Courses from 1000+ universities
Buried in Coursera’s 300-page prospectus: two failed merger attempts, competing bidders, a rogue shareholder, and a combined market cap that shrank from $3.8 billion to $1.7 billion.
600 Free Google Certifications
Communication Skills
Digital Marketing
Caregiving
Extreme Geological Events
Python and Statistics for Financial Analysis
Internet History, Technology, and Security
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Explore a wide range of free and certified Food production online courses. Find the best Food production training programs and enhance your skills today!
Explore robust ML model testing in production using MLflow, SciPy, and statsmodels. Learn key statistical tests, metrics for drift detection, and gain practical insights for maintaining model effectiveness.
Comprehensive overview of MLflow Model Serving, covering offline and online scoring, deployment options, and integration with Databricks, with practical examples and recent features.
Learn to build and deploy production-ready machine learning pipelines using Python, Databricks, and MLflow. Explore training and prediction workflows, model registration, and deployment options for both batch and on-demand processing.
Explore MLflow's latest features for productionizing machine learning, including model management, CI/CD, data schemas, and integration with PyTorch for seamless deployment and operation of ML applications.
Explore MLflow and RedisAI integration for efficient deep learning model deployment, featuring multi-framework support, auto-batching, and DAGing capabilities in a reliable runtime environment.
Explore scaling data and ML with Apache Spark and Feast, focusing on feature engineering challenges and solutions for big data-driven machine learning in production environments.
Explore sketching algorithms for efficient big data analysis, enabling fast approximate answers to complex queries and real-time processing of massive datasets in production environments.
Explore feature store implementation as an orchestration engine for ML pipeline stages using Spark and MLflow, focusing on deployment management, A/B testing, discovery, and governance.
Explore key strategies for optimizing streaming jobs in production, covering input parameters, stateful streaming, output configurations, and job modifications to ensure performance and fault tolerance.
Practical deep dive on production monitoring of machine learning models, covering standard techniques and advanced paradigms like concept drift, outlier detection, and explainability, with hands-on examples and architectural patterns.
Real-world case studies on AI failures in production, covering concept drift, A/B testing pitfalls, and systems that learn in production. Best practices for executives and technical leaders to ensure success.
Explore strategies for managing machine learning models in production, including real-time learning, model competition, and versioning, to build responsive and intelligent services.
Explore cloud migration strategies and data transformation techniques for large-scale systems, focusing on minimizing downtime and optimizing performance in enterprise environments.
Explore cloud directions, MLOps, and production data science with Prof. Hellerstein. Learn key challenges, lifecycle management, and innovative solutions for scaling data-intensive applications in the cloud.
Explore sustainable diets, nutrient deficiency, and strategies to reduce emissions in livestock farming while addressing global food security and environmental stewardship.
Get personalized course recommendations, track subjects and courses with reminders, and more.