Improving Model Understanding with Captum & Fiddler

Table of Contents

1. Introduction
2. Understanding Model Internals and Predictions
3. Support for Various Types of Models
4. Scalable Implementations of Attribution Algorithms
5. Evaluation Metrics
6. Captain for Research and Production
7. Internal and External Opportunities
8. Fedora's Mission
9. Challenges in Explaining Complex Deep Models
10. Captain's Interface for Python Users
11. Tools and Algorithms for Monitoring and Debugging Models
12. Captain's Role in Model Understanding during Authoring, Exploration, and Evaluation
13. Extending Tools into Complex Machine Learning Ecosystems
14. Learning to Explain Complex Systems

Using Captain and Fiddler Together to Improve AI Model Understanding

Artificial intelligence (AI) has become an integral part of many industries, from healthcare to finance to retail. As AI models become more complex, it becomes increasingly important for developers to understand their model internals and predictions. This is where Captain and Fiddler come in. Captain is a generic and unified model interpretability library that aims to help model developers better understand their models, while Fiddler's mission is to bring trust into AI by ensuring that it is done in a responsible way that is inclusive and accountable.

Understanding Model Internals and Predictions

Captain supports various types of models, including multi-modal models, which means that the model inputs can be both dense and sparse features coming from various different sources. The open source version of Captain library currently offers scalable implementations of a number of gradient and perturbation Based attribution algorithms, and it also includes a couple of evaluation metrics. The library is designed to be used for both research and production.

Support for Various Types of Models

There is a strong need from developers to solve very specific problems within products, and Captain is designed to address this need. The library is also useful for applied researchers who are working on new architectures and capabilities. The models have become more complex, and the problem areas have also become complex. One of the main things that a lot of teams work on is content understanding. When we talk to customers, the most interesting and valuable use cases are those that involve complex data sets, such as text and images, and dense features.

Scalable Implementations of Attribution Algorithms

Captain offers scalable implementations of a number of gradient and perturbation based attribution algorithms. These algorithms help developers understand how the model is making its predictions. For example, if the model is predicting that a certain post is toxic, the attribution algorithm can help developers understand which words or phrases in the post are contributing to that prediction.

Evaluation Metrics

Captain also includes a couple of evaluation metrics that help developers evaluate the performance of their models. These metrics can help developers understand how well their models are performing and identify areas for improvement.

Captain for Research and Production

Captain is designed to be used for both research and production. It is a robust framework that addresses the challenges of model understanding during authoring, exploration, and evaluation. It is also useful for monitoring and debugging models during production.

Internal and External Opportunities

There are both internal and external opportunities for using Captain and Fiddler together to improve AI model understanding. From an internal perspective, there is a strong need from developers and applied researchers to better understand their models. From an external perspective, there is a need to bring trust into AI by ensuring that it is done in a responsible way that is inclusive and accountable.

Fedora's Mission

Fiddler's mission is to bring trust into AI by ensuring that it is done in a responsible way that is inclusive and accountable. This is important because for complex deep models, one of the Core challenges is to explain them. To do this, we need to hook into those models at the appropriate points so that we can properly probe their behavior. This is easier said than done because there are a plethora of frameworks out there, and getting these hooks into the model is a complex task.

Challenges in Explaining Complex Deep Models

For us to be able to serve any model out there, we need a platform and a contract and an interface that is easily accessible to users and which they can make Sense of. It's not feasible for us to manually sit down with every customer and help them import that model. Captain's interface for Python users has defined and standardized that interface, and being an open source library by Facebook, we know that they will support it for a while, which gives us the confidence of using it to extend its benefits to our own users.

Captain's Interface for Python Users

Captain's interface for Python users makes it easy to inject models and understand their behavior. We help our customers understand what we really need, and then it makes model injection for them a lot easier. With the growth of the PyTorch community, we realized that it is essential to have tools and algorithms that can help to monitor and debug the models. They have to be easy to use, solid, and generic.

Tools and Algorithms for Monitoring and Debugging Models

Captain and Fiddler bring together tools and algorithms for monitoring and debugging models. These tools and algorithms help developers understand how their models are performing and identify areas for improvement. They also help developers debug their models and identify issues that may be causing performance problems.

Captain's Role in Model Understanding during Authoring, Exploration, and Evaluation

Captain's role in model understanding during authoring, exploration, and evaluation is critical. It helps developers build better models by providing them with the tools and algorithms they need to understand their models. It also helps them explore and evaluate their models to identify areas for improvement.

Extending Tools into Complex Machine Learning Ecosystems

One of the challenges of using Captain and Fiddler together is extending the tools into complex machine learning ecosystems. There are many models and frameworks out there, and they Interact in very complex ways. They are also being updated and owned by different teams at different cadences. Being able to zoom in and out in terms of either looking at the entire inference graph or looking at the raw inputs and what the output happens across the entire graph is important. Being able to zoom in on specific areas is also important.

Learning to Explain Complex Systems

Learning to explain complex systems is another area of research that We Are interested in. Captain and Fiddler bring together tools and algorithms that can help developers understand complex systems. They help developers understand each model by parts and tally everything together to weave it all together. This is important because a model is not one pickled object. It is a system of models and code that comes together to take in an input and finally give a result.

Highlights

• Captain is a generic and unified model interpretability library that helps model developers better understand their models.
• Fiddler's mission is to bring trust into AI by ensuring that it is done in a responsible way that is inclusive and accountable.
• Captain supports various types of models, including multi-modal models, and offers scalable implementations of a number of gradient and perturbation based attribution algorithms.
• Captain is designed to be used for both research and production and is a robust framework that addresses the challenges of model understanding during authoring, exploration, and evaluation.
• Captain's interface for Python users makes it easy to inject models and understand their behavior.
• Captain and Fiddler bring together tools and algorithms for monitoring and debugging models, helping developers understand how their models are performing and identify areas for improvement.

FAQ

Q: What is Captain? A: Captain is a generic and unified model interpretability library that helps model developers better understand their models.

Q: What is Fiddler's mission? A: Fiddler's mission is to bring trust into AI by ensuring that it is done in a responsible way that is inclusive and accountable.

Q: What types of models does Captain support? A: Captain supports various types of models, including multi-modal models.

Q: What algorithms does Captain offer? A: Captain offers scalable implementations of a number of gradient and perturbation based attribution algorithms.

Q: What is Captain's role in model understanding during authoring, exploration, and evaluation? A: Captain's role in model understanding during authoring, exploration, and evaluation is critical. It helps developers build better models by providing them with the tools and algorithms they need to understand their models.

Q: What is the interface for Python users in Captain? A: Captain's interface for Python users makes it easy to inject models and understand their behavior.

Q: What do Captain and Fiddler bring together? A: Captain and Fiddler bring together tools and algorithms for monitoring and debugging models, helping developers understand how their models are performing and identify areas for improvement.