Current best practices for training LLMs from scratch
Introduction Although we’re only a few years removed from the transformer breakthrough, LLMs have already grown massively in performance, cost, and promise. At W&B, we’ve been fortunate to see more teams try to build LLMs than anyone else. But many of the critical details and key decision points are often passed down by word of […]
What is MLOps? Machine learning operations explained
The real challenges in machine learning go beyond just building an ML model. In contrast to conventional software systems, the performance of ML systems can degrade faster, requiring close monitoring and frequent retraining. MLOps—short for Machine Learning Operations—bridges the gap between data science and IT operations, ensuring that models are not only built but deployed, monitored, […]
What is an ML Model Registry?
On this page What is a model registry in ML? Benefits of an ML Model Registry Where does a data and model registry fit in your MLOps stack? The Weights & Biases Registry Exploring the Benefits of W&B Registry How to Get Started with the W&B Registry Conclusion In the rapidly changing landscape of machine […]
Intro to MLOps: Machine learning experiment tracking
On this page What is Experiment Tracking in Machine Learning? Why Do You Need to Track Your ML Experiments? How Do You Track Machine Learning Experiments? Conclusion Imagine you are trying to develop a recipe for the best chocolate chip cookies. After the first try, you might increase the amount of flour. One time, you […]
Intro to MLOps: Hyperparameter tuning
On this page What are Hyperparameters in Machine Learning? What is Hyperparameter Optimization in Machine Learning? How Do You Optimize Hyperparameters? Methods for Automated Hyperparameter Optimization Conclusion References Introduction While the ingredients of a recipe play an important role, the instructions are just as important. Whether you bake a batch of cookies at 160°C for […]
Intro to MLOps: Data and model versioning
On this page Fundamentals of Version Control Version Control in Machine Learning Best Practices of Version Control in Machine Learning Conclusion References In software engineering, it’s common practice to use version control to keep track of changes, ensure traceability and enable collaboration. But version control isn’t just useful in software development – it’s vital in […]
MLOps: A comprehensive look at machine learning operations
The article dives into the world of machine learning operations (MLOps), giving an overview of MLOps, explaining its essential components, and a whole lot more.