Getting Started with the What-if Tool | Introducing the What-If Tool
Summary
TLDRThe video script introduces the What-If Tool, a visual interface for understanding machine learning models and datasets. It's compatible with various platforms and supports TensorFlow models, with extensions for others. The tool features three tabs: data point editor, performance and fairness, and features analysis. It allows users to manipulate data, visualize model predictions, and assess model fairness and performance. The script also demonstrates using the tool with Cloud AI Platform and comparing different models.
Takeaways
- 🛠️ The What-If Tool is a visual interface designed to help users understand their datasets and the output of their machine learning models.
- 📈 It can be used with minimal code on various platforms, including Jupyter Notebooks, Kollab, TensorBoard, and Cloud AI Platform Notebooks.
- 🔍 Analysis with the What-If Tool is beneficial during training data collection, model creation, and post-training evaluation.
- 🌐 The tool supports TensorFlow models natively and can be adapted to work with models from other frameworks with a few lines of code.
- 🏠 An example given in the script involves a TensorFlow model trained on the Iowa Housing dataset, predicting house prices as above or below $160,000.
- 📊 The What-If Tool features three tabs: Data Point Editor, Performance and Fairness, and Features, each offering different insights into the model's performance and data.
- 🔧 The Data Point Editor allows users to inspect and modify individual data points, create custom visualizations, and see model predictions.
- 📉 The Performance and Fairness tab provides common evaluation metrics and tools to slice data and optimize for fairness.
- 📋 The Features tab displays the balance of the dataset and the range of values for each feature, which can be loaded even before model training.
- ☁️ The tool can be used with models deployed on Cloud AI Platform, utilizing the Wit Config Builder for customization.
- 📝 The script demonstrates how to compare the performance of different models using the What-If Tool by adding a line in the Wit Config Builder.
- 📚 The What-If Tool is versatile, supporting both classification and regression models, and can be used for analyzing image and text models as well.
Q & A
What is the purpose of the What-If Tool?
-The What-If Tool is a visual interface designed to help users understand their datasets and the output of their machine learning models.
Who are James and Sara in the context of the video?
-James is an engineer on the What-If Tool, and Sara is a developer advocate on Cloud AI Platform.
On which platforms can the What-If Tool be run?
-The What-If Tool can be run on various platforms including Jupyter Notebooks, Kollab, TensorBoard, and Cloud AI Platform Notebooks.
What are the different phases of machine learning where the What-If Tool can be helpful?
-The What-If Tool can be helpful during training data collection, model creation, and post-training evaluation.
What type of model is used in the example provided in the script?
-In the example, a TensorFlow binary classification model is used, which predicts whether a house is worth more or less than $160,000.
How can the What-If Tool be instantiated in an example?
-The What-If Tool can be instantiated with a few lines of code by passing it a set of test data points along with the ground truth labels for those examples.
What are the three tabs with different features in the What-If Tool?
-The three tabs are the data point editor, Performance and Fairness, and Features.
What can be done in the data point editor tab of the What-If Tool?
-In the data point editor, users can see the model's prediction for each data point, inspect individual data points, change feature values, create custom visualizations, and more.
What is displayed in the Performance and Fairness tab of the What-If Tool?
-The Performance and Fairness tab shows common evaluation metrics for the model and allows users to slice data by different features and apply strategies to optimize for fairness.
What information can be found in the Features tab of the What-If Tool?
-The Features tab shows how balanced the dataset is, displaying the range of values for each feature sent to the What-If Tool.
How can the What-If Tool be used to compare models?
-The What-If Tool can compare models by adding a line to the Wit Config Builder to specify which models to compare, allowing users to evaluate their performance using various capabilities of the tool.
What additional capabilities of the What-If Tool are mentioned for future videos?
-Future videos will explore more about what can be done in the data point editor and Performance and Fairness tabs of the What-If Tool.
Are there any limitations on the types of models the What-If Tool supports?
-The What-If Tool supports TensorFlow models out of the box and can support models built with any other framework with just a few lines of code. It can be used for both classification and regression models.
How can users find more demos of the What-If Tool?
-Users can find more demos of the What-If Tool in the links provided below the video, which show how it works with image and text models.
Outlines
🔍 Introduction to the What-If Tool
James and Sara introduce themselves and the What-If Tool, a visual interface designed to enhance understanding of machine learning models and datasets. They discuss its utility across various platforms like Jupyter Notebooks, Kollab, TensorBoard, and Cloud AI Platform Notebooks. The tool is beneficial during different stages of the machine learning process, including data collection, model creation, and post-training evaluation. Sara also mentions the tool's compatibility with TensorFlow models and its extendibility to other frameworks with minimal additional code.
📊 Exploring the What-If Tool's Features
The presenters delve into the What-If Tool's functionality, starting with an example using a TensorFlow model trained on the Iowa Housing dataset. They demonstrate how to instantiate the tool with test data points and ground truth labels. The tool's interface is explored through its three tabs: the data point editor for model prediction inspection and feature value adjustments, the Performance and Fairness tab for evaluation metrics and fairness optimization strategies, and the Features tab for dataset balance analysis. The tool's versatility for both classification and regression models is highlighted.
🌐 Using the What-If Tool with Cloud AI Platform
Sara showcases how to utilize the What-If Tool with models deployed on Cloud AI Platform. She walks through opening an AI Platform Notebook instance to analyze an XGBoost model, using the Wit Config Builder with the set AI Platform model method, which requires the Google Cloud project ID, model name, and version. The Wit Config Builder is highlighted for its customization capabilities for different use cases.
📊 Comparing Models with the What-If Tool
James and Sara discuss the ability to compare models using the What-If Tool, especially when they are trained on the same dataset. Sara explains the process of adding a configuration line to compare the performance of different models within the tool. They emphasize the tool's applicability to various model types, including classification and regression models.
🎥 Conclusion and Further Exploration
In conclusion, James and Sara recap the introduction to the What-If Tool across different platforms and model types. They encourage viewers to stay tuned for more detailed videos on the tool's capabilities in the data point editor and Performance and Fairness tabs. Sara also directs viewers to additional demos linked below to see the tool in action with image and text models, wrapping up the session with thanks for watching.
Mindmap
Keywords
💡What-If Tool
💡Engineer
💡Developer Advocate
💡Machine Learning Models
💡Jupyter Notebooks
💡Kollab
💡TensorBoard
💡Cloud AI Platform Notebooks
💡Binary Classification
💡Data Point Editor
💡Performance and Fairness
💡Features Tab
💡Wit Config Builder
💡XGBoost Model
💡Regression Models
Highlights
James Wexler and Sara Robinson introduce the What-If Tool, a visual interface for understanding machine learning models and datasets.
The tool can be used on various platforms including Jupyter Notebooks, Kollab, TensorBoard, and Cloud AI Platform Notebooks.
Analysis with the What-If Tool is beneficial during training data collection, model creation, and post-training evaluation.
Sara Robinson demonstrates the What-If Tool's support for TensorFlow models and its adaptability to other frameworks with minimal code.
The What-If Tool can be utilized with models deployed on Cloud AI Platform.
An example showcases the tool's use with a TensorFlow model trained on the Iowa Housing dataset from Kaggle.
The What-If Tool's instantiation requires only a few lines of code and a set of test data points with their ground truth labels.
The Data Point Editor tab allows for model prediction inspection, individual data point analysis, and feature value changes.
Custom visualizations can be created in the Data Point Editor to better understand model predictions.
The Performance and Fairness tab displays common evaluation metrics and allows data slicing by different features for fairness optimization.
The Features tab provides insights into the balance of the dataset and the range of values for each feature.
The What-If Tool can be used for both classification and regression models.
Sara Robinson shows how to use the What-If Tool for models deployed on Cloud AI Platform using an AI Platform Notebook instance.
The Wit Config Builder is used to customize the What-If Tool for specific use cases, including setting AI Platform model methods.
Models trained on the same dataset can be compared using the What-If Tool to analyze their performance.
The What-If Tool offers capabilities to compare models and evaluate their performance and fairness.
Upcoming videos will explore the What-If Tool's features in the data point editor and Performance and Fairness tabs.
Demos of the What-If Tool with image and text models are available in the provided links.
Transcripts
JAMES WEXLER: I'm James, an engineer on the What-If Tool.
SARA ROBINSON: I'm Sara, a developer advocate on Cloud AI
Platform.
JAMES WEXLER: We're going to give you
an introduction into how you can use the What-If Tool to better
understand your machine learning models.
SARA ROBINSON: We'll also show you
how to run it on various platforms and model types.
[MUSIC PLAYING]
So James, can you tell everyone what the What-If Tool is
and when they should use it?
JAMES WEXLER: The What-If Tool is a visual interface designed
to help you understand your data sets
and the output of your machine learning models.
You can run with minimal code from many different platforms,
including Jupyter Notebooks, Kollab, TensorBoard, and Cloud
AI Platform Notebooks.
Analysis using the What-If Tool can
be helpful during training data collection, model creation,
and post-training evaluation.
Sara, you've tried the What-If Tool on a bunch platforms.
What are the ways you can use it?
SARA ROBINSON: The What-If Tool supports TensorFlow models out
of the box and can support models
built with any other framework with just a few lines of code.
You can also use the What-If Tool
with your models deployed on Cloud AI Platform.
JAMES WEXLER: Let's start with an example in Kollab.
Here we've trained a TensorFlow model on this Iowa Housing
data set from Kaggle.
It's a binary classification model
that predicts whether a house is worth more
or less than $160,000.
We can instantiate the What-If Tool with a few lines of code
by passing it a set of test data points
along with the ground truth labels for those examples.
The What-If Tool has three tabs with different features.
And the initial view we're dropped into
is the data point editor.
Here we can see our model's prediction for each data point
we've sent to the What-If Tool.
The blue points indicate houses our model
classified as above $160,000.
In the data point editor, we can also
inspect individual data points, change feature values,
create custom visualizations, and much more.
SARA ROBINSON: The next tab, Performance and Fairness,
shows a couple of common evaluation
metrics for our model.
It also lets us slice data by different features
and apply various strategies to optimize for fairness.
The final tab, Features, shows us
how balanced our data set is.
For each feature, we can see the range of values for the data
we've sent to the What-If Tool.
You can load the Features tab with only a data set even
before you've trained a model.
JAMES WEXLER: That's awesome.
Do you want to show how you can use the What-If Tool for models
deployed on Cloud AI Platform?
SARA ROBINSON: Yes.
Here are a few models I've deployed on AI Platform.
I'll open up an AI Platform Notebook instance here
to analyze this XGBoost model.
Then I create an instance of the Wit Config Builder, this time
using the set AI Platform model method,
passing in my Google Cloud project ID, model name,
and model version.
The Wit Config Builder contains more methods
to customize the What-If Tool for your use case.
JAMES WEXLER: I noticed two of your models
are trained on the same data set.
Can you show us how we can use the What-If
Tool to compare them?
SARA ROBINSON: Sure.
All I need to do is add a line to my Wit Config Builder
to tell it which model I want to compare.
Now you can compare the performance
of both models using various capabilities in the What-If
Tool.
I should also mention that you can
use the What-If Tool for both classification models
and regression models.
JAMES WEXLER: So to recap, we've introduced you
to using the What-If Tool on various platforms and model
types.
Stay tuned for more videos to learn
what you can do in the data point editor
and Performance and Fairness tabs of the tool.
SARA ROBINSON: You can also check out more What-If Tool
demos in the links below to see how it works
with image and text models.
Thanks for watching.
[MUSIC PLAYING]
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