Automating Production Level Machine Learning Operations on AWS
Explore ML Ops challenges and solutions using AWS services. Learn to bridge the gap between data science and deployment with a "model factory" architecture for effective CI/CD in machine learning.
Inference at Scale with Apache Beam
Learn to deploy and scale machine learning models efficiently using Apache Beam for distributed inference on CPUs and GPUs, with practical insights on parallelizing workloads.
Databricks for MLOps - Experiment Tracking, Model Packaging, and Deployment
Master MLflow in Databricks: Experiment tracking, model packaging, and deployment for efficient machine learning operations.
Introduction to MLOps: What, Why, and How
Learn MLOps fundamentals, challenges, and benefits. Explore reusable pipelines, continuous training, and automation in AI/ML development.
Build and Deploy Machine Learning Models on Azure ML Studio
Explore ML fundamentals with Azure, from setup to deployment. Ideal for beginners building their first model using Azure ML studio.
Why ML in Production is Still Broken
Explore challenges in deploying ML projects to production, focusing on the disconnect between development and real-world application. Learn key aspects distinguishing ML from traditional software engineering.
How to Monitor Machine Learning Stacks - Detecting and Addressing Special Issues
Discover advanced monitoring techniques for ML stacks, addressing unique challenges beyond traditional metrics to ensure robust performance and early issue detection.
Build, Train, and Deploy Models with Amazon SageMaker
Learn to build, train, and deploy machine learning models using Amazon SageMaker through hands-on experience. Gain practical skills in ML model development and implementation on AWS platform.
Validate and Monitor Your AI and Machine Learning Models
Discover techniques for validating and monitoring advanced AI and ML models, ensuring long-term performance and business adoption in various industries.
MLOps at Scale: Predicting Bus Departure Times Using 18,000 ML Models
Explore MLOps at scale through a case study on predicting bus departure times using 18,000 ML models. Learn practical insights for implementing large-scale machine learning projects.
MLOps and Automation Workshop - Bringing Machine Learning to Production in a Few Easy Steps
Learn to deploy ML/AI to production faster using MLOps technologies. Explore automation techniques and best practices for efficient machine learning operations.
Your First ML Model in Production - Considerations and Examples
Explore strategies for impactful data science projects, from identifying business objectives to deploying ML models in production. Learn real-time scoring, API integration, and lifecycle management with practical examples.
Metrics That Matter: Communicating ML Value to Stakeholders in Insurance
Explore creative strategies to gain stakeholder buy-in for ML projects in insurance, focusing on metrics that matter beyond traditional ROC scores.
Principles of Good Machine Learning Systems Design
Learn key principles for designing effective machine learning systems, from project scoping to deployment and maintenance, with insights on team structuring and the ML production ecosystem.
Machine Learning Deployment and Product Integration - Best Practices and Lessons Learned
Explore best practices for deploying ML models and integrating them into core business functions. Learn from real-world experiences in on-demand inference deployment.
Enhancing Machine Learning Applications with Wisdom of the Crowd
Explore ensemble techniques to boost ML model performance, explainability, and usability for complex problems by leveraging multiple specialized models.
Simplifying Machine Learning Lifecycle Management in Healthcare
Explore end-to-end ML lifecycle in healthcare using Azure services. Learn data ingestion, model creation, deployment, and visualization for real-time patient predictions and batch scoring.
Simplifying Machine Learning Lifecycle Management in Healthcare
Simplify ML lifecycle management in healthcare with practical insights and strategies for efficient model development, deployment, and monitoring.
Your First ML Model in Production: Examples and Considerations
Explore strategies for impactful ML projects, from identifying business objectives to deploying models in production. Learn real-time scoring, API integration, and lifecycle management.
Structuring Data Science Projects with Docker
Structure data science projects using Docker for seamless model deployment. Learn advanced techniques, explore code, address vulnerabilities, and weigh pros and cons of Docker in data science workflows.
Forecasting the Future: An Introduction to Machine Learning for Weather Prediction in Native Ruby
Explore weather prediction using machine learning in Ruby. Learn to build, train, and deploy ML models natively, discovering accessible tools and libraries for Ruby-based AI projects.