Moving across the typical machine learning lifecycle can be a nightmare. From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot.
As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.
Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models. To do that, you’d need to take a systematic approach to MLOps—enter platforms!
Machine learning platforms are increasingly looking to be the “fix” to successfully consolidate all the components of MLOps from development to production. Not only does the platform give your team the tools and infrastructure they need to build and operate models at scale, but it also applies standard engineering and MLOps principles to all use cases.
But here’s the catch: understanding what makes a platform successful and building it is no easy feat. With so many tools, frameworks, practices, and technologies available, it can be overwhelming to know where to start. That’s where this guide comes in!