What will you learn in Production Machine Learning Systems Course
Architect production-grade ML pipelines on GCP: design training vs. serving, data validation, and monitoring frameworks.
Handle static, dynamic, and continuous training/inference paradigms for real-world deployment scenarios.
Integrate Vertex AI and TensorFlow for scalable model management, including distributed training with custom estimators.
Manage data challenges: extraction, feature engineering, dealing with concept drift, and online vs. batch inference.
Program Overview
Module 1: Architecting Production ML Systems
⏳ ~4 hours
Topics: Core components of production ML: data ingestion, feature extraction, model lifecycle, serving, monitoring.
Hands-on: Architect a structured-data pipeline using Vertex AI.
Module 2: Designing Adaptable Systems
⏳ ~3 hours
Topics: Handling concept drift, dynamic vs. static pipelines, system robustness, error-handling strategies.
Hands-on: Lab exercise on using TensorFlow Data Validation to detect and react to data shifts.
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Job Outlook
Equips learners for the Google Cloud Professional Machine Learning Engineer role and supports prep for the associated certification.
Relevant for ML Engineer, MLOps Engineer, and data scientists working on scalable, production-level AI systems. Expertise in pipeline design and monitoring is in high demand.
Explore More Learning Paths
Enhance your expertise in deploying and managing machine learning systems with these carefully selected courses, designed to help you build, operationalize, and scale ML models in production environments.
Related Courses
Machine Learning in Production Course – Learn how to deploy machine learning models effectively and ensure their reliability in real-world systems.
Deployment of Machine Learning Models Course – Gain hands-on experience in deploying ML models to production, monitoring performance, and managing updates.
Machine Learning Classification Course – Understand classification techniques and their practical applications in production-ready ML systems.
Related Reading
What Is Python Used For – Explore Python’s role in building, training, and deploying machine learning models in production environments.
Specification: Production Machine Learning Systems Course
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