What you will learn in Machine Learning in Production Course
Design an end-to-end ML production system: project scoping, data requirements, modeling strategies, and deployment constraints.
Establish a model baseline, address concept drift, and prototype the development, deployment, and continuous improvement of a productionized ML application.
Build data pipelines by gathering, cleaning, and validating datasets.
Implement feature engineering, transformation, and selection using tools like TensorFlow Extended.
Apply best practices and progressive delivery techniques to maintain a continuously operating production system.
Program Overview
Overview of the ML Lifecycle and Deployment
⏳ 3 hours
- Introduction to ML production systems, focusing on requirements, challenges, deployment patterns, and monitoring strategies.
Modeling Challenges and Strategies
⏳ 4 hours
- Covers model strategies, error analysis, handling different data types, and addressing class imbalance and skewed datasets.
Data Definition and Baseline
⏳ 4 hours
- Focuses on working with various data types, ensuring label consistency, establishing performance baselines, and discussing improvement strategies.
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Job Outlook
Equips learners with practical skills for roles such as ML Engineer, Data Scientist, and AI Specialist.
Provides hands-on experience in deploying and maintaining ML systems in production environments.
Enhances qualifications for positions requiring expertise in MLOps and production-level machine learning applications.
Specification: Machine Learning in Production
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