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[Overview] MLOps: What It Is, Why it Matters, and How To Implement it

Both legacy companies and many tech companies doing commercial ML have pain points regarding:

  • Moving to the cloud,
  • Creating and managing ML pipelines,
  • Scaling,
  • Dealing with sensitive data at scale,
  • And about a million other problems.

At the same time, if we want to be serious and actually have models touch real-life business problems and real people, we have to deal with the essentials like:

  • acquiring & cleaning large amounts of data;
  • setting up tracking and versioning for experiments and model training runs;
  • setting up the deployment and monitoring pipelines for the models that do get to production.
  • and we need to find a way to scale our ML operations to the needs of the business and/or users of our ML models.

This article gives you broad overview on the topic:

What is MLOps

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