AWS re:Invent Recap: Amazon SageMaker Clarify

Amazon SageMaker Clarify

What happened?

AWS released Amazon SageMaker Clarify, a new tool for mitigating bias in machine learning model that helps customers more accurately and rapidly detect bias to build better solutions. This provides critical data and insights that increase transparency to help support analysis and explanation of model behavior to stakeholders and customers.

Why is it important?

  • Easily Detect Bias: SageMaker Clarify will help data scientists detect bias in data sets before training and their models after training.
  • Valuable Metrics & Statistics: It explains how feature values contribute to the predicted outcome, both for the model overall and for individual predictions.
  • Build Better Solutions: With the capability for developers to specify important model attributes, such as location, occupation, age, teams are better able to focus the set of algorithms in a sophisticated way to detect any presence of bias in those attributes. This enables teams to build the most accurate and effective solutions that drive client success.

Why are we excited?

With Amazon SageMaker Clarify, we can now better understand each feature in our ML models and give more detailed explanations to stakeholders. It provides transparency in model understanding that gives leadership more valuable information to inform critical business decision-making. SageMaker Clarify also includes feature importance graphs that explain model predictions and produce reports for presentations to better highlight any significant business impacts.


SageMaker Clarify is available in all regions where Amazon SageMaker is available. The tool will come free for all current users of Amazon SageMaker.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!

AWS re:Invent Recap: Machine Learning Keynote

Here are the key announcements from the re:Invent 2020 Machine Learning Keynote:

  1. Faster Distributed Training on Amazon SageMaker is the quickest and most efficient approach for training large deep learning models and datasets. Through model parallelism and data parallelism, SageMaker distributed training automatically splits deep learning models and datasets for training in significantly less time across AWS GPU instances.
  2. Amazon SageMaker Clarify detects potential bias during all phases of the data preparation, model training, and model deployment, giving development teams greater visibility into their training data and models to resolve potential bias and explain predictions in greater detail.
  3. Deep Profiling for Amazon SageMaker Debugger gives developers the capability to train models at a quicker pace by monitoring system resource utilization automatically and providing notifications of training bottlenecks.
  4. Amazon SageMaker Edge Manager: provides developers the tools to optimize, secure, monitor, and maintain ML model management on edge devices like smart cameras, robots, personal computers, and mobile devices.
  5. Amazon Redshift ML empowers data analyst, development, and scientist teams to create, train, and deploy machine learning (ML) models using SQL commands. Teams can now build and train machine learning models from Amazon Redshift datasets and apply them to use cases.
  6. Amazon Neptune ML leverages Graph Neural Networks (GNNs) to make easy, fast, and more accurate predictions using graph data. The accuracy of most graph predictions increases to 50% with Neptune ML when compared to non-graph prediction methods. The selection and training of the best ML model for graph data are automated and lets users run ML on their graph directly using Neptune APIs and queries. ML teams can now create, train, and apply ML on Neptune data, reducing the development time from weeks down to a matter of hours.
  7. Amazon Lookout for Metrics applies ML to detect metrics anomalies in your metrics to perform proactive monitoring of the health of your business, issue diagnosis, and opportunity identification quickly that can save costs, increase margins, and improve customer experience.
  8. Amazon HealthLake leverages ML models to empower healthcare and life sciences organizations to aggregate various health information from different silos and formats into a centralized AWS data lake to standardize health data.

If you’re looking to explore these services further and need some guidance, let us know and we’ll connect you to an Idexcel expert!