Remove Explainability Remove ML Engineer Remove Prompt Engineer
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Top Artificial Intelligence AI Courses from Google

Marktechpost

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It also includes guidance on using Google Tools to develop your own Generative AI applications.

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Choose Your Weapon: Survival Strategies for Depressed AI Consultants

Towards AI

One example is prompt engineering. Prompt engineering has proved to be very useful. Some people foresaw the emergence of prompt engineer as a new title. Is this the future of the ML engineer? Let’s think about why prompt engineering has been developed.

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AI Engineer’s Toolkit

Towards AI

It starts from explaining what an LLM is in simpler terms, and takes you through a brief history of time in NLP to the most current state of technology in AI. The defacto manual for AI Engineering. This book provides practical insights and real-world applications of, inter alia, RAG systems and prompt engineering.

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Moderate audio and text chats using AWS AI services and LLMs

AWS Machine Learning Blog

The LLM analysis provides a violation result (Y or N) and explains the rationale behind the model’s decision regarding policy violation. The Anthropic Claude V2 model delivers responses in the instructed format (Y or N), along with an analysis explaining why it thinks the message violates the policy.

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How ChatGPT really works and will it change the field of IT and AI??—?a deep dive

Chatbots Life

As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. and we will also explain how GPT can create jobs.

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Unlocking the Potential of LLMs: From MLOps to LLMOps

Heartbeat

Feature Engineering and Model Experimentation MLOps: Involves improving ML performance through experiments and feature engineering. LLMOps: LLMs excel at learning from raw data, making feature engineering less relevant. The focus shifts towards prompt engineering and fine-tuning.

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LLM experimentation at scale using Amazon SageMaker Pipelines and MLflow

AWS Machine Learning Blog

You can customize the model using prompt engineering, Retrieval Augmented Generation (RAG), or fine-tuning. It can also be done at scale, as explained in Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services. You can then select the best model based on the evaluation results.

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