MLOps with MLflow: Fine-Tuning LLMs - Tracking Experiments and Logging Models
Fine-tune LLMs, track experiments, and log models using MLflow for efficient MLOps practices in data science and machine learning projects.
Transforming a Jupyter Notebook into a Reproducible Pipeline for ML Experiments
Learn to transform Jupyter Notebooks into reproducible ML pipelines using Git and DVC. Explore techniques for experiment tracking, modularization, and result comparison in a text2image project with Stable Diffusion.
MLtraq - Track Your ML/AI Experiments at Hyperspeed
Explore MLtraq's superior speed and capabilities for tracking ML/AI experiments. Compare leading tools, understand performance factors, and learn efficient Python object serialization techniques.
Advanced Experiment Tracking for LLM-Powered Applications with Customized Open-Source MLFlow
Discover Intuit's use of open-source MLFlow for enhancing ML models and LLM applications through experiment tracking, reproducibility, model lineage, and streamlined review workflows.
MLflow - Platform for Complete Machine Learning Lifecycle
Discover how MLflow simplifies machine learning development by tracking experiments, registering projects, and streamlining model deployment across various platforms.
MLOps: From Jupyter to Production - Conf42 ML 2024
Learn to transition machine learning projects from Jupyter notebooks to production environments, covering MLOps best practices and real-world implementation strategies.
Experiment Tracking in the Age of LLMs - MLOps Podcast
Explore experiment tracking in the age of LLMs with Neptune's CEO. Gain insights on managing ML experiments, prompt engineering, and achieving control and confidence in model development.
MLOps MLflow: Deep Learning Frameworks - PyTorch and TensorFlow Integration
Explore integrating deep learning frameworks Pytorch and Tensorflow with MLflow for enhanced model management and tracking.
MLOps: Integrating HuggingFace Transformers with MLflow
Integrate HuggingFace Transformers with MLflow for efficient MLOps, enhancing model tracking and deployment in machine learning projects.
Kubeflow vs MLFlow: Choosing the Right MLOps Platform
Explore production-grade MLOps with Kubeflow and MLFlow. Compare features, benefits, and use cases to choose the right open-source tool for your machine learning operations.
Kubeflow vs MLFlow: Comparing Open-Source MLOps Platforms
Compare Kubeflow and MLFlow for MLOps, exploring their similarities, differences, and selection criteria. Gain insights into open-source machine learning operations and Canonical's solutions.
Introduction to MLFlow: Managing Machine Learning Workflows
Explore MLFlow's role in managing machine learning workflows, its key components, and how it fits into the MLOps ecosystem. Learn about Charmed MLFlow and its applications.