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

AWS Machine Learning

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. We add this data to Snowflake as a new table.

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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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6 Online Data Analyst Courses

JivoChat

Data-driven decisions are essential in businesses to diminish the chances of errors, and online data analyst courses will teach you how to interpret data precisely. There is where data analysis comes in, you can use the data your company has, and key performance indicators (KPIs) to indicate what path you should follow.

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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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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

Today, companies are establishing feature stores to provide a central repository to scale ML development across business units and data science teams. SageMaker Feature Store consists of an online and an offline mode for managing features. Table formats provide a way to abstract data files as a table.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).