For building any product, whether it includes ML or not, the first step is to identify the problem you're trying to solve. ML is a great tool for solving some problems, but there are many where it's best to start simpler. In this post, let's consider working for a company building a hypothetical product for automatically transcribing university lectures. We're going to build an automatic speech recognition (ASR) system which is tuned to work well for lectures -- this is something that definitely needs machine learning at its core. The product team have decided to start small and focus initially on just Physics lectures as a proof of concept.

This blog covers the oblivious training function and the internals of Maggy presented at Spark AI Summit 2020, on June 26th. TLDR; Maggy is an open-source framework for distributed machine learning. In this post, we introduce a new unified framework for writing core ML training logic as "oblivious training functions". Maggy enables you to reuse the same training code whether training small models on your laptop or reusing the same code to scale out hyperparameter tuning or distributed deep learning on a cluster. Maggy enables the replacement of the current waterfall development process for distributed ML applications, where code is rewritten at every stage, with an iterative development process.