The hidden link between data-centric machine learning and MLOps — You start your day with jupyter lab, and look at the latest state of affairs from yesterday. The notebook springs up on port 8888 and you see your matplotlib graph from yesterday, annoyingly stuck at 80% accuracy. After a few hours of hard work, you finally find a hyperparameter that…

Why data scientists need to own their ML workflows in production — This article discusses the benefits of giving data scientists ownership of their workflows in a production environment. We also discuss ZenML, an open-source framework that is built specifically to facilitate this ownership. Why do we need ML pipelines? Let’s start with the most obvious question: Why do we need ML pipelines in the first place? …

Eliminate technical debt with iterative, reproducible pipelines — Today, Machine Learning powers the top 1% of the most valuable organizations in the world (FB, ALPH, AMZ, N etc). However, 99% of enterprises struggle to productionalize ML, even with the possession of hyper-specific datasets and exceptional data science departments. Going one layer further into how ML propagates through an…

Optimize ML training costs on the cloud using spot instances — Every organization at any scale understands that leveraging the public cloud is a trade-off between convenience and cost. While cloud providers like Google, Amazon, and Microsoft have immensely reduced the barrier of entry for machine learning, GPU costs are still at a premium. There is an increasing fear in the…

Strike a balance between rapid results and high-quality coding. — Okay, let's make it clear at the start: This post is NOT intended for people who are doing one-off, siloed projects like participating in Kaggle competitions or doing hobby projects on Jupyter notebooks to learn the trade. The value of throw-away, quick, dirty script code is obvious there — and…

It is now widely agreed that reproducibility is an important aspect of any scientific endeavor. With Machine Learning being a scientific discipline, as well as an engineering one, reproducibility is equally important here. There is widespread fear in the ML community that we are living through a reproducibility crisis. Efforts…

Just a few days ago, I was able to share my thoughts on the state of Machine Learning in production, and why it’s (still) broken, on the MLOps World 2020. Read on for a writeup of my presentation, or checkout the recording of the talk on Youtube. From research to production By now, chances…

One attempt to ensure that ML models generalize in unknown settings is splitting data. This can be done in many ways, from 3-way (train, test, eval) splits to k-splits with cross-validation. …

Around 87% of machine learning projects do not survive to make it to production. There is a disconnect between machine learning being done in Jupyter notebooks on local machines and actually being served to end-users to provide some actual value. Source: Hidden Technical Debt in Machine Learning Systems (Sculley et…