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Create Multi-Lingual Subtitles with AssemblyAI and DeepL

AssemblyAI

Before you start To complete this tutorial, you'll need: An upgraded AssemblyAI account A DeepL API account. It returns metadata about the submitted transcription, from which the ID is used to set the ID of the Job. You'll then use DeepL to translate the subtitles into different languages.

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Build a serverless meeting summarization backend with large language models on Amazon SageMaker JumpStart

AWS Machine Learning Blog

You can use large language models (LLMs), more specifically, for tasks including summarization, metadata extraction, and question answering. SageMaker endpoints are fully managed and support multiple hosting options and auto scaling. Complete the following steps: On the Amazon S3 console, choose Buckets in the navigation pane.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you see the complete model lineage with data/models/experiments used downstream?

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Deploy generative AI agents in your contact center for voice and chat using Amazon Connect, Amazon Lex, and Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Complete the following steps to set up your knowledge base: Sign in to your AWS account, then choose Launch Stack to deploy the CloudFormation template: Provide a stack name, for example contact-center-kb. This is where the content for the demo solution will be stored. For the demo solution, choose the default ( Claude V3 Sonnet ).

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Time series forecasting with Amazon SageMaker AutoML

AWS Machine Learning Blog

In the training phase, CSV data is uploaded to Amazon S3, followed by the creation of an AutoML job, model creation, and checking for job completion. All other columns in the dataset are optional and can be used to include additional time-series related information or metadata about each item.

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LLM Fine-Tuning and Model Selection Using Neptune and Transformers

The MLOps Blog

<pre class =" hljs " style =" display : block; overflow-x: auto; padding: 0.5 <pre class =" hljs " style =" display : block; overflow-x: auto; padding: 0.5 Here’s the complete code: import evaluate import torch from tqdm.auto import tqdm import numpy as np def get_logits_and_labels (sample_, max_new_tokens) : sample = sample_.copy()

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Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints

AWS Machine Learning Blog

As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity.

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