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Top Generative Artificial Intelligence AI Courses in 2024

Marktechpost

In recent years, generative AI has surged in popularity, transforming fields like text generation, image creation, and code development. Learning generative AI is crucial for staying competitive and leveraging the technology’s potential to innovate and improve efficiency.

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Evaluation of generative AI techniques for clinical report summarization

AWS Machine Learning Blog

This is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API. It can be achieved through the use of proper guided prompts.

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Introducing the AWS Generative AI Innovation Center’s Custom Model Program for Anthropic Claude

AWS Machine Learning Blog

Since launching in June 2023, the AWS Generative AI Innovation Center team of strategists, data scientists, machine learning (ML) engineers, and solutions architects have worked with hundreds of customers worldwide, and helped them ideate, prioritize, and build bespoke solutions that harness the power of generative AI.

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Top Generative Artificial Intelligence AI Courses in 2024

Marktechpost

In recent years, generative AI has surged in popularity, transforming fields like text generation, image creation, and code development. Learning generative AI is crucial for staying competitive and leveraging the technology’s potential to innovate and improve efficiency.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps).

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning Blog

By investing in robust evaluation practices, companies can maximize the benefits of LLMs while maintaining responsible AI implementation and minimizing potential drawbacks. To support robust generative AI application development, its essential to keep track of models, prompt templates, and datasets used throughout the process.

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From concept to reality: Navigating the Journey of RAG from proof of concept to production

AWS Machine Learning Blog

Generative AI has emerged as a transformative force, captivating industries with its potential to create, innovate, and solve complex problems. Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements.