Machine learning is becoming essential for a lot of companies and they want to use it to optimize their operations and make new services. One of the challenges is that sometimes you need to deploy a model in an environment where you have limited internet connection and no operators to…

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…

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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