Azure Machine Learning Pros and Cons

Azure Machine Learning uses a layered form to building your custom learning application. If you keep the structure very simplified you can build your data sets into separate groups and reference them only when you need them and assign access to individuals, flagging data for further human review. Review collected by and hosted on G2.com.

Customization limitations, for example, if Azure Machine Learning was like building a house, your pre-built walls, doors, and windows, help you get "started" quickly and easily, even if you're not a professional builder. But if you have a very specific design in mind, or if you need to accommodate unusual features, you might need to custom-build some components yourself. Since this is such a specialized software within Azure, it's unlikely you'll find anyone quickly to help with this or be able to outsource it and explain the details to them to actually help you. So this could mean long hours just doing trial and error until you get the desired results. Review collected by and hosted on G2.com.

Azure machine learning is a powerful cloud service which manages the machine learning project lifecycle.

It allows us to collaborate with team via notebooks, serverless computing, data and more.

We can deploy Machine Learning models easily with scalability and can govern them with MLOpe. Review collected by and hosted on G2.com.

Thee free version have limited storage which restrics us for larger projects

Lot of issues while integrating with Tenserflow.

The cost should have been less as there are some cons for Azure machine learning. Review collected by and hosted on G2.com.

One of the standout features of Azure Machine Learning is its scalability and integration with other Azure services. It allows seamless deployment and management of machine learning models, making it easier to leverage the power of AI in various applications. Review collected by and hosted on G2.com.

One potential downside is the learning curve for users who are new to Azure or machine learning in general. It can take some time to become familiar with the platform’s tools and processes. Review collected by and hosted on G2.com.

Azure machine learning offers several compelling features but if I had to choose one it would be it's seamless integration with other azure services. It provides a comprehensive ecosystem for cloud computing and azure machine learning leverages this ecosystem to enable smooth data preparation, model training, deployment and monitoring. Review collected by and hosted on G2.com.

It is a powerful platform with many benefits but there are areas where it could be more user friendly, cost effective and perfomant. Review collected by and hosted on G2.com.

This product help me to traing models for my cybersecurity projects, making my job better and easier. Now its part of my daily products, cause its easy to train, implement and integrate with my tools and projects. Review collected by and hosted on G2.com.

The cost can be a limit but the you realise that totally worths Review collected by and hosted on G2.com.

There is many things which I like about Azure Machine Learning but one of the best thing it's scalability according to the requirements which makes it cost efficient. Review collected by and hosted on G2.com.

There is no such thing to dislike but if I have to choose one than it takes time to master it. Review collected by and hosted on G2.com.

The think I like best about Azure Machine is the ease to use for normal users and the user experience Review collected by and hosted on G2.com.

The I dislike is the limit or service if its more then It would be great Review collected by and hosted on G2.com.

Most of the services are predefined to meet the business needs.Easy to create the experiment, understanding of the algorithm is easy and we can able to deploy the model as a web service. Review collected by and hosted on G2.com.

Nothing specific to explain, However cost wise Azure can rethink to provide competitive pricing Review collected by and hosted on G2.com.

Its easy to use and get started. We can deploy the models as a web service very efficiently using Azure ML Review collected by and hosted on G2.com.

It's a bit tough to integrate the data while creating new models Review collected by and hosted on G2.com.

Azure Machine Learning Studio is an efficient environment to launch and monitor machine learning jobs and experiments. It is easy to use and implement jobs with integrated VS Code, Jupyter Lab, and Terminal.

The UI is intuitive with a number of features like Job Overview, Metrics, Docker Images, Log view, Explanations, Model Monitoring and Code Files containing the model settings.

As my frequency of use is daily I leverage the code and log features to assess the model settings and warning/info logs in runtime.

The customer support for Azure Stack is awesome with proper documentations and community support. Review collected by and hosted on G2.com.

The lack of metric support and cascading of jobs is missing, which I would like in Azure ML. Also a few flows are non-intuitive including the back from compute to Jobs. Review collected by and hosted on G2.com.