This course is a gentle introduction to Weights & Biases with a focus on experiment tracking. Learn to track, visualize, and optimize your ML experiments, streamline collaboration with your team, and make your projects efficient and reproducible.
0.5 Hour
Free
Your goals
Sign up for this course to:
Discover the essential features of Weights & Biases for experiment tracking, hyperparameter optimization, data visualization, and collaboration
Enhance your machine learning productivity and rapidly iterate on experiments to achieve better results
Learn how to integrate W&B with your Python training script
I'm new to W&B and already learnt a lot about how useful this tool is.
A superb, hands-on intro to W&B.
A well guided hands-on intro to W&B which gives a good idea of its experiment tracking capabilities.
Great Refresher.
Great refresher course for me since I have been away from WandB for several years.
Perfect entrypoint to W&B.
been using wandb for a long time now, and came across courses offered by W&B.
This course seems like the perfect entry and glimpse of what W&B has to offer!
and the best thing is, it's just around 10 minutes of content that you can start and finish anytime without any worries!
PS: looking for a more detailed, more features covered, and a longer version of everything W&B course!
W&B Platform is really insightful.
It's amazing being led to this amazing platform where I can readily practice MLOps and LLMOps all from an amazing team. With just some simple steps one is presented with a vast range of toolkits that makes building Machine Learning products and LLM-powered products a presentable, explainable, and illustrative process/endeavour. W&B makes AI development especially Generative AI Development a communicable endeavor actually. I mean, it adds a great deal of white tints to our blackbox. Cheers! We're just getting started, and thus far I've been well-led.
Course instructor
Scott Condron
MLEWeights & Biases
Scott Condron is a Machine Learning Engineer at W&B and works on the Growth team. He has a background in using Machine Learning for Speech and Audio applications and previously worked as a Research Engineer at Speech Graphics developing audio-driven facial animation tools.
Overcome model chaos, automate key workflows, ensure governance, and streamline the end-to-end model lifecycle. This course will provide you with the concepts, best practices, and tools to level up your model management and drive success.
Bringing machine learning models to production is challenging, with a continuous iterative lifecycle that consists of many complex components. Having a disciplined, flexible and collaborative process - an effective MLOps system - is crucial to enabling velocity and rigor, and building an end-to-end machine learning pipeline that continually delivers production-ready ML models and services.
Streamline your ML workflows and save valuable time by automating your pipelines and deploying models with confidence. Learn how to use GitHub Actions and integrate W&B experiment tracking in this practical, hands-on learning experience.
Gain expertise in data validation to build robust production ML pipelines, detect data drift, and manage data quality using cutting-edge automated toolkits.
Learn to optimize decision rules, translating machine learning predictions into actionable insights. Discover how to achieve practical value and business impact by measuring performance using business metrics, and deploy ML models successfully.
This compact course, led by ML Success Engineer Ken Lee, dives into advanced model management utilizing Weights and Biases for logging, registering, and managing ML models.
This course introduces Weave by Weights & Biases, a toolkit for developing Generative AI applications. Learn to log, debug, and evaluate language model workflows, ensuring accurate and consistent results across your projects.
