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23 Inspiring Women to Watch in 2023

TechSee

She is also an experienced National Account Manager with a demonstrated history of working in the hospitality industry. Caroline Yap, Director of AI Practice, Google Cloud – Caroline’s team accelerates customer transformations with AI and Industry Solutions, including Contact Center AI (CCAI), Vertex AI, and DocAI.

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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.

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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. Across accounts, automate deployment using export and import dataset, data source, and analysis API calls provided by QuickSight.

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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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Amazon SageMaker Studio Lab continues to democratize ML with more scale and functionality

AWS Machine Learning

We made it simple to get started with just an email address, without the need for installs, setups, credit cards, or an AWS account. Going forward every customer be required to link their account to a mobile phone number. Eda Johnson, Partner Industry Solutions Manager at Snowflake. Customer success stories.

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

AWS Machine Learning

According to a Forbes survey , there is widespread consensus among ML practitioners that data preparation accounts for approximately 80% of the time spent in developing a viable ML model. However, a lot of these processes are still currently done manually by a data engineer or analyst who analyzes the data using these tools.

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

AWS Machine Learning

Instructions – The following are some examples from the design instructions: Header Design: - Choose an attention-grabbing background color and font that aligns with the client's industry. Prerequisites For this post, you need the following prerequisites: An AWS account. The AWS Command Line Interface (AWS CLI) installed.