Analyses

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MLops: The rise of machine learning operations

Once machine learning models make it to production, they still need updates and monitoring for drift. A team to manage ML operations makes good business sense

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Beyond NoSQL: The case for distributed SQL

What if the main problem with relational databases was the back end and not the front end?

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When not to use AIops for cloudops

The excitement around AIops means that it’s sometimes being deployed for the wrong reasons. Here are a few situations where AIops is contraindicated

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3 ways to apply agile to data science and dataops

Take an agile approach to dashboards, machine learning models, cleansing data sources, and data governance

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Working with the Azure Kinect Developer Kit

Building applications on top of the Kinect depth sensor

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