AI Systems: From Models to Production Deployment.
What does this program include?
Learning Path
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Model Serving Infrastructure
TensorFlow Serving configuration, REST and gRPC APIs, batch prediction systems, and caching strategies for frequently requested inferences.
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MLOps Pipeline Design
CI/CD for machine learning, automated testing for models, data validation, feature store architecture, and experiment tracking systems.
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Monitoring and Observability
Performance metrics, data quality checks, model drift detection, alerting systems, and debugging techniques for production ML issues.
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Scalability Patterns
Horizontal scaling, load balancing, asynchronous processing, GPU resource management, and cost optimization for inference workloads.
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Model Governance
Version control for models and datasets, reproducibility requirements, audit trails, and compliance considerations for regulated industries.
- Capstone Project
- Design and deploy a complete ML system including data pipeline, model serving, monitoring dashboard, and automated retraining workflow.
Why should you join this program?
Training a model is one thing. Running it reliably in production is entirely different. This program focuses on the engineering challenges that emerge when AI moves from notebooks to live systems serving thousands of requests daily.
You will work with TensorFlow Serving, MLflow, and Kubernetes to build deployment pipelines. The curriculum covers data versioning, model monitoring, A/B testing frameworks, and strategies for handling concept drift. Each week includes debugging exercises based on real production failures.
Technical Requirements
You need Python experience and basic understanding of machine learning concepts. The program assumes you have trained models before, even simple ones, and want to learn the infrastructure side. We use Docker extensively, so container familiarity helps but is not required.
Projects include deploying a recommendation engine, building a real-time image classification API, and implementing automated retraining pipelines. You will also learn cost optimization techniques for cloud-based inference and strategies for serving models at different latency requirements.
Ready to start your learning journey?
Enroll now and gain access to structured content, expert guidance, and a supportive community. Each program is designed to help you build practical skills and apply them in real scenarios.
Enroll in program