Microsoft named a leader in Forrester’s Notebook-based Predictive Analytics & Machine Learning Wave
Published Sep 29 2020 02:24 PM 25.2K Views
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Forrester Research recently released their Wave report for Notebook-based Predictive Analytics and Machine Learning. Microsoft Azure is named a leader in this Wave, receiving the highest score possible in the ability to execute criteria and rated highest in the strategy category. You can download a complimentary copy of The Forrester Wave™ for Notebook-based Predictive Analytics and Machine Learning Solutions, Q3 2020 .... In this post we’ll look at why Forrester rated Microsoft Azure as a leader.


Forrester evaluated Azure Machine Learning, recognizing its ‘full suite of enterprise PAML capabilities, from centralized model registries to hyperparameter tuning and modular model training and deployment pipelines.’ Forrester gave Azure Machine Learning the highest possible score in 13 evaluation metrics, the most in the report.


The major cloud vendors have long had a gap in offering a comprehensive PAML platform that meets the full set of enterprise data science team needs, to the detriment of bewildered customers who have had to build or find their own solutions. Microsoft has filled that gap and then some.


-  The Forrester Wave™:  Notebook-based Predictive Analytics and Machine Learning, Q3 2020


Let’s take a closer look at the capabilities that Azure Machine Learning features:


  1. Collaboration and productivity – Azure Machine Learning offers customers a collaborative workspace with a dedicated notebook-based machine learning experience along with integrated compute (CPU and GPU) environments with support for all open source tools, frameworks and libraries. The Azure Machine Learning SDK, offered for both Python and R, also makes it simple to for people using tools like Jupyter, VS Code or any other notebook environment/IDE, to collaborate and reap the benefits of Azure Machine Learning. This makes data scientists productive from day 1.


  1. Comprehensive coverage of the ML lifecycle – Azure Machine Learning helps with every step of the ML lifecycle. From data preparation and modelling, through to deployment and monitoring, every aspect of machine learning is carefully optimized with features like data labelling, hyperdrive, pipelines, drift monitoring and responsible ML toolkits. Azure Machine Learning also brings optimizers for popular frameworks and libraries to ensure that the training process runs optimally.


  1. Enterprise readiness – Azure Machine Learning is a service that enables operationalization of ML models irrespective of how stringent the criteria is. Offering a best-in-class MLOps experience, Azure Machine Learning is equipped to help implement a robust deployment pipeline with automated monitoring and retraining capabilities. Azure Machine Learning also offers the security and governance features like private linkcustomer managed keys, VNet, and role-based access control (RBAC).  


  1. Ecosystem – Azure Machine Learning is part of growing ecosystem of services with Azure’s Data & AI offerings. It integrates natively with services like Azure Synapse Analytics, Azure Databricks and Power BI, to offer customers the flexibility to leverage the engine of their choice, like Apache Spark™ and/or SQL, for data wrangling and model scoring. It also brings a strong partner ecosystem and a dedicated certification accompanied by self-paced learning courses on Microsoft Learn and Udacity.


We feel Azure Machine Learning is the best environment for any organization that is building an ML practice with a code-first approach. Be it with notebooks or IDEs, the Azure Machine Learning Studio and the accompanying SDK, makes the Azure Machine Learning capabilities omnipresent across developers and data scientists tools and offers the best way to do secure, managed and scalable data science.


Microsoft provides coding data scientists with all the bells and whistles.


- The Forrester Wave™:  Notebook-based Predictive Analytics and Machine Learning, Q3 2020


Although we doubled down on a lot of the notebook-based capabilities, Azure Machine Learning offers even more to the developer data scientist and/or citizen data scientist community. Capabilities like automated ML and designer, which we’ve recently made generally available, offer an experience where users can build machine learning models without knowing the intricacies of how frameworks and algorithms work. They can let the platform do the heavy lifting for them.


Microsoft’s mission is to empower every person and every organization on the planet to achieve more. With Azure Machine Learning we’re trying to accomplish this for the data science community.



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