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. Strong domain knowledge for tuning, including prompt engineering, is required as well.

Generative AI 123

Generative AI Prompt Engineer Prompt Engineering ML 123

AWS Machine Learning Blog

AUGUST 26, 2024

DevOps From a DevOps perspective, the frontend uses Amplify to build and deploy, and the backend is uses AWS Serverless Application Model (AWS SAM) to build, package, and deploy the serverless applications. We use the few-shot prompting technique by providing a few examples to produce an accurate ASL gloss.

Generative AI 117

Generative AI DevOps AI AI 117

AWS Machine Learning Blog

NOVEMBER 19, 2024

These sessions, featuring Amazon Q Business , Amazon Q Developer , Amazon Q in QuickSight , and Amazon Q Connect , span the AI/ML, DevOps and Developer Productivity, Analytics, and Business Applications topics. Attendees will learn practical applications of generative AI for streamlining and automating document-centric workflows.

ML 90

ML Generative AI AI AI 90

AWS Machine Learning Blog

NOVEMBER 17, 2023

Components in agents for Amazon Bedrock Behind the scenes, agents for Amazon Bedrock automate the prompt engineering and orchestration of user-requested tasks. They can securely augment the prompts with company-specific information to provide responses back to the user in natural language.

DevOps 107

DevOps Prompt Engineer Prompt Engineering Automation 107

AWS Machine Learning Blog

FEBRUARY 25, 2025

By automating initial error analysis and providing targeted solutions or guidance, you can improve operational efficiency and focus on solving complex infrastructure challenges within your organizations compliance framework.

Generative AI 63

Generative AI Prompt Engineer Prompt Engineering AI 63

ODSC - Open Data Science

DECEMBER 1, 2023

Over the course of 3+ hours, you’ll learn How to take your machine learning model from experimentation to production How to automate your machine learning workflows by using GitHub Actions.

Data Science 52

Data Science Machine Learning DevOps Large Language Models 52

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Heartbeat

OCTOBER 5, 2023

MLOps, often seen as a subset of DevOps (Development Operations), focuses on streamlining the development and deployment of machine learning models. Where is LLMOps in DevOps and MLOps In MLOps, engineers are dedicated to enhancing the efficiency and impact of ML model deployment.

Large Language Models 52

Large Language Models DevOps Machine Learning Prompt Engineer 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

With these tools in hand, the next challenge is to integrate LLM evaluation into the Machine Learning and Operation (MLOps) lifecycle to achieve automation and scalability in the process. Those metrics serve as a useful tool for automated evaluation, providing quantitative measures of lexical similarity between generated and reference text.

LLM 104

LLM Data Scientist Algorithm Generative AI 104

Mlearning.ai

JUNE 16, 2023

Game changer ChatGPT in Software Engineering: A Glimpse Into the Future | HackerNoon Generative AI for DevOps: A Practical View - DZone ChatGPT for DevOps: Best Practices, Use Cases, and Warnings. GitHub - cirolini/chatgpt-github-actions Aims to automate code review using the ChatGPT language model.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

The MLOps Blog

JUNE 5, 2023

I’ve seen tools that help you write and author pull requests more efficiently, and that help automate building documentation. How do you have a similar tool for experimentation on prompts? Keep track of versions of prompts and what worked and all that. Why do we have MLOps as opposed to DevOps?

Large Language Models 52

Large Language Models Machine Learning Software Engineer Natural Language Processing 52

AWS Machine Learning Blog

NOVEMBER 27, 2024

Amazon Bedrock offers a powerful solution by automating the process of scanning repositories for vulnerabilities and remediating them. This capability is essential for engineers because it speeds up the process of securing code and maintaining compliance with established best practices from the outset.

Generative AI 101

Generative AI Automation DevOps Prompt Engineer 101

O'Reilly Media

NOVEMBER 26, 2024

In 2020, the World Economic Forum estimated that automation will displace 85 million jobs by 2025 but will also create 97 million new jobs. Examples of these skills are artificial intelligence (prompt engineering, GPT, and PyTorch), cloud (Amazon EC2, AWS Lambda, and Microsoft’s Azure AZ-900 certification), Rust, and MLOps.

Machine Learning 110

Machine Learning Software Engineer Python Software Architect 110