ODSC - Open Data Science

AUGUST 24, 2023

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. Auto Eval Common Metric Eval Human Eval Custom Model Eval 3.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Responsible AI 52

Snorkel AI

JUNE 7, 2023

They focussed largely on the challenges and opportunities in leveraging large language models and foundation models , as well as data-centric AI development approaches. Panel – Adopting AI: With Power Comes Responsibility Harvard’s Vijay Janapa Reddi, JPMorgan Chase & Co.’s

Data Scientist 52

Data Scientist Large Language Models Machine Learning AI 52

Snorkel AI

JUNE 7, 2023

They focussed largely on the challenges and opportunities in leveraging large language models and foundation models , as well as data-centric AI development approaches. Panel – Adopting AI: With Power Comes Responsibility Harvard’s Vijay Janapa Reddi, JPMorgan Chase & Co.’s

Data Scientist 52

Data Scientist Large Language Models Machine Learning AI 52

Snorkel AI

MARCH 2, 2023

Today we’re going to be talking essentially about how responsible generative-AI-model adoption can happen at the enterprise level, and what are some of the promises and compromises we face. The foundation of large language models started quite some time ago. What are the promises?

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Today we’re going to be talking essentially about how responsible generative-AI-model adoption can happen at the enterprise level, and what are some of the promises and compromises we face. The foundation of large language models started quite some time ago. What are the promises?

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

The MLOps Blog

JUNE 27, 2023

W&B (Weights & Biases) W&B is a machine learning platform for your data science teams to track experiments, version and iterate on datasets, evaluate model performance, reproduce models, visualize results, spot regressions, and share findings with colleagues. Is it fast and reliable enough for your workflow?

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134