ML Without the Ops: Running Experiments at Scale with Ploomber on AWS

For the past couple of months, we've chatted with many Data Science and Machine Learning teams to understand their pain points. Of course, there are many of these. Still, the one that surprised me the most is how hard it is to get some simple end-to-end workflow working, partially because vendors often lock teams into complicated solutions that require a lot of setup and maintenance. This blog post will describe a simple architecture that you can use to start building data pipelines in the cloud without sacrificing your favorite tooling or recurring high maintenance costs. The solution involves using our open-source frameworks and AWS Batch.