Accelerate Machine Learning with a Unified Analytics Architecture

Chapter 4. In-Database Machine Learning

Being able to do machine learning where the data lives has been a goal from the beginning of the data explosion. But what does in-database ML mean, and how does it work?

What Is In-Database ML?

In-database machine learning is the practice of developing and executing ML workloads—exploring and preparing the data; training algorithms; evaluating, saving, and versioning models; and serving those models in production—inside the database that contains the data. As we’ve seen, many business architectures operate with their data science teams extracting subsets and samples of data from data lakes and data warehouses, training models, and then embarking on the journey of MLOps—from building distributed pipelines to do all the data preparation steps on production data, serving models, often via representational state transfer (REST) endpoints, to persisting the inputs and outputs of each prediction, to analyzing those results for concept and feature drift, and finally reextracting the new data to retrain the model, and starting the process over (see Figure 4-1).

Many tools have been created and advertised as simplifications for this involved process, but most present the same problematic strategy: bringing the data to the model. Since modern datasets for ML tend to be huge (terabytes or even petabytes), the principle of data gravity makes moving all that data difficult, if not outright impossible. The solution during training is sampling and working ...