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Governing the ML lifecycle at scale, Part 3: Setting up data governance at scale

AWS Machine Learning

It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications. This approach was not only time-consuming but also prone to errors and difficult to scale.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning

We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw. For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py

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21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

Callminer

For instance, to improve key call center metrics such as first call resolution , business analysts may recommend implementing speech analytics solutions to improve agent performance management. Successful call centers use analytics to help aid, streamline and maximize customer service and sales needs…”. AmraBeganovich. Kirk Chewning.

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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

However, as a new product in a new space for Amazon, Amp needed more relevant data to inform their decision-making process. Part 1 shows how data was collected and processed using the data and analytics platform, and Part 2 shows how the data was used to create show recommendations using Amazon SageMaker , a fully managed ML service.

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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. 1 with the following additions: The Snowflake Connector for Python to download the data from the Snowflake table to the training instance.

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How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

With SageMaker Processing jobs, you can use a simplified, managed experience to run data preprocessing or postprocessing and model evaluation workloads on the SageMaker platform. Twilio needed to implement an MLOps pipeline that queried data from PrestoDB. For more information on processing jobs, see Process data.

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Guest Blog: Contact Center Talent in These Changing Times, Part 1 – Setting the Stage

Calabrio

The one-size-fit-all script no longer cuts it. Technology is also creating new opportunities for contact centers to not only better serve customers but also gain deep insights through Big Data. With analytics, contact centers can leverage their data to see trends, understand preferences and even predict future requirements.