English | Size: 611 MB
Genre: eLearning
The Real-World Machine Learning Tutorial, Build a real-world ML system from A to Z. Learn how to design, develop, and deploy end-to-end ML products that help top startups and enterprises solve their business problems. This comprehensive tutorial covers the complete journey from understanding business problems to deploying ML solutions using MLOps best practices. You’ll master the 4 essential steps that transform any business problem into an ML solution: translating business problems into Machine Learning problems, preparing data and building robust pipelines, model prototyping with experimentation and training, and model operationalization with deployment and monitoring.
What you’ll learn
Learn to translate business problems into Machine Learning solutions
Build clean and robust data pipelines with Python to fetch, validate, transform and generate training data
Create quick and powerful model baselines with feature engineering and boosting trees
Optimize model hyper-parameters to maximize performance
Transform ML model prototypes into fully working real-world batch-scoring systems
Deploy ML solutions using Feature Store and GitHub actions
Build frontend dashboards to show live predictions
Implement monitoring systems for ML models
Master the 4 steps from business problem to ML solution
Apply MLOps best practices for deployment and monitoring
Who this course is for
You’ve trained an ML model before and know how to prepare data and train a model in a notebook
You want to learn how to design, implement and deploy a Machine Learning solution from A to Z
You want to master the 4 steps that transform any business problem into an ML solution
You want to land an ML Engineer job and stand out from the competition
You don’t know how to turn ML prototypes into complete ML services
You want to go one step further in your ML career
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