Amazon’s SageMaker is a cloud based machine-learning platform that enables developers to create, train, and deploy machine-learning (ML) models on the cloud.

Deepnote is a collaboration first data platform. It provides a powerful, cloud-based workspace that allows users to easily explore, collaborate on, and share data, create interactive charts and dashboards, and build and deploy machine learning models.

Let’s break them down.

AI
While SageMaker, and its purpose is to build and manage AI, little AI is actually used in the platform.  While Amazon offers a very strong code completion in their collab notebooks via their own CodeWhisperer, they are missing an AI Agent that is able to interact with the Notebooks.  In addition to offering code completion powered by Codeium, Deepnote’s Autonomous AI is able to read Notebook context, datasources, generate blocks, and execute them.  

Jupyter Compatibility
Both Deepnote and SageMaker platforms feature a Jupyter compatible Notebook experience.  You will not have to learn any new proprietary Notebook.  You will encounter the same Jupyter you know and love.  You can upload your Notebooks as an IPYNB and instantly begin working on your Notebook. 

Connecting to your data
Connecting to your various data sources, an essential part of data science, is handled eloquently by both platforms. As part of the AWS stack, SageMaker makes use of numerous other Amazon products and allows you to connect to the usual array of warehouses and databases. 

Deepnote also features a dizzying array of  built-in data connectors for major cloud platforms, such as BigQuery, Snowflake, Redshift, Athena, and Clickhouse.

Interacting and displaying your data
SQL is an important chunk of any notebook, and both Deepnote and SageMaker have you covered with Notebook SQL blocks.  However, Deepnote’s SQL and Python blocks come equipped with code completion and syntax highlighting leading to a more IDE-like experience.  Deepnote comes with native charting, for building out dashboards, and easily displaying your data.

Both platforms make it easy to schedule your notebook.  Both with fine grade permissioning that allows organizations to share reports publicly and within the organization.

It is worth noting that Deepnote comes with a fully fledged dashboarding solution. Allowing you to easily generate reports, and interactive dashboards.

Collaboration
It comes as no surprise that since Deepnote bills itself as collaboration first, Deepnote has a huge advantage in delivering collaborative features to help teams succeed.  Deepnote is leading the pack in collaborative notebooks, with multi-user editing, commenting, robust permissioning.  Collaboration is not the focus of SageMaker, so it is lacking in teamwork oriented features.

Pricing
Deepnote comes equipped with a free tier, and a free two week trial on signup, with no credit card needed.  Deepnote offers the standard seat based pricing, with additional computer addons.  As for SageMaker’s pricing, it is similar to other AWS products, in that it is a completely usage based solution, pricing can be found here.

Conclusion
Although this comparison may seem apples to oranges, both Deepnote and SageMaker allow you to manage your data, develop your models, deploy your models, and review your models, all in one place.  They both utilize Notebook solutions to use as the standard for data science.  As SageMaker maintains a strong connection with other Amazon services, it’s easy to recommend SageMaker if you are already familiar with and in the Amazon ecosystem. 

If you prefer an AI-powered collaboration based notebook, then Deepnote is your best bet.