Python and distributed machine learning
In today's computing world, machine learning is hitting a performance block. More and more companies want to run them on-demand, instead of as batch processes and want their ML models to deliver results in real-time. Often, the datasets are big-data. Thus, the ML frameworks that data scientsits learnt (pandas, scikit-learn, pyTorch, keras) and know to use don't scale well in this fast production environment or is too cumbersome to implement. This blog explores the approaches the ML, DevOps, HPC industry has arrived at in 2019.
