By using AWS’s innovative technologies, such as machine learning, to deliver more in-depth insights and provide fans a better understanding of the split-second decisions made on the pitch, Bundesliga Match Facts enables viewers to gain deeper insights into the key decisions in each match. "Amazon SageMaker Clarify seamlessly integrates with the rest of the Bundesliga Match Facts digital platform and is a key part of our long-term strategy of standardizing our ML workflows on Amazon SageMaker. Knowing respective feature attributions and explaining outcomes helps in model debugging and increasing confidence in ML algorithms, which results in higher-quality predictions. With Amazon SageMaker Clarify, the Bundesliga can now interactively explain what some of the key, underlying components are in determining what led the ML model to predict a certain xGoals value. Read our blog post to learn more.īundesliga Match Facts, powered by AWS, provides a more engaging fan experience during soccer matches for Bundesliga fans around the world. SageMaker Data Wrangler offers three balancing operators: random undersampling, random oversampling, and SMOTE to rebalance data in your unbalanced datasets. In case of imbalances, you can use SageMaker Data Wrangler to balance your data. For example, in a financial dataset that contains only a few examples of business loans to one age group as compared to others, the bias metrics will indicate the imbalance so that you can address the imbalances in your dataset and potentially reduce the risk of having a model that is disproportionately inaccurate for a specific age group. SageMaker Clarify then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias. You specify input features, such as gender or age, and SageMaker Clarify runs an analysis job to detect potential bias in those features. If you're reading this post, you probably want to improve your English conversation skills, and the best way to do that is by having actual conversations.With SageMaker Clarify, you can identify potential bias during data preparation without having to write your own code as part of Amazon SageMaker Data Wrangler. Try These Expressions in a Real Conversation!
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |