SigOpt combines scale-oriented platform engineering, algorithmic optimization research engineering, applied machine learning, and user-focused design when developing our product.
This unique combination makes our product unique among MLOps solutions for training and tuning. Other tracking solutions may make it just as easy to track a run, but do not include an easy way to scale intelligent hyperparameter optimization algorithms like Bayesian optimization. Others may include intelligent hyperparameter optimization packages, but exclude agnostic design that makes these algorithms portable across any modeling library.
As a result, SigOpt’s users benefit from a combination of intelligent optimization, the most complete set of advanced experimentation features, and an easy way to operationalize model development. Here are the key components of SigOpt that make this possible.
- Runs: Execute a run to track training and organize modeling attributes
- Dashboard: Create projects, customize visualizations, and collaborate with teams
- Experiments: Automate hyperparameter optimization
- Metrics: Define, select, and analyze a variety of metrics
- Advanced Experimentation: Explore model performance more rigorously
Each of these components is uniquely designed to help you develop a more rigorous, scalable, and repeatable approach to model development. In this blog post series, we are going to explore each of these core components in a bit more detail and give you access to underlying resources to implement them in your workflow.
If you want to try out the product, sign up for free access, execute a run to track your training, and launch an experiment to automate hyperparameter optimization. If you want to learn more about our products, track industry news, and hear from our research team, follow our blog or subscribe to our youtube channel.
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