MLOps Engineering and Machine Learning Deployment Training Course

 MLOps Engineering and Machine Learning Deployment Training Course 

Introduction 

MLOps Engineering and Machine Learning Deployment Training Course is designed to equip participants with advanced knowledge and technical skills for developing, deploying, automating, monitoring, and maintaining machine learning models in production environments. The course explores the integration of Machine Learning Operations (MLOps), DevOps, Data Engineering, Artificial Intelligence (AI), and cloud computing to establish scalable, reliable, and efficient machine learning workflows. Participants gain practical experience in machine learning lifecycle management, model versioning, automated machine learning pipelines, Continuous Integration and Continuous Deployment (CI/CD), model orchestration, containerization, Kubernetes, and cloud-native deployment. The training emphasizes modern MLOps frameworks, including MLflow, Kubeflow, TensorFlow Extended (TFX), Docker, Kubernetes, Apache Airflow, and cloud-based machine learning platforms. 

The course also examines enterprise machine learning infrastructure, model governance, automated testing, performance optimization, model observability, and production monitoring. Participants explore strategies for managing model drift, data quality, reproducibility, scalability, security, and responsible AI throughout the machine learning lifecycle. Through practical exercises and international industry case studies, participants develop the capabilities to transform experimental machine learning solutions into production-ready applications. The programme is relevant to organizations pursuing AI transformation, intelligent automation, predictive analytics, and data-driven decision-making across financial services, healthcare, telecommunications, manufacturing, logistics, and public sector institutions. 

Course Objectives 

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

1. Understand MLOps architecture and enterprise machine learning lifecycle management.
2. Design scalable machine learning deployment pipelines.
3. Implement CI/CD and Continuous Training (CT) workflows.
4. Apply MLflow for experiment tracking and model registry management.
5. Deploy machine learning models using Docker and Kubernetes.
6. Build automated machine learning pipelines using Kubeflow and TFX.
7. Implement cloud-based ML deployment using AWS, Azure, and Google Cloud.
8. Apply model monitoring, observability, and performance optimization.
9. Identify and mitigate data drift and model drift.
10. Implement automated model testing and validation frameworks.
11. Strengthen machine learning security and governance.
12. Manage model versioning, reproducibility, and rollback strategies.
13. Develop scalable production-ready AI and machine learning solutions.

Organizational Benefits
 

1. Accelerates enterprise AI adoption and digital transformation.
2. Reduces machine learning deployment costs and operational delays.
3. Improves model reliability, scalability, and reproducibility.
4. Strengthens automated machine learning lifecycle management.
5. Enhances collaboration between Data Science and Engineering teams.
6. Minimizes production failures through continuous monitoring.
7. Improves predictive analytics and business intelligence capabilities.
8. Strengthens AI governance, security, and regulatory compliance.
9. Enables faster innovation through automated deployment pipelines.
10. Maximizes returns on enterprise machine learning investments.

Target Audience
 

1. Machine Learning Engineers.
2. Data Scientists and Data Analysts.
3. MLOps Engineers and DevOps Professionals.
4. AI and Machine Learning Architects.
5. Cloud Engineers and Solutions Architects.
6. Data Engineers and Platform Engineers.
7. IT Managers and Technology Consultants.
8. Research Scientists and AI Project Managers.

Course Duration: 5 days

Course Modules

Module 1: Introduction to MLOps Engineering
 

• MLOps fundamentals, principles, architecture, and business applications.
• Machine learning lifecycle management and operational workflows.
• Integration of Data Science, DevOps, and Data Engineering.
• MLOps maturity models and enterprise implementation strategies.
• Machine learning reproducibility and automation.
• Case study: Netflix's machine learning infrastructure and production model operations.

Module 2: Machine Learning Pipeline Development
 

• Designing automated end-to-end machine learning pipelines.
• Data ingestion, preprocessing, feature engineering, and validation.
• Workflow orchestration using Apache Airflow.
• Pipeline automation using Kubeflow and TensorFlow Extended.
• Pipeline testing, scheduling, and dependency management.
• Case study: Uber's Michelangelo platform for machine learning workflows.

Module 3: Experiment Tracking and Model Management
 

• Experiment tracking and performance evaluation using MLflow.
• Model registry, version control, and artifact management.
• Reproducible machine learning experiments.
• Model packaging, metadata tracking, and lineage management.
• Model approval, staging, and production promotion.
• Case study: Spotify's machine learning experimentation and model management practices.

Module 4: Machine Learning Deployment and Containerization
 

• Deploying models through REST APIs and microservices.
• Containerizing machine learning applications using Docker.
• Kubernetes orchestration and scalable model serving.
• Batch inference, real-time inference, and serverless deployment.
• Model integration with enterprise applications and databases.
• Case study: Airbnb's machine learning deployment and scalable prediction systems.

Module 5: CI/CD Automation and Cloud-Based MLOps
 

• Implementing Continuous Integration and Continuous Deployment pipelines.
• Continuous Training (CT) and automated model retraining.
• Git-based workflows and infrastructure automation.
• Deploying models using AWS SageMaker, Azure Machine Learning, and Google Vertex AI.
• Automated testing, release management, and rollback strategies.
• Case study: Google Cloud's Vertex AI platform for enterprise machine learning deployment.

Module 6: Model Monitoring, Observability, and Optimization
 

• Production model monitoring and performance evaluation.
• Detecting data drift, concept drift, and model degradation.
• Model observability, logging, and alerting.
• Resource utilization, inference latency, and cost optimization.
• Automated retraining and model performance improvement.
• Case study: Evidently AI-based monitoring of production machine learning systems.

Module 7: Machine Learning Security, Governance, and Responsible AI
 

• Machine learning security and access control frameworks.
• Data privacy, encryption, and secure model deployment.
• Model governance, auditability, and regulatory compliance.
• Responsible AI, explainability, and bias detection.
• Model risk management and lifecycle documentation.
• Case study: Microsoft's Responsible AI governance and enterprise machine learning practices.

Module 8: Advanced MLOps Implementation and Capstone Project
 

• Designing enterprise-grade MLOps reference architectures.
• Implementing end-to-end automated deployment workflows.
• Scaling machine learning applications across cloud environments.
• Integrating model monitoring, governance, and automated retraining.
• Capstone project: Building and deploying a production-ready predictive machine learning application.
• Case study: Amazon's recommendation systems and large-scale machine learning deployment.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using industry-standard MLOps tools.
• Hands-on machine learning pipeline development exercises.
• Docker containerization and Kubernetes deployment simulations.
• Real-world international case studies and group assignments.
• Capstone projects, technical assessments, and knowledge-sharing sessions.

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.