Machine Learning became a necessity for a lot of companies — from Fortune 500 companies to small startups. With all the frameworks and libraries available out there, it became a lot easier to start developing machine learning models. The new challenge is to architect a prediction pipeline in the cloud…


Organizing infrastructure for ML applications is a challenging task. Depending on your pipeline you may need to combine different types of virtual machines, organize research infrastructure as well as monitor the system. You may also need to have a way of maintaining ML infrastructure so it’s to deploy updates. …


MLOps is an emerging field of best practices for businesses to run ML workflows in production. There are multiple challenges associated with MLOps which makes it different from traditional DevOps. From monitoring where you may need to monitor model performance and change in data to cost and scale optimizations where…


Satellite imagery is becoming a more and more important source of insights about changes that happen worldwide. There are multiple satellites that provide publicly available data with almost full earth coverage and almost weekly frequency.

One of the main challenges with satellite imagery is to deal with getting insights from…

Rustem Feyzkhanov

I'm a senior machine learning engineer at Instrumental, where I work on analytical models for the manufacturing industry, and AWS Machine Learning Hero.

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