Machine Learning Engineering on AWS

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Introduction to ML Engineering on AWS

Most of us started our machine learning (ML) journey by training our first ML model using a sample dataset on our laptops or home computers. Things are somewhat straightforward until we need to work with much larger datasets and run our ML experiments in the cloud. It also becomes more challenging once we need to deploy our trained models to production-level inference endpoints or web servers. There are a lot of things to consider when designing and building ML systems and these are just some of the challenges data scientists and ML engineers face when working on real-life requirements. That said, we must use the right platform, along with the right set of tools, when performing ML experiments and deployments ...