Building a system of record for ML
How to build a comprehensive system of record for your entire ML team to train, fine-tune, and productionize your models
Created on September 9|Last edited on September 10
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In today’s rapidly evolving world of artificial intelligence, having a robust system of record for experimentation and model management is no longer a luxury—it’s a necessity.
As models grow more complex, data pipelines become more intricate, and the demand for reproducibility intensifies, it’s vital to have a centralized hub that tracks all assets along with their lineage. Weights & Biases (W&B) Models is the solution used by leading teams today for building a system of record for training, fine-tuning, governing, and managing models from experimentation to production. W&B Models provides a unified framework to ensure your machine learning workflows are streamlined, transparent, and reproducible.
Let’s dive into how W&B’s products—Registry, Automations, Launch, Artifacts, Reports, and Experiments—come together to create the ultimate system of record for the AI model lifecycle.
Want to learn more? We're hosting a webinar on September 18th about how we can help you build a system of record for your ML projects. Register here for free.
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Experiments: The powerful core for rapid experimentation and comparison
Every project begins with a first experiment and the core of W&B Models powers that first step—and every subsequent step—on the way to a model in production.
Experiments enables you to track every aspect of your ML experiments, from hyperparameters and performance metrics to code versions and environment settings. You can compare model experiments, identify trends, and make data-driven decisions.
Experiments is also deeply customizable, letting you save the views and metrics you care about most so that you—or your team—has the right information at your fingertips.
Every model training run, hyperparameter sweep, artifact, and more can be organized into projects in discrete teams, ensuring the right people have the right access to the right information.
The power of Experiments lies in its ability to streamline the experimentation process. By providing a centralized dashboard for tracking and visualizing results, Experiments make it easy to iterate quickly and find the best-performing models. Teammates can see each others world, building on promising experiments, and when it's combined with the rest of W&B Models, Experiments becomes even more powerful, feeding into the Registry, triggering Automations, and influencing future training runs.
Artifacts: Lineage tracking for reproducibility
As you train, W&B tracks metrics and important milestones along the way, including the datasets, code, and model parameters you use along the way. All of these are captured as Artifacts.
Artifacts serve as the lineage tracking tool that ensures every dataset, model, and experiment is traceable. By capturing the entire lifecycle of your ML assets, Artifacts makes it easy to understand the provenance of your data and models, from the initial input data to the final inference, and stay compliant with any regulatory bodies important in your industry.
Traceability is invaluable when it comes to debugging, auditing, or simply revisiting past experiments. With Artifacts, you can be confident that your work is reproducible, and your results can be trusted. Whether you’re collaborating with teammates or revisiting a project months later, Artifacts ensures that you have a clear and comprehensive history of your work.
Registry: A single pane of glass for your ML models
At the heart of any system of record is the ability to manage and track models efficiently. Registry serves as the single pane of glass that provides visibility into all your models, datasets, metadata, and their lineage. Whether you’re dealing with dozens or thousands of models, Registry offers a centralized place where you can catalog, version, and manage them with ease.
Registry ensures that your models are not just stored but are also organized with metadata, customizable tags, performance metrics, and deployment status. You can see which models are in production, which ones are under experimentation, and how each version has evolved over time. This transparency is key to maintaining control over your ML projects and ensuring that your team is always on the same page.
Automations: The interaction layer for deploying experiments and models
Once your models are cataloged in the Registry, Automations is your interaction layer that bridges the gap between experimentation and deployment. Automations allows you to define triggers, actions, and workflows that streamline the deployment process. You can automate model training, testing, and deployment, ensuring that the right models are deployed to the right environments at the right time.
Imagine a scenario where your best-performing model in an experiment automatically triggers a deployment to a staging environment for further testing with rigorous inference evaluations. With Automations, this level of sophistication is effortless. By reducing manual intervention, you can focus more on innovation and less on operational overhead.
Launch: The runtime orchestrator for training workloads
Training ML models can be resource-intensive and complex, especially when dealing with large datasets and multiple models. Launch simplifies this by acting as the runtime orchestrator for your training workloads. Whether you’re running experiments on local machines, in the cloud, or across distributed systems, Launch ensures that your workloads are managed efficiently.
With Launch, you can schedule and monitor training jobs, allocate resources dynamically, and even scale up or down based on the demands of your experiments. This level of control ensures that your training processes are both cost-effective and time-efficient, allowing you to iterate faster and achieve better results.
Reports: Communicate results and insights effectively
A crucial part of any ML workflow is the ability to communicate findings clearly and effectively. Reports is a powerful tool that enables you to create interactive, visually compelling reports directly from your experiments. With Reports, you can seamlessly combine code, visualizations, and narrative to share insights with your team, stakeholders, or even the broader community. In fact, you're reading a Report right now.
Whether you’re documenting a complex experiment, presenting results to non-technical stakeholders, or creating a tutorial, Reports make it easy to tell the story of your AI project. The interactive nature of these reports allows others to explore your results in depth, fostering collaboration and enabling more informed decision-making. Charts are dynamic and update in real-time, so documentation is never out of date. By integrating Reports into your workflow, you ensure that your work is not only well-documented but also easily understood.
Conclusion
Building a system of record for AI model development is essential for any team looking to scale their AI projects efficiently and effectively. Weights & Biases offers a comprehensive platform that makes this process seamless, from model management with Registry to automation with Automations, training orchestration with Launch, reproducibility with Artifacts, collaboration with Reports, and rapid experimentation with Experiments.
By adopting W&B Models as your system of record, you can ensure that your ML projects are organized, transparent, and reproducible—paving the way for faster innovation and success. Register for the upcoming webinar where I'll demo how to put these tools together to build an AI system of record for pre-training, fine-tuning, and managing foundation models.
We’ll see you there.
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