ML Engineer NLP Prompt Engineering 
Marktechpost
MAY 30, 2024
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 covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow.
Towards AI
MAY 30, 2024
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.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
AWS Machine Learning Blog
MAY 13, 2024
In this part of the blog series, we review techniques of prompt engineering and Retrieval Augmented Generation (RAG) that can be employed to accomplish the task of clinical report summarization by using Amazon Bedrock. It can be achieved through the use of proper guided prompts. There are many prompt engineering techniques.
Chatbots Life
MAY 12, 2023
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. Will ChatGPT replace software engineers?
AWS Machine Learning Blog
MARCH 13, 2024
Use LLM prompt engineering to accommodate customized policies The pre-trained Toxicity Detection models from Amazon Transcribe and Amazon Comprehend provide a broad toxicity taxonomy, commonly used by social platforms for moderating user-generated content in audio and text formats. LLMs, in contrast, offer a high degree of flexibility.
AWS Machine Learning Blog
SEPTEMBER 1, 2023
After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. These users need strong end-to-end ML and data science expertise and knowledge of model deployment and inference.
AWS Machine Learning Blog
JULY 24, 2024
Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You can customize the model using prompt engineering, Retrieval Augmented Generation (RAG), or fine-tuning.
Expert insights. Personalized for you.
132 
Let's personalize your content