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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. They are designed for real-time, interactive, and low-latency workloads and provide auto scaling to manage load fluctuations. The format of the recordings must be either.mp4,mp3, or.wav.

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Llamaindex Query Pipelines: Quickstart Guide to the Declarative Query API

Towards AI

prompt -> LLM prompt -> LLM -> prompt -> LLM retriever -> response synthesizer As a full DAG (more expressive) When you are required to set up a complete DAG, for instance, a Retrieval Augmented Generation (RAG) pipeline. Sequential Chain Simple Chain: Prompt Query + LLM The simplest approach, define a sequential chain.

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Build a news recommender application with Amazon Personalize

AWS Machine Learning Blog

You can take two different approaches to ingest training data: Batch ingestion – You can use AWS Glue to transform and ingest interactions and items data residing in an Amazon Simple Storage Service (Amazon S3) bucket into Amazon Personalize datasets.

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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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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

Ingesting features into the feature store contains the following steps: Define a feature group and create the feature group in the feature store. Prepare the source data for the feature store by adding an event time and record ID for each row of data. Ingest the prepared data into the feature group by using the Boto3 SDK.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

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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.