The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

The MLOps Blog

MARCH 12, 2024

TL;DR LLMOps involves managing the entire lifecycle of Large Language Models (LLMs), including data and prompt management, model fine-tuning and evaluation, pipeline orchestration, and LLM deployment. Prompt-response management: Refining LLM-backed applications through continuous prompt-response optimization and quality control.

Prompt Engineering 52

Prompt Engineering Prompt Engineer Large Language Models LLM 52

ODSC - Open Data Science

FEBRUARY 24, 2025

This approach allows AI applications to interpret natural language queries, retrieve relevant data, and generate human-like responses grounded in accurate information. When a user inputs a query, an LLM (large language model) interprets it using Natural Language Understanding (NLU). and Mistral.

Data Drift 52

Data Drift Data Ingestion Natural Language Processing LLM 52