Model Deployment and Production Engineering Training Course

 Model Deployment and Production Engineering Training Course 

Introduction 

Model Deployment and Production Engineering Training Course is designed to equip professionals with the technical expertise and practical skills required to transform machine learning models into reliable, scalable, secure, and production-ready solutions. As organisations increasingly adopt artificial intelligence (AI), machine learning (ML), deep learning, and data-driven technologies, the ability to deploy, manage, monitor, and maintain models in real-world environments has become a critical business capability. This course explores modern Machine Learning Operations (MLOps), model serving, containerisation, cloud-native infrastructure, automated machine learning pipelines, Continuous Integration and Continuous Deployment (CI/CD), model versioning, and production engineering. Participants will gain practical knowledge of deploying models using platforms such as Kubernetes, Docker, MLflow, Kubeflow, Amazon SageMaker, Microsoft Azure Machine Learning, and Google Vertex AI. 

The training also examines advanced production engineering practices, including model performance optimisation, inference scaling, automated testing, model observability, data drift detection, model governance, cybersecurity, and lifecycle management. Through practical exercises and international case studies, participants will understand how leading organisations operationalise artificial intelligence while maintaining reliability, cost efficiency, scalability, and regulatory compliance. The course emphasises real-world implementation strategies for building resilient AI systems, managing production workloads, automating deployment workflows, and improving model performance across cloud, hybrid, and on-premises environments. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand machine learning deployment architectures and production engineering principles.
2. Develop scalable MLOps workflows and automated model deployment pipelines.
3. Implement CI/CD automation for machine learning applications.
4. Apply Docker containerisation and Kubernetes orchestration.
5. Configure model serving, REST APIs, and inference endpoints.
6. Implement model versioning, experiment tracking, and lifecycle management.
7. Apply cloud-based machine learning deployment using AWS, Azure, and Google Cloud.
8. Establish model monitoring, observability, and performance optimisation frameworks.
9. Identify data drift, model drift, and production reliability challenges.
10. Implement automated testing and quality assurance for machine learning systems.
11. Strengthen AI security, governance, and production compliance.
12. Optimise inference latency, throughput, and computational resources.
13. Design resilient, scalable, and cost-efficient production machine learning infrastructure.

Organizational Benefits
 

1. Improved efficiency in deploying and managing machine learning models.
2. Reduced operational costs through automated MLOps workflows.
3. Faster delivery of AI-powered products and services.
4. Improved model reliability and production performance.
5. Stronger data governance and AI security practices.
6. Reduced deployment errors through automated testing.
7. Enhanced scalability of enterprise AI infrastructure.
8. Improved collaboration between data science and engineering teams.
9. Better identification of model drift and performance degradation.
10. Increased organisational competitiveness through production-ready AI innovation.

Target Audiences
 

1. Machine Learning Engineers.
2. Data Scientists and AI Researchers.
3. MLOps Engineers and Specialists.
4. DevOps and Platform Engineers.
5. Cloud Solutions Architects.
6. Data Engineers and Software Developers.
7. AI Product Managers and Technical Leads.
8. IT Infrastructure and Enterprise Technology Managers.

Course Duration: 5 days
 
Course Modules

Module 1: Foundations of Model Deployment and Production Engineering
 

• Principles of machine learning deployment and production engineering.
• Differences between model development, staging, and production environments.
• Production ML architectures, deployment strategies, and infrastructure requirements.
• Model lifecycle management, reproducibility, and operational readiness.
• Introduction to MLOps frameworks and enterprise deployment standards.
• Case study: Netflix's machine learning infrastructure for large-scale personalised recommendations.

Module 2: Machine Learning Pipelines and MLOps Automation
 

• Designing automated machine learning pipelines and workflows.
• Implementing experiment tracking using MLflow and Kubeflow.
• Model versioning, artifact management, and reproducibility.
• Automating training, validation, and deployment processes.
• Integrating CI/CD pipelines into machine learning workflows.
• Case study: Uber's Michelangelo platform for managing machine learning workflows at scale.

Module 3: Containerisation and Infrastructure Orchestration
 

• Docker containerisation for machine learning applications.
• Kubernetes architecture, clusters, pods, and deployment configurations.
• Managing dependencies, environment variables, and container registries.
• Implementing scalable infrastructure and workload orchestration.
• Deploying machine learning services using cloud-native technologies.
• Case study: Spotify's containerised infrastructure and large-scale engineering practices.

Module 4: Model Serving, APIs, and Inference Engineering
 

• Deploying machine learning models through REST APIs and endpoints.
• Implementing batch inference and real-time model serving.
• Model serving frameworks including TensorFlow Serving and TorchServe.
• Optimising inference latency, throughput, and resource utilisation.
• Implementing load balancing, autoscaling, and distributed inference.
• Case study: Google Search's large-scale machine learning serving infrastructure.

Module 5: Cloud-Based Model Deployment and Production Infrastructure
 

• Deploying models using Amazon SageMaker and AWS infrastructure.
• Implementing Microsoft Azure Machine Learning deployment services.
• Managing machine learning workloads using Google Vertex AI.
• Configuring cloud storage, compute resources, networking, and access controls.
• Implementing hybrid cloud and multi-cloud deployment architectures.
• Case study: Airbnb's adoption of cloud-based machine learning infrastructure for business intelligence and personalisation.

Module 6: Model Monitoring, Observability, and Reliability Engineering
 

• Implementing production model monitoring and observability.
• Detecting data drift, concept drift, and model performance degradation.
• Monitoring accuracy, precision, recall, latency, and throughput.
• Establishing logging, alerting, and incident response mechanisms.
• Applying reliability engineering and service-level objectives.
• Case study: Microsoft's Azure Machine Learning monitoring and model management practices.

Module 7: Production Security, Governance, and Quality Assurance
 

• Implementing machine learning testing and validation frameworks.
• Managing model security, access controls, and sensitive data.
• Applying responsible AI principles and production governance.
• Establishing audit trails, compliance, and model documentation.
• Implementing rollback strategies, disaster recovery, and risk management.
• Case study: JPMorgan Chase's model risk management and governance practices.

Module 8: Advanced Production Optimisation and Continuous Improvement
 

• Optimising model inference through quantisation and hardware acceleration.
• Implementing GPU utilisation and distributed computing strategies.
• Applying automated retraining and continuous model improvement.
• Managing production costs, scalability, and infrastructure efficiency.
• Developing enterprise-ready model deployment and operational strategies.
• Case study: Amazon's recommendation systems and continuous optimisation of large-scale production machine learning services.

Training Methodology
 

• Instructor-led presentations covering model deployment and production engineering concepts.
• Practical demonstrations using Docker, Kubernetes, MLflow, and cloud ML platforms.
• Hands-on exercises involving model packaging, deployment, monitoring, and optimisation.
• Group discussions exploring real-world MLOps challenges and international case studies.
• Practical projects involving automated pipelines, CI/CD, and production model serving.
• Case-based assessments, technical problem-solving, and participant presentations.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.