Explainable AIBeta
Understand AI output and build trust
Explainable AI is a set of tools and frameworks to help you develop interpretable and inclusive machine learning models and deploy them with confidence. With it, you can understand feature attributions in AutoML Tables and AI Platform and visually investigate model behavior using the What-If Tool. It also further simplifies model governance through continuous evaluation of models managed using AI Platform.
Design interpretable and inclusive AI
Build interpretable and inclusive AI systems from the ground up with tools designed to help detect and resolve bias, drift, and other gaps in data and models. AI Explanations in AutoML Tables and AI Platform provide data scientists with the insight needed to improve data sets or model architecture and debug model performance. The What-If Tool lets you investigate model behavior at a glance.
Deploy AI with confidence
Grow end-user trust and improve transparency with human-interpretable explanations of machine learning models. When deploying a model on AutoML Tables or AI Platform, you get a prediction and a score in real time indicating how much a factor affected the final result. While explanations don’t reveal any fundamental relationships in your data sample or population, they do reflect the patterns the model found in the data.
Streamline model governance
Simplify your organization’s ability to manage and improve machine learning models with streamlined performance monitoring and training. Easily monitor the predictions your models make on AI Platform. The continuous evaluation feature lets you compare model predictions with ground truth labels to gain continual feedback and optimize model performance.
Features
AI Explanations
Receive a score explaining how each factor contributed to the final result of the model predictions. Learn how to understand these scores here.
What-If Tool
Investigate model performances for a range of features in your dataset, optimization strategies, and even manipulations to individual datapoint values using the What-If Tool integrated with AI Platform.
Continuous evaluation
Sample the prediction from trained machine learning models deployed to AI Platform. Provide ground truth labels for prediction inputs using the continuous evaluation capability. Data Labeling Service compares model predictions with ground truth labels to help you improve model performance.
Understanding how models arrive at their decisions is critical for the use of AI in our industry. We are excited to see the progress made by Google Cloud to solve this industry challenge. With tools like What-If Tool, and feature attributions in AI Platform, our data scientists can build models with confidence, and provide human-understandable explanations.
Stefan Hoejmose, Head of Data Journeys, Sky
Resources
Pricing
Explainable AI tools are provided at no extra charge to users of AutoML Tables or AI Platform. Note that Cloud AI is billed for node-hours usage, and running AI Explanations on model predictions will require compute and storage. Therefore, users of Explainable AI may see their node-hour usage increase.
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Learn and build
New customers get $300 in free credits to learn and build on Google Cloud for up to 12 months.
Need more help?
Our experts will help you build the right solution or find the right partner for your needs.
This product is in beta. For more information on our product launch stages, see here.
Cloud AI products comply with the SLA policies listed here. They may offer different latency or availability guarantees from other Google Cloud services.