Research and Data Analysis

MLOps for Reproducible Research and Model Deployment Training Course

Introduction

In today’s fast-paced data science landscape, the demand for scalable, automated, and reproducible machine learning workflows is higher than ever. MLOps (Machine Learning Operations) bridges the gap between research and production by offering a systematic approach to deploying machine learning models with speed, scalability, and reliability. MLOps for Reproducible Research and Model Deployment Training Course is designed to empower data scientists, ML engineers, and research professionals with cutting-edge MLOps practices, tools, and frameworks to ensure reproducible research, automated workflows, and robust model deployment across diverse environments.

Whether you're working in academic research or enterprise AI, mastering MLOps is essential to boost your productivity and maintain trust in your models. By combining version control, CI/CD, containerization, model monitoring, and governance, this hands-on training ensures you're equipped with the skills needed to build, test, and deploy models seamlessly and consistently. Participants will gain practical exposure through industry-relevant case studies and labs that reflect real-world MLOps challenges and solutions.

Programme Curriculum

MLOps for Reproducible Research and Model Deployment Training Course

Introduction

Course Objectives

Understand the fundamentals of MLOps principles and lifecycle management.
Implement reproducibility best practices using Git, DVC, and MLFlow.
Build CI/CD pipelines for automated ML workflows.
Integrate data versioning into ML experiments for traceability.
Deploy machine learning models using Docker and Kubernetes.
Apply model registry systems for tracking and version control.
Monitor deployed models for drift detection and performance.
Use cloud-native MLOps tools like AWS SageMaker, Azure ML, or Google Vertex AI.
Develop model explainability and fairness reporting in production.
Apply infrastructure as code (IaC) for reproducible environments.
Ensure governance and compliance in model deployment pipelines.
Collaborate effectively using experiment tracking and reproducible research protocols.
Solve real-world challenges through domain-specific case studies in health, finance, and manufacturing.

Target Audiences

Data Scientists
Machine Learning Engineers
AI Researchers
DevOps Engineers
Software Developers transitioning to MLOps
Cloud Architects
Research Analysts
Postgraduate Students in Data Science

Course Duration: 5 days

Course Modules

Module 1: Introduction to MLOps & Reproducibility

What is MLOps? Importance and Benefits
Principles of Reproducible Research
Common Challenges in ML Projects
Introduction to ML Lifecycle Management
Tools Overview: Git, DVC, MLFlow
Case Study: Tracking Experiments in Academic Research with MLFlow

Module 2: Data Versioning & Experiment Tracking

Implementing Data Version Control (DVC)
MLFlow for Metrics & Artifact Tracking
Structuring Experiments for Reproducibility
Logging and Documentation Practices
Managing Data Pipelines at Scale
Case Study: Data Provenance in Climate Change Models

Module 3: CI/CD for Machine Learning

Introduction to CI/CD for ML Pipelines
Jenkins, GitHub Actions, GitLab CI for ML
Testing and Validation Strategies
Automating Model Retraining
Integration with Docker & Kubernetes
Case Study: Building a Continuous Deployment Pipeline for Retail Forecasting

Module 4: Containerization & Orchestration

Docker Essentials for ML Projects
Kubernetes for Scalable Model Deployment
Helm Charts and Kubernetes Operators
Environment Reproducibility using Dockerfiles
Security Considerations for Containers
Case Study: Scalable Deployment of NLP Models in Healthcare

Module 5: Model Deployment & Serving

RESTful APIs for Model Serving
TensorFlow Serving and TorchServe
Batch vs Real-Time Inference
A/B Testing and Canary Deployments
Integrating with Edge and Cloud
Case Study: Real-Time Fraud Detection System on AWS

Module 6: Model Monitoring & Drift Detection

Monitoring Tools: Prometheus, Grafana
Model Drift vs Concept Drift
Feedback Loops and Retraining
Alerting and Logging Frameworks
Model Performance Metrics in Production
Case Study: Monitoring Image Classification Models in Manufacturing

Module 7: Governance, Ethics & Compliance

Regulatory Requirements for ML Models
Model Explainability and Transparency
Bias Detection and Mitigation
Documentation and Audits
Role-Based Access and ML Security
Case Study: Explainable AI in Credit Scoring

Module 8: Cloud MLOps Tools & Final Project

Overview of AWS SageMaker, Azure ML, and Google Vertex AI
ML Infrastructure as Code with Terraform
Running Pipelines in the Cloud
Scaling ML Workloads Across Teams
Capstone Deployment Project Presentation
Case Study: End-to-End Deployment using Vertex AI for Disease Prediction

Training Methodology

Instructor-led interactive live sessions
Hands-on lab exercises with cloud-based tools
Step-by-step project-based learning
Real-world case studies and portfolio development
Group activities and peer collaboration
Assessment through quizzes and capstone project

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

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 Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

MLOps reproducibility model deployment

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000