Companies that have pioneered the application of AI at scale did so using their own in-house ML platforms (uber, LinkedIn, Facebook, Airbnb). Many vendors are now making these capabilities available to purchase as off-the-shelf products. There's also a range of open-source tools addressing MLOps. The rush to the space has created a new problem — too much choice. There are now hundreds of tools and at least 40 platforms available:
(Timeline image from Thoughtworks Guide to Evaluating MLOps Platforms.)
Doing data science today is far more difficult than it will be in the next 5 to 10 years. Sharing and collaborating on workflows in painful, pushing models into production is challenging. Let’s explore what Azure provides to ease data scientists’ pains.