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.
Specification: Production Machine Learning Systems Course
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