Unite.AI

NOVEMBER 20, 2024

These agents perform tasks ranging from customer support to software engineering, navigating intricate workflows that combine reasoning, tool use, and memory. This is where AgentOps comes in; a concept modeled after DevOps and MLOps but tailored for managing the lifecycle of FM-based agents.

LLM 179

LLM AI AI DevOps 179

Mlearning.ai

DECEMBER 21, 2023

Design patterns in software engineering are typical solutions to common problems in software design. They represent best practices, evolved over time, and are a toolkit for software developers to solve common problems efficiently. Source: Image by the Author What are Design Patterns?

Software Engineer 52

Software Engineer Software Development DevOps Algorithm 52

AWS Machine Learning Blog

MAY 1, 2024

Praveen Kumar Jeyarajan is a Principal DevOps Consultant at AWS, supporting Enterprise customers and their journey to the cloud. He has 13+ years of DevOps experience and is skilled in solving myriad technical challenges using the latest technologies. He holds a Masters degree in Software Engineering.

Chatbots 121

Chatbots Automation Machine Learning DevOps 121

AWS Machine Learning Blog

JULY 25, 2024

Given this analysis, I categorize this input as: C " } } } } The trace shows that after reviewing the conversation history, the evaluator concludes, “the agent will be unable to answer or assist with this question using only the functions it has access to.” Bobby Lindsey is a Machine Learning Specialist at Amazon Web Services.

Conversational AI 125

Conversational AI Machine Learning AI AI 125

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework. pre-deployment checks).

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

Explosion

JUNE 18, 2021

How implement models ML fundamentals training and evaluation improve accuracy use library APIs Python and DevOps What when to use ML decide what models and components to train understand what application will use outputs for find best trade-offs select resources and libraries The “how” is everything that helps you execute the plan.

NLP 52

NLP Machine Learning Natural Language Processing DevOps 52