Engineering an LLM Application: Chat with MLOps World
Develop a chatbot using MLOps World content, exploring techniques to transform it from concept to production-ready application.
Dreambooth - Stable Diffusion for Custom Images
Explore stable diffusion techniques for creating custom images using Dreambooth. Learn color manipulation, texture enhancement, and image blending to transform ordinary photos into extraordinary art.
Systematic Approaches and Creativity - Building DoorDash's ML Platform During the Pandemic
Explore DoorDash's ML platform development journey, focusing on collaboration, guardrails, principled approaches, and architecture powering billions of daily predictions for diverse use cases.
Closing the Production Gap with MLOps
Explore MLOps strategies to accelerate deployment, monitor models, ensure governance, and manage the model lifecycle, including automated retraining and challenger models in production.
The Rapid Evolution of the Canonical Stack for Machine Learning
Explore the evolution of AI/ML tech stacks and the emergence of a canonical stack for machine learning, democratizing AI for companies of all sizes.
MLOps at Acerta - Automating the Machine Learning Lifecycle for Manufacturing
Automate the machine learning lifecycle for manufacturing, enhancing efficiency, scalability, and reproducibility. Learn to build a robust MLOps platform for production-grade systems.
Iterative Development Workflows for Building AI Applications
Explore iterative development workflows for AI applications using Snorkel framework. Learn guided error analysis, quality improvement techniques, and collaboration with domain experts.
MLOps with PyCaret - Practical Demo for Data Science Workflows
Supercharge your data science team's productivity with PyCaret, an open-source ML library for efficient model preparation and deployment in Python.
Machine Learning Tools: Skyline and RL-Scope for Performance Optimization
Explore Skyline and RL-Scope, two innovative tools for optimizing machine learning performance. Learn about interactive profiling, visualization, and debugging for PyTorch networks and cross-stack profiling for deep reinforcement learning.
Creating Machine Learning Retraining Pipelines with Kubeflow and Kubernetes
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
How to Iterate and Productionize Machine Learning Models with Speed
Explore strategies to overcome deployment challenges and accelerate the productionization of machine learning models for faster impact.
Machine Learning Monitoring in Production - Lessons Learned from 30+ Use Cases
Discover practical strategies for effective machine learning monitoring in production, drawing insights from over 30 real-world use cases.
Build High Performance MLOps With ML Monitoring and AI Explainability
Discover techniques for enhancing MLOps through ML monitoring and AI explainability. Learn to identify model failures, address operational issues, and streamline model deployment using advanced analytics.
The Ops in MLOps - Cost Predictive, Security Compliant, and Data Governance Techniques
Explore techniques for creating ML environments with cost predictability, security compliance, and proper data governance. Focus on the often-overlooked Ops components of MLOps.
From Concept to Production: Template for the Entire ML Journey - Part 1
Develop a production-ready ML pipeline from concept to deployment using Python. Gain hands-on experience with data prep, model training, and deployment on Google Cloud Platform.
Unlock the Power of MLFlow with Multi-Cloud Compute Infrastructure
Harness MLFlow's potential using multi-cloud compute infrastructure. Learn integration techniques, architectural considerations, and scaling strategies for effective ML training and deployment across clouds.
MLOps with Dataiku - Considerations for Model Deployment and Monitoring
Explore key MLOps concepts, deployment readiness, model monitoring strategies, and how Dataiku streamlines MLOps processes for efficient AI industrialization.
Deploying Efficient Data-Centric AI 100x Faster
Accelerate AI application development and deployment 100x using dedicated platforms, data labeling, pre-built models, and iterative design. Explore region classification and deep-trained object detection approaches.
MLOps with Orchestration: Accelerating Deployment of AI - Workshop Sessions
Accelerate AI deployment with MLOps orchestration. Learn to integrate tools, streamline processes, and handle complex use cases for efficient ML application development and continuous delivery in production environments.
How Not to Let Your Data and Model Drift Away Silently
Discover strategies to prevent data and model drift, ensuring ML models maintain performance and value over time in production environments.
From Concept to Production: Building a Production-Ready ML Pipeline - Part 2
Develop a production-ready ML pipeline from concept to deployment using Python, Kubeflow, and Google Cloud Platform. Master data prep, model training, and orchestration techniques.