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

AWS Machine Learning

Many businesses already have data scientists and ML engineers who can build state-of-the-art models, but taking models to production and maintaining the models at scale remains a challenge. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations.

Analytics 132
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23 Inspiring Women to Watch in 2023

TechSee

She combines expertise in operations management, finance, customer operations, strategy development and execution, complex problem solving, and large organization leadership with complex negotiation, analytical, and interpersonal skills. She is a force to be reckoned with as a writer and speaker on customer experience.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. About the Authors Stephen Randolph is a Senior Partner Solutions Architect at Amazon Web Services (AWS).

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

AWS Machine Learning

Construction Technology Solutions - Construction Data Analytics and Reporting. Industry insights – Your LLMs can use industry pain points, news, and other resources to enrich personalized content. Client-Centric Approach: We put our clients at the heart of everything we do. Our offerings include: 1.

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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. Solution overview The batch inference feature in Amazon Bedrock provides a scalable solution for processing large volumes of data across various domains.

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Automated exploratory data analysis and model operationalization framework with a human in the loop

AWS Machine Learning

Now we have low-code and no-code tools like Amazon SageMaker Data Wrangler , AWS Glue DataBrew , and Amazon SageMaker Canvas to assist with data feature engineering. However, a lot of these processes are still currently done manually by a data engineer or analyst who analyzes the data using these tools. SageMaker Pipeline Execution.

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Build an agronomic data platform with Amazon SageMaker geospatial capabilities

AWS Machine Learning

These platforms help farmers make sense of their data by integrating information from multiple sources for use in visualization and analytics applications. By removing masked pixels (clouds) from further image processing, downstream analytics and products have improved accuracy and provide value to farmers and their trusted advisors.

APIs 96