In this session Yaron Haviv discusses how to enable continuous delivery of machine learning to production using Git based ML pipelines (Github Actions) with hosted training and model serving environments. Yaron touches upon how to leverage Git to solve rigorous MLOps...

In this article, we’ll talk about different roles in a data team and discuss their responsibilities. In particular, we will cover: The types of roles in a data team; The responsibilities of each role; The skills and knowledge each role needs to have. Want to listen to...

Avoid these common ML monitoring mistakes – your model’s success hangs in the balance.  So you’ve built a machine learning model that works well in the lab. You’ve validated it, gotten the green light from the internal stakeholders, ensured that it met any regulatory...

When an organization commits to DevSecOps, a fundamental shift takes place across teams. Security becomes everyone’s responsibility. From the beginning of the development cycle, code is reviewed, audited, and tested for security issues. Those issues can be resolved...

Inaccurate models can be costly for businesses. Whether a model is responsible for predicting fraud, approving loans, or targeting ads, small changes in model accuracy can result in big impacts to your bottom line. Over time, even highly accurate models are prone to...

Introduction Common reasons behind computer vision projects failing are (1) a failure to make it to production, (2) the time where your coveted computer vision scientists and engineers spend too much of their time on menial tasks, and (3) increased governance risk. It...