ClearML-Data
This video covers clearml-data tool, which is meant to help you version control, share and manage your different datasets. It integrates seamlessly in the existing clearml ecosystem, so a lot of terms will feel familiar!
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Pipelines from tasks
This video combines the knowledge we gathered from the previous videos to tackle a cool application of the powers of ClearML: pipelines from tasks. Learn how it works and how to get started.
How to do Hyperparameter Optimization better
A Simple Hyperparameter Optimization Guide.
Pipelines from code
This video combines the knowledge we gathered from the previous videos to tackle a cool application of the powers of ClearML: hyperparameter optimization. Learn how it works and how to get started.
Hyperparameter Optimization (HPO) is simpler than you think.
For a quick refresher on what hyperparameter optimization is and what frameworks and strategies are supported by ClearML out of the box, check out our previous blogpost!
Hyperparameter Optimization
This video combines the knowledge we gathered from the previous videos to tackle a cool application of the powers of ClearML: hyperparameter optimization. Learn how it works and how to get started.
Agent Remote Execution and Automation
This video introduces the clearML agent. A remote worker that will allow you to run tasks remotely and start automating certain workflows. We will cover what it is, what it does and how to set one up.
Experiment Management Best Practices
This video will serve as an overview of some of the more advanced features and neat little trick you can use while working with the experiment manager. It's all about using the full potential of the tool, to make it easier for your mlops people (or yourself) to deploy models properly.
Experiment Manager Hands-on
This video will guide you through the first steps of using the ClearML Experiment Manager. How to install, how to add the necessary code lines and an overview of the web-based UI.
Growing AI Fast with ML-Ops: Breaking the barrier between research and production
AI models get smarter, more accurate, and therefore more useful over the course of their training on large datasets that have been painstakingly curated, often over a period of years. But in real-world applications, datasets start small. To design a new drug, for instance, researchers start by testing a compound and need to use the power of AI to predict the best possible permutation.