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Top 9 YouTube channels to learn large language models  ? 

Data Science Dojo

Learn about Llama Index, LangChain, Redis, Retrieval Augmented Generation, AI observability, and more with this channel. Link to channel Eve on Tech Eye on Tech focuses on the latest business technology and IT topics, including AI, DevOps, security, networking, cloud, storage, and more.

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YouTube channels to get started with large language models  ? 

Data Science Dojo

Learn about Llama Index, LangChain, Redis, Retrieval Augmented Generation, AI observability, and more with this channel. Link to channel Eve on Tech Eye on Tech focuses on the latest business technology and IT topics, including AI, DevOps, security, networking, cloud, storage, and more.

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Top 9 YouTube channels to learn large language models  ? 

Data Science Dojo

Learn about Llama Index, LangChain, Redis, Retrieval Augmented Generation, AI observability, and more with this channel. Link to channel Eve on Tech Eye on Tech focuses on the latest business technology and IT topics, including AI, DevOps, security, networking, cloud, storage, and more.

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Cracking the large language models code: Exploring top 20 technical terms in the LLM vicinity

Data Science Dojo

ChatGPT is also capable of answering questions, providing summaries of factual topics, and generating different creative text formats. Redis Redis is an in-memory data store that can be used to store and retrieve data quickly. Redis is a popular choice for NLP applications because it is fast and scalable.

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Knowledge Bases for Amazon Bedrock now supports hybrid search

AWS Machine Learning Blog

Use cases for hybrid search The following are some common use cases for hybrid search: Open domain question answering – This involves answering questions on a wide variety of topics. Contextual-based chatbots – Conversations can rapidly change direction and cover unpredictable topics.

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Feature Platforms?—?A New Paradigm in Machine Learning Operations (MLOps)

IBM Data Science in Practice

In other words, companies need to move from a model-centric approach to a data-centric approach.” — Andrew Ng To gain a deeper understanding of this topic, Andrew shares his insights here , and he weighs in on where we are today (Model-centric ML) and where we should be going (Data-centric ML).

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Claypot AI CEO on why you should deploy models the hard way

Snorkel AI

And that’s the topic that we will talk about today. So I’ll cover three topics: first, online predictions, and then continual learning, and then real-time monitoring, which is extremely important to enable continual learning. This brings us to the next topic, monitoring. And here’s my email. Here’s my Twitter.

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