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Boost employee productivity with automated meeting summaries using Amazon Transcribe, Amazon SageMaker, and LLMs from Hugging Face

AWS Machine Learning Blog

The service allows for simple audio data ingestion, easy-to-read transcript creation, and accuracy improvement through custom vocabularies. Hugging Face is an open-source machine learning (ML) platform that provides tools and resources for the development of AI projects.

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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

AWS Machine Learning Blog

If you’re not actively using the endpoint for an extended period, you should set up an auto scaling policy to reduce your costs. SageMaker provides different options for model inferences , and you can delete endpoints that aren’t being used or set up an auto scaling policy to reduce your costs on model endpoints.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Training Models on Streaming Data [Practical Guide]

The MLOps Blog

These days when you are listening to a song or a video, if you have auto-play on, the platform creates a playlist for you based on your real-time streaming data. It provides a web-based interface for building data pipelines and can be used to process both batch and streaming data. Happy Learning!

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Streaming data to a BigQuery table with GCP

Mlearning.ai

BigQuery is very useful in terms of having a centralized location of structured data; ingestion on GCP is wonderful using the ‘bq load’ command line tool for uploading local .csv PubSub and Dataflow are solutions for storing newly created data from website/application activity, in either BigQuery or Google Cloud Storage.

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How to Build ML Model Training Pipeline

The MLOps Blog

Complete ML model training pipeline workflow | Source But before we delve into the step-by-step model training pipeline, it’s essential to understand the basics, architecture, motivations, challenges associated with ML pipelines, and a few tools that you will need to work with.

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