alternatives to data science and machine learning platforms?

Alternatives to data science and machine learning solutions can replace this type of software, either partially or completely:

AI & machine learning operationalization software: Depending on the use case, businesses might consider AI and machine learning operationalization software. This software does not provide a platform for the full end-to-end development of machine learning models but can provide more robust features around operationalizing these algorithms. This includes monitoring the health, performance, and accuracy of models.

Machine learning software: Data science and machine learning platforms are great for the full-scale development of models, whether that be for computer vision, natural language processing (NLP), and more. However, in some cases, businesses may want a solution that is more readily available off the shelf, which they can use in a plug-and-play fashion. In such a case, they can consider machine learning software, which will involve less setup time and development costs.

There are many different types of machine learning algorithms that perform a variety of tasks and functions. These algorithms may consist of more specific ones, such as association rule learning, Bayesian networks, clustering, decision tree learning, genetic algorithms, learning classifier systems, and support vector machines, among others. This helps organizations look for point solutions.

Challenges with DSML platforms

Software solutions can come with their own set of challenges. 

Data requirements: A great deal of data is required for most AI algorithms to learn what is needed. Users need to train machine learning algorithms using techniques such as reinforcement learning, supervised learning, and unsupervised learning to build a truly intelligent application.

Skill shortage: There is also a shortage of people who understand how to build these algorithms and train them to perform the necessary actions. The common user cannot simply fire up AI software and have it solve all their problems.

Algorithmic bias: Although the technology is efficient, it is not always effective and is marred by various types of biases in the training data, such as race or gender biases. For example, since many facial recognition algorithms are trained on datasets with primarily white male faces, others are more likely to be falsely identified by the systems.

Which companies should buy DSML engineering platforms?

The implementation of AI can have a positive impact on businesses across a host of different industries. Here are a handful of examples:

Financial services: AI is widely used in financial services, with banks using it for everything from developing credit score algorithms to analyzing earnings documents to spot trends. With data science and machine learning software solutions, data science teams can build models with company data and deploy them to internal and external applications.

Healthcare: Within healthcare, businesses can use these platforms to better understand patient populations, such as predicting in-patient visits and developing systems that can match people with relevant clinical trials. In addition, as the process of drug discovery is particularly costly and takes a significant amount of time, healthcare organizations are using data science to speed up the process, using data from past trials, research papers, and more.

Retail: In retail, especially e-commerce, personalization rules supreme. The top retailers are leveraging these platforms to provide customers with highly personalized experiences based on factors such as previous behavior and location. With machine learning in place, these businesses can display highly relevant material and catch the attention of potential customers. 

How to choose the best data science and machine learning (DSML) platform

Requirements gathering (RFI/RFP) for DSML platforms

If a company is just starting out and looking to purchase its first data science and machine learning platform, or wherever a business is in its buying process, g2.com can help select the best option.

The first step in the buying process must involve a careful look at one’s company data. As a fundamental part of the data science journey involves data engineering (i.e., data collection and analysis), businesses must ensure that their data quality is high and the platform in question can adequately handle their data, both in terms of format as well as volume. If the company has amassed a lot of data, it needs to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use this software, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the deployment scope, producing an RFI, a one-page list with a few bullet points describing what is needed from a data science platform might be helpful.

Compare DSML products

Create a long list

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

Create a short list

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.

Conduct demos

To ensure a thorough comparison, the user should demo each solution on the short list using the same use case and datasets. This will allow the business to evaluate like-for-like and see how each vendor compares against the competition.

Selection of DSML platforms

Choose a selection team

Before getting started, it's crucial to create a winning team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interests, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants, multitasking, and taking on more responsibilities.

Negotiation

Just because something is written on a company’s pricing page does not mean it is fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or to recommend the product to others.

Final decision

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

Cost of data science and machine learning platforms

As mentioned above, data science and machine learning platforms are available as both on-premises and cloud solutions. Pricing between the two might differ, with the former often requiring more upfront infrastructure costs. 

As with any software, these platforms are frequently available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will frequently not have as many features and may have usage caps. DSML vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, which might be unlimited or capped at a certain number of hours per billing cycle.

Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.

Return on Investment (ROI)

Businesses decide to deploy data science and machine learning platforms with the goal of deriving some degree of ROI. As they are looking to recoup the losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, these platforms typically are billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.

Implementation of data science and machine learning platforms

How are DSML software tools implemented?

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience under their belts, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

Who is responsible for DSML platform implementation?

It may require many people or teams to properly deploy a data science platform, including data engineers, data scientists, and software engineers. This is because, as mentioned, data can cut across teams and functions. As a result, one person or even one team rarely has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can begin to piece together its data and begin the journey of data science, starting with proper data preparation and management.

What is the implementation process for data science and machine learning products?

In terms of implementation, it is typical for the platform to be deployed in a limited fashion and subsequently rolled out in a broader fashion. For example, a retail brand might decide to A/B test its use of a personalization algorithm for a limited number of visitors to its site to understand better how it is performing. If the deployment is successful, the data science team can present their findings to their leadership team (which might be the CTO, depending on the structure of the business).

If the deployment is unsuccessful, the team can return to the drawing board to determine what went wrong. This will involve examining the training data and algorithms used. If they try again, yet nothing seems to be successful (i.e., the outcome is faulty or there is no improvement in predictions), the business might need to go back to basics and review their data.

When should you implement DSML tools?

As previously mentioned, data engineering, which involves preparing and gathering data, is a fundamental feature of data science projects. Therefore, businesses must make getting their data in order their top priority, ensuring that there are no duplicate records or misaligned fields. Although this sounds basic, it is anything but. Faulty data as an input will result in faulty data as an output.