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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata. Also note the completion metrics on the left pane, displaying latency, input/output tokens, and quality scores. When the indexing is complete, select the created index from the index dropdown. Rerun the translation.

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Why Accelerated Data Processing Is Crucial for AI Innovation in Every Industry

NVIDIA

In early trials, cuOpt delivered routing solutions in 10 seconds , achieving a 90% reduction in cloud costs and enabling technicians to complete more service calls daily. The company found that data scientists were having to remove features from algorithms just so they would run to completion.

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Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

AWS Machine Learning Blog

Launch the instance using Neuron DLAMI Complete the following steps: On the Amazon EC2 console, choose your desired AWS Region and choose Launch Instance. You can update your Auto Scaling groups to use new AMI IDs without needing to create new launch templates or new versions of launch templates each time an AMI ID changes.

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Optimize data preparation with new features in AWS SageMaker Data Wrangler

AWS Machine Learning Blog

With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. million reviews spanning May 1996 to July 2014. Next, select a training method.

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Time series forecasting with Amazon SageMaker AutoML

AWS Machine Learning Blog

In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). 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.

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MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

The MLOps Blog

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

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Managing Computer Vision Projects with Micha? Tadeusiak 

The MLOps Blog

You would address it in a completely different way, depending on what’s the problem. 2 The more interesting ones are the ones that don’t have the data science teams, or sometimes they don’t even have software developers in the way that they are companies that live in the 21st century.