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How iFood built a platform to run hundreds of machine learning models with Amazon SageMaker Inference

AWS Machine Learning Blog

Integrating model deployment into the service development process was a key initiative to enable data scientists and ML engineers to deploy and maintain those models. The ML platform empowers the building and evolution of ML systems. The iFoods ML platform, ML Go!

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Saket Saurabh, CEO and Co-Founder of Nexla – Interview Series

Unite.AI

Self-service and collaboration: With Nexla, data consumers not only access data on their own and build Nexsets and flows. They can collaborate and share their work via a marketplace that ensures data is in the right format and improves productivity through reuse. Auto generation: Integration and GenAI are both hard.

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10 Best AI Real Estate Tools (March 2025)

Unite.AI

It aggregates data on over 136 million U.S. With HouseCanary, agents and investors can instantly obtain a data-driven valuation for any residential property, complete with a confidence score and 3-year appreciation forecast. or even local market trends, and get insightful responses to guide your approach Visit DealMachine 9.

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Transforming customer service: How generative AI is changing the game

IBM Journey to AI blog

Generative AI auto-summarization creates summaries that employees can easily refer to and use in their conversations to provide product, service or recommendations (and it can also categorize and track trends). Watsonx.ai is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI.

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Inference AudioCraft MusicGen models using Amazon SageMaker

AWS Machine Learning Blog

The following diagram shows how MusicGen, a single stage auto-regressive Transformer model, can generate high-quality music based on text descriptions or audio prompts. When working with music generation models, it’s important to note that the process can often take more than 60 seconds to complete. Create a Hugging Face model.

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. However, transforming raw LLMs into production-ready applications presents complex challenges.

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Search enterprise data assets using LLMs backed by knowledge graphs

Flipboard

Prepare the test data A sample dataset is needed for testing the functionalities of the solution. In your AWS account, prepare a table using Amazon DataZone and Athena completing Step 1 through Step 8 in Amazon DataZone QuickStart with AWS Glue data. 1 MinContainers Minimum containers for auto scaling.

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