Machine Learning Engineering
Implement and deploy machine learning models in production environments at scale with comprehensive training in MLOps practices, model pipelines, and deployment strategies.
Course Overview
This course covers feature engineering, model training pipelines, and deployment strategies using MLOps practices. Students learn TensorFlow and PyTorch frameworks, model optimization techniques, and A/B testing for machine learning systems in production environments.
The program addresses model versioning, monitoring drift, and explainable AI requirements. Participants work with cloud ML platforms, implement recommendation systems, and build computer vision applications. The curriculum emphasizes practical deployment concerns faced by engineering teams.
Designed for engineers transitioning to machine learning roles, this course focuses on production deployment rather than theoretical research. Projects include building fraud detection systems, implementing natural language processing pipelines, and deploying real-time prediction services that handle significant traffic.
Career Development Outcomes
Graduates transition into machine learning engineering positions at companies deploying AI systems in production. The practical focus on deployment and monitoring prepares students for roles maintaining and improving operational ML systems.
Production ML Portfolio
Build systems demonstrating end-to-end ML pipelines including data processing, model training, deployment, and monitoring. Projects show your ability to operate ML systems at scale.
MLOps Proficiency
Develop expertise in model versioning, experiment tracking, automated retraining, and deployment automation. Learn practices that bridge machine learning research and production systems.
Framework Mastery
Gain hands-on experience with TensorFlow and PyTorch, understanding when to use each framework. Learn optimization techniques for inference latency and model size reduction.
Cross-Functional Skills
Develop abilities to communicate technical decisions to stakeholders, estimate project timelines, and collaborate with data scientists and software engineers on ML systems.
Technologies & Development Stack
Work with industry-standard frameworks and deployment platforms used by ML teams. Learn tools for experiment tracking, model serving, and production monitoring employed at technology companies.
Core Technologies
- TensorFlow and PyTorch frameworks
- Python data science stack (NumPy, Pandas, Scikit-learn)
- MLflow for experiment tracking
- Docker for containerization
- Cloud ML platforms and APIs
Development Practices
- Feature engineering and data preprocessing
- Model versioning and experiment tracking
- A/B testing and online evaluation
- Monitoring for model drift and performance
- Model optimization and quantization
Production Deployment & Operational Standards
Deploying machine learning models requires addressing concerns beyond model accuracy. The course emphasizes latency requirements, monitoring strategies, and handling model degradation in production environments.
Model Serving Architecture
Implement model serving solutions considering throughput, latency, and resource constraints. Learn batch prediction patterns, real-time inference APIs, and edge deployment strategies for different use cases.
Monitoring and Observability
Establish monitoring for model performance, data quality, and system health. Detect distribution shift, track prediction metrics, and implement alerting for degraded model behavior.
Continuous Training Pipelines
Design automated retraining workflows triggered by performance metrics or data volume thresholds. Implement validation gates and gradual rollout procedures for model updates.
Designed For
This course serves software engineers and data analysts transitioning into machine learning engineering roles. Prerequisites include programming proficiency in Python, understanding of basic statistics, and familiarity with software development practices. Prior machine learning coursework is helpful but not required.
Software Engineers
Developers seeking to work on ML systems and AI products. Those wanting to understand how to integrate and deploy machine learning models in production applications.
Data Analysts
Analysts with Python experience looking to expand into predictive modeling and ML systems. Those wanting to move beyond exploratory analysis into production systems.
Backend Engineers
Engineers building data-intensive applications seeking ML capabilities. Those responsible for implementing recommendation systems, search ranking, or personalization features.
Technical Transitioners
Professionals from technical backgrounds entering machine learning field. Those seeking practical deployment skills rather than theoretical research focus.
Progress Assessment & Evaluation
Assessment focuses on implementing end-to-end ML systems from data processing through deployment. Each module includes assignments building toward complete production ML pipelines demonstrating practical engineering skills.
Module Assignments
Complete exercises covering data preprocessing, model training, evaluation metrics, and deployment preparation. Assignments emphasize practical considerations like handling imbalanced data, feature engineering, and model selection for specific problems.
Production Projects
Build three complete systems: a fraud detection model with real-time inference, a natural language processing pipeline for text classification, and a recommendation system with A/B testing capabilities. Projects demonstrate your ability to deploy and monitor ML systems handling production workloads.
System Design Reviews
Present ML system architectures explaining design decisions, trade-offs, and operational considerations. Receive feedback on scalability approaches, monitoring strategies, and deployment patterns.
Start Building Production ML Systems
Connect with our program advisors to discuss enrollment procedures, course schedule, and technical prerequisites. We can answer questions about curriculum content and help determine if this course aligns with your professional objectives.
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