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Driving advanced analytics outcomes at scale using Amazon SageMaker powered PwC’s Machine Learning Ops Accelerator

AWS Machine Learning

However, putting an ML model into production at scale is challenging and requires a set of best practices. Integrations with CI/CD workflows and data versioning promote MLOps best practices such as governance and monitoring for iterative development and data versioning.

Analytics 132
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Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock

AWS Machine Learning

We also explore best practices for optimizing your batch inference workflows on Amazon Bedrock, helping you maximize the value of your data across different use cases and industries. In this post, we demonstrate the capabilities of batch inference using call center transcript summarization as an example.

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Reinvent personalization with generative AI on Amazon Bedrock using task decomposition for agentic workflows

AWS Machine Learning

In parallel, OneCompany maintains a market research repository gathered by their researchers, offers industry-specific services outlined in documents, and has compiled approved customer testimonials. UX/UI designers have established best practices and design systems applicable to all of their websites.

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Bring SageMaker Autopilot into your MLOps processes using a custom SageMaker Project

AWS Machine Learning

The majority of enterprise customers already have a well-established MLOps practice with a standardized environment in place—for example, a standardized repository, infrastructure, and security guardrails—and want to extend their MLOps process to no-code and low-code AutoML tools as well.

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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

The context will be coming from your RAG solutions like Amazon Bedrock Knowledgebases. For this example, we take a sample context and add to demo the concept: input_output_demarkation_key = "nn### Response:n" question = "Tell me what was the improved inflow value of cash?" See Amazon Bedrock Recipes and GitHub for more examples.

APIs 142
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Automate cloud security vulnerability assessment and alerting using Amazon Bedrock

AWS Machine Learning

Deploy the solution Complete the following steps to deploy the solution: On the EventBridge console, create a new rule for GuardDuty findings notifications. The example rule in the following screenshot filters high-severity findings at severity level 8 and above. read()) message = response_body.get('content')[0].get("text")

APIs 87