Deploying an ML Model is easy with MLFlow and AWS Sagemaker. Step-by-step Tutorial
Knowing how to properly fine-tune an ML model is great, but the question arises of how to let other users, inside or outside the company, to use it. That’s when the rubber hits the road because only sharing your model and running it on real data creates actual value for the business.
At the same time, deploying requires a different set of knowledge that has become an important domain in data science. The questions are: “Where to start your introduction with the cloud solutions?”, “What service provider to use?”, “How to deploy and keep the model live?”.
The data analytics world develops rapidly, and working day-on-day on your skills is essential to keep up with the market. The good news is that the new software developments that make data scientist’s life easier are showing up every day.
With the right set of tools pushing the ML model live is easy. Today I’ll show one of the approaches that might work for you. Let’s start learning some ML engineering from here.
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