Both legacy companies and many tech companies doing commercial ML have pain points regarding:
- Moving to the cloud,
- Creating and managing ML pipelines,
- 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: