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

Virtual connections between a distributed network of teams.

CIOs offer strategies for engaging an entirely mobile workforce

Working from home has been a success operationally, but it has strained people, processes and technology. CIOs discuss the opportunity to make each better in the days to come.

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How data analysis, AI, and IoT will shape the post-pandemic ‘new normal’

Only a common public health infrastructure of AI, cloud computing, streaming, and the Internet of Things can marshal our data against pandemics

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5 lessons companies should learn about working at home

Companies now have the opportunity to learn from what is and isn’t working during the coronavirus crisis. Use this time to build out a strategy so you won’t have to use band aids and duct tape next time.

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