Design, deploy, and operate production-ready machine learning systems on Google Cloud.
Moving a machine learning model from experimentation to production requires more than model accuracy.
This course focuses on building scalable, reliable, and maintainable ML systems by implementing production training pipelines, distributed model training, inference services, and operational workflows using Google Cloud and TensorFlow.
- Why get trained: Gain practical experience implementing static, dynamic, and continuous training pipelines, deploying batch and online inference, configuring distributed TensorFlow workloads, and applying production practices for scalable machine learning systems.
- Why it matters: Production ML systems must support continuous model updates, reliable inference, performance monitoring, and operational scalability. Machine learning engineers who understand production architectures can reduce deployment risk, improve model reliability, and support AI applications running in enterprise environments.
- Who should attend: Machine Learning Engineers, Data Scientists, AI Engineers, Cloud Architects, MLOps Engineers, Data Engineers, software developers building ML applications, and professionals preparing to deploy machine learning workloads on Google Cloud.
Apply production engineering practices that help machine learning models move from development into reliable, scalable, and maintainable production environments. HRD Corp Claimable.
Overview
Dive into the components and best practices of building high-performing ML systems in production environments.
This course covers how to implement various flavors of production ML systems, including:
- Static, dynamic, and continuous training
- Static and dynamic inference
- Batch and online processing
You will delve into TensorFlow abstraction levels, explore options for distributed training, and learn how to write distributed training models using custom estimators.
Skills Covered
Upon completion of this course, learners will be able to:
- Differentiate between static, dynamic, and continuous training pipelines.
- Implement static and dynamic inference for production models.
- Choose between batch and online processing based on use case requirements.
- Navigate TensorFlow abstraction levels (from high-level Keras to low-level custom ops).
- Set up and manage distributed training jobs on Google Cloud.
- Write distributed training models using custom estimators.
- Apply best practices for productionizing ML systems.
Prerequisites
- Completion of Course 1 in the Advanced Machine Learning on Google Cloud series (recommended)
- Working knowledge of TensorFlow (including Keras)
- Familiarity with Python and basic cloud concepts
Target Audience
- Cloud Architects designing ML pipelines
- Intermediate Machine Learning Engineers
- Data Scientists moving models to production
- Learners who have completed the first course in the Advanced ML on Google Cloud series
Module 1: Introduction to Production ML Systems
- Production challenges
- Static vs. dynamic vs. continuous training
- Batch vs. online processing
Module 2: Inference in Production
- Static inference (precomputed)
- Dynamic inference (on-demand)
- Latency and throughput considerations
Module 3: TensorFlow Abstraction Layers
- TF 2.x ecosystem
- Â Estimators, Keras, and custom loops
- When to use which abstraction
Module 4: Distributed Training Fundamentals
- Why distribute training
- Data parallelism vs. model parallelism
- MirroredStrategy, TPUStrategy, MultiWorkerMirroredStrategy
Module 5:Â Custom Estimators for Distributed Training
- Writing custom estimators
- Model functions and input functions
- Lab: Distributed training with custom estimators
Module 6:Â Production Pipeline Architecture
- Continuous training pipelines
- Model versioning and rollback
- Monitoring and alerting
Module 7:Â Challenge Lab (Skills Badge)
- Jump directly to a challenge lab
- Demonstrate production ML skills without completing all modules
Dates & Locations
September 8, 2026 - September 9, 2026
September 8, 2026 - September 9, 2026
November 3, 2026 - November 4, 2026
November 3, 2026 - November 4, 2026
Exam & Certification
Training & Certification Guide
Frequently Asked Questions
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All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631
