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Machine Learning

Building Custom Runtimes with Editors in Cloudera Machine Learning

Cloudera Machine Learning (CML) is a cloud-native and hybrid-friendly machine learning platform. It unifies self-service data science and data engineering in a single, portable service as part of an enterprise data cloud for multi-function analytics on data anywhere. CML empowers organizations to build and deploy machine learning and AI capabilities for business at scale, efficiently and securely, anywhere they want.

From AutoML to AutoMLOps: Automated Logging & Tracking of ML

AutoML with experiment tracking enables logging and tracking results and parameters, to optimize machine learning processes. But current AutoML platforms only train models based on provided data. They lack solutions that automate the entire ML pipeline, leaving data scientists and data engineers to deal with manual operationalization efforts. In this post, we provide an open source solution for AutoMLOps, which automates engineering tasks so that your code is automatically ready for production.

YOLOv5 Now Integrates Seamlessly with ClearML

The popular object detection model and framework made by ultralytics now has ClearML built-in. It’s now easier than ever to train a YOLOv5 model and have the ClearML experiment manager track it automatically. But that’s not all, you can easily specifiy a ClearML dataset version ID as the data input and it will automatically be used to train your model on. Follow us along in this blogpost, where we talk about the possibilities and guide you through the process of implementing them.

Modernizing MLOps: Why I Chose Continual

It says something about a company and its people when they drop the process of formulaic job interviews and just let you pitch ideas for the job you want. That’s what happened when I applied to Continual as a Technical Marketing Manager. Five weeks in, I’m pleased to say I’m working on those same ideas, which I’ll detail in a couple minutes.