Tag Archive: Machine learning

Fine Tuning vs. Prompt Engineering Large Language Models

Author: Niels Bantilan When to manipulate the input prompt and when to roll up your sleeves and update parameter weights. Chances are you’ve already interacted in some way with a Large Language Model (LLM): either through a hosted interface like ChatGPT, HuggingChat and Bard,... View article

We need POSIX for MLOps

Author: Médéric Hurier (Fmind) If you work on MLOps, you must navigate an ever-growing landscape of tools and solutions. This is both an intense source of stimulation and fatigue for MLOps practitioners. Vendors and users face the same problem: How can we combine all these tools... View article

Traceability & Reproducibility

Author: Vechtomova Maria In the context of MLOps, traceability is the ability to trace the history of data, code for training and prediction, model artifacts, and environment used in development and deployment. Reproducibility is the ability to reproduce the same... View article

MLOps Maturity Assessment

Author: Vechtomova Maria As more and more companies rely on machine learning to run their daily operations, it’s becoming important to adopt MLOps best practices. However, it can be hard to find structured information on what those best practices actually... View article

Data Engineering

MLOps is 98% Data Engineering.

MLOps emerged as a new category of tools for managing data infrastructure, specifically for ML use cases with the main assumption being that ML has unique needs. After a few years and with the hype gone, it has become apparent that MLOps overlap more with Data Engineering than most people believed. Let’s see why and what that means for the MLOps ecosystem.... View article

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