Vertex AI Notebooks Reviews & Product Details

Internal Database/SharePoint Connection: This refers to connecting a database or SharePoint system within an organization’s internal network. It involves setting up secure connections, often using APIs or custom interfaces, to query, retrieve, and update data stored in these systems.

Connection to Various GCP Services: Google Cloud Platform (GCP) provides services like BigQuery, Cloud Storage, and Pub/Sub. Establishing connections to these services involves using GCP SDKs, authentication through service accounts, and making API calls to manage and process data.

Scheduling Job via Crontab: Crontab is a Unix-based job scheduler that allows scheduling automated tasks, such as running scripts, by specifying the time and frequency (e.g., daily, weekly). These jobs can run tasks like database backups, file transfers, or other scheduled processes.

Connecting JupyterLab via VS Code: JupyterLab is often used for data science. In VS Code, you can connect to JupyterLab by using the Python extension that supports Jupyter notebooks. This allows coding, testing, and visualizing notebook content directly within VS Code, combining the benefits of an IDE and Jupyter notebooks for a seamless experience. Review collected by and hosted on G2.com.

In Vertex AI notebooks, the connection can get disconnected after being left idle for some time due to session timeouts or resource management on Google Cloud. This is common with cloud-based environments, where inactive sessions are often terminated to free up resources. When you attempt to reconnect, the system sometimes prompts you to clear the workspace to free up resources or reset the environment for a fresh session. This process ensures that the notebook can re-establish a connection and operate smoothly by eliminating potential conflicts or memory overloads from previous states. Review collected by and hosted on G2.com.

1. Collaborate with different teams in real-time.

2. Automatic resource scaling.

3. Integration with Google Cloud services like BigQuery, Cloud Storage, and Pub/Sub.

4. Lots of APIs, Managed services Review collected by and hosted on G2.com.

Limited customization in GCP.

Dependency on the Google Cloud services.

Documentation is insufficient, Need to work on that. Review collected by and hosted on G2.com.

Easy access to BigQuery Studio, Cloud buckets, etc. Vertex AI provides autoML facility in which pre-built AI/ML algorithms are there we can directly use them. It offers us various notebooks options as per our business requirements such as Python, Pyspark, Pytorch, R, Tensor Flow & XGBoost, etc. Review collected by and hosted on G2.com.

It's Computing/Processing power is very less as compared to Big Query Studio. Review collected by and hosted on G2.com.

I am working on Mahindra as cloud engineer , all use case that we have on our company I will work on , like deploy application on Compute instance , GKE and pass servcie cloud run also . and data analytic part : we used cloud composer , big query , data proc cluster .. as storage we use cloud spanner , gcs , sql .. at ui and servcie side google cloud is ok , Review collected by and hosted on G2.com.

google cloud ui for IAM releted not good , speed also not good network side . google team need to work on documention also . google team aslo team to wrok on avilablity of instance on all region . Review collected by and hosted on G2.com.

Large range of tools available for variety of tasks Review collected by and hosted on G2.com.

Pricing information can be more detailed Review collected by and hosted on G2.com.

Some of the advantages I see are Scalable, easy plug and play notebooks with no server overhead and its seamless GCP integration and colab based UI. Review collected by and hosted on G2.com.

high costs with creating notebooks and datasets disk usage charges. Review collected by and hosted on G2.com.

According to me, and my experience with usage, i think what stands out the most is its integration with google cloud exosystem. How it makes it so easy to access big Query data or cloud storage data directly from it. Review collected by and hosted on G2.com.

Nothing to dislike, but one thing where i think is a scope of improvement is sometimes large hardware configurations disrupt the workflows. Also if not monitored correctly it can be a huge budget. Review collected by and hosted on G2.com.

Vertex AI Notebooks is very powerful and developed by Google Engineer called Vertex AI. It intergrate with google cloud and managing jupyter notebook. It is scalable in nature you can up and down the resources as per the requirement. There is a high frequency of use in the Information technology market and ease of use is user friendly. It's implementation enhance the user to share notebook across the teams and projects. It has 24 x 7 customer support which makes the user experience much better. Review collected by and hosted on G2.com.

It is very useful for the organisation so I don't think that there is any cons for using the vertex AI notebooks Review collected by and hosted on G2.com.

I really live excited about the impact of Vertex AI Notebooks as they are superbly potent and flexible. By this measure, I am able to exploit Google's cloud based infrastructure for complex data analytics and applications that are machine trained. Thanks to my possibility to scale up or down my projects over my needs, and also integrating other Google Cloud services with ease. Review collected by and hosted on G2.com.

The disadvantage of Vertex AI Notebooks is the fact that it doesn't have a disconnected way of accessing data. It turns out to be a big deal if the project comes up during travel when I may not have a reliable Internet connection. It becomes the barrier that impedes tool’s productivity in circumstances where real-time availability is critical. Review collected by and hosted on G2.com.

Integrated workflow with Google cloud services specially all Vertex AI services. This platform provides a variety of advanced capabilities, including version control, scheduling, and pre-configured environments for popular languages and frameworks. Vertex AI Workbench enables team collaboration within Vertex AI notebooks, which improves communication and information sharing across all data scientists and ML developers. I think using Imagen 2 is the best thing I ever observed specially in terms of image editing using mask and prompt. Review collected by and hosted on G2.com.

It is difficult to integrate and use specially by the new users. Apart of this I would be platform dependent only. And costlier than few other solutions. Review collected by and hosted on G2.com.