Review: Microsoft Azure AI and Machine Learning aims for the enterprise

Microsoft Azure combines a wide range of cognitive services and a solid platform for machine learning that supports automated ML, no-code/low-code ML, and Python-based notebooks.

Review: Microsoft Azure AI and Machine Learning aims for the enterprise
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At a Glance

Microsoft has a presence in most enterprise development and IT shops, so it’s not a surprise that the Azure AI and Machine Learning platform has a presence in most enterprise development, data analysis, and data science shops. Enterprise AI often has demanding requirements, and the Azure offerings do their best to meet them.

Azure AI and Machine Learning includes 17 cognitive services, a machine learning platform pitched at three different skill levels, cognitive search, bot services, and Azure Databricks, an Apache Spark product optimized for the Azure platform and integrated with other Azure services.

Rather than force companies to run all Azure services on the Azure platform, Microsoft also offers several Docker containers that allow companies to use AI on premises. These support a subset of Azure Cognitive Services, and allow companies to run the cognitive services within their firewall and near on-prem data, which is a sine qua non for companies with tight data security policies and companies subject to restrictive data privacy regulations.

Responsible AI was in the news recently, although not in a good way, when Google fired Timnit Gebru. Earlier in 2020, the Responsible AI news was more positive, as various companies introduced tools to promote more responsible machine learning. Microsoft, for example, added interpretability features to its Azure Machine Learning product, and also released three Responsible AI projects as open source: FairLearn, InterpretML, and SmartNoise.

Fairlearn contains mitigation algorithms as well as a Jupyter widget for model assessment, and has been integrated into a Fairness panel in Azure Machine Learning. InterpretML helps you understand your model’s global behavior, or understand the reasons behind individual predictions, and has been integrated into an Explanation dashboard in Azure Machine Learning. The SmartNoise project, in collaboration with OpenDP, aims to make differential privacy broadly accessible to future deployments by providing several basic building blocks that can be used by people involved with sensitive data. You can bring SmartNoise into a Python notebook by installing and importing the project, and adding a few calls to fuzz your sensitive data.

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