Data Versioning in Generative AI: A Pathway to Cost-Effective ML
Explore data versioning strategies for Generative AI, focusing on managing large-scale unstructured data and optimizing resource usage in model scoring and API interactions.
Applying GitOps Principles in End-to-End MLOps Projects - Interactive Workshop
Implement GitOps principles in MLOps using DVC to create an end-to-end pipeline centered around a git repository as the single source of truth.
Data Versioning in Generative AI - A Pathway to Cost-effective Machine Learning
Explore data versioning strategies for generative AI, focusing on cost-effective techniques to minimize processing time and API calls while enhancing collaboration and efficient versioning of annotations and embeddings.
Building an ML Platform from Scratch
Develop a customizable ML platform using open-source tools. Learn to integrate Cookiecutter, DVC, MLFlow, KFServing, and more for a flexible solution tailored to your organization's needs.
Using Reproducible Experiments to Create Better Machine Learning Models
Enhance ML model efficiency through reproducible experiments. Learn to track hyperparameter, code, and data changes using DVC for grid and random search tuning methods.
Build End-to-End MLOps Platform with Open Source DVC Ecosystem
Master MLOps platform development using DVC ecosystem for data versioning, experiment tracking, and CI/CD pipeline automation. Learn practical implementations across various industries and ML applications.
Adapting Continuous Integration and Continuous Delivery for Machine Learning
Explore MLOps practices for managing ML complexity, adapting CI/CD workflows using Git, GitHub Actions, and DVC to balance experimentation with stability in production.
End to End Deep Learning Project Using MLOPS DVC Pipeline With Deployments Azure and AWS - Krish Naik
Comprehensive guide to building and deploying a deep learning project for chicken disease classification using MLOps tools, DVC pipeline, and cloud platforms Azure and AWS.
End-to-End Deep Learning Project: Kidney Disease Classification with MLflow, DVC, and Deployment
Comprehensive deep learning project on kidney disease classification, covering data ingestion, model preparation, training, evaluation, MLflow integration, DVC pipeline, and AWS deployment.
Open Source Tools for ML Experiments Management
Explore ML workflow management tools, focusing on DVC for efficient data and model handling in AI projects. Learn about versioning, metrics visualization, and addressing unique ML development challenges.
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.
Automating Machine Learning Workflow with DVC
Discover how to automate and streamline machine learning workflows using DVC, from data ingestion to model deployment. Learn to organize pipelines efficiently, regardless of project size.
10X Faster Machine Learning: From R&D to Production
Supercharge ML experimentation with PyTorch Lightning and DVC, and streamline production using Kubeflow. Learn to overcome challenges in complex ML model development and deployment.