Remove 2010 Remove APIs Remove Metrics
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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning

They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data.

APIs 101
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Customizing coding companions for organizations

AWS Machine Learning

This benefits enterprise software development and helps overcome the following challenges: Sparse documentation or information for internal libraries and APIs that forces developers to spend time examining previously written code to replicate usage. Inadvertent use of deprecated code and APIs by developers.

APIs 102
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Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning

If you need an automated workflow or direct ML model integration into apps, Canvas forecasting functions are accessible through APIs. When the model is ready, select the model and click on the latest version Review the model metrics and column impact and if you are satisfied with the model performance, click Predict.

Finance 121
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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

Analytics 122
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The Definitive 2021 Guide to Customer Effort Score

Nicereply

Created in 2010, the Customer Effort Score is fairly new to the scene but is becoming increasingly more popular. Customer Effort Score is a metric, which customer service teams are using to evaluate how easy customers thought it was to get a resolution to their recent contact. And the metric works. looks pretty good, right?

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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Evaluate model performance on the hold-out test data with various evaluation metrics. This notebook demonstrates how to use the JumpStart API for text classification. After the fine-tuning job is complete, we deploy the model, run inference on the hold-out test dataset, and compute evaluation metrics. Text classification.

Scripts 84
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A review of purpose-built accelerators for financial services

AWS Machine Learning

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. From 2010 onwards, other PBAs have started becoming available to consumers, such as AWS Trainium , Google’s TPU , and Graphcore’s IPU.