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BERKELEY SCHOOL OF BUSINESS, ARTS & SCIENCES

MLOps

Develop practical expertise in Machine Learning Operations by learning how to build, deploy, monitor, automate, and manage scalable machine learning models and AI workflows across cloud and enterprise environments.

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MLOps

Overview

MLOps

The MLOps program develops practical skills to deploy, automate, monitor, and manage machine learning models in production. Participants learn ML lifecycle management, model versioning, CI/CD, containerization, Kubernetes, cloud deployment, monitoring, security, governance, and responsible AI through practical projects and a capstone. Graduates gain the expertise to build reliable, scalable, and production-ready AI solutions.

Modules:

ModuleTopic
Module 1MLOps Fundamentals & ML Lifecycle
Module 2Data & Model Versioning
Module 3CI/CD for Machine Learning
Module 4Containerization & Kubernetes
Module 5Cloud Deployment
Module 6Model Monitoring & Observability
Module 7Security, Governance & Responsible AI
Module 8Capstone Project & Enterprise AI Deployment

Offered By

Berkeley School of Business, Arts & Sciences

Vision & Mission

Our vision is to develop skilled MLOps professionals who can deploy and scale reliable AI solutions. Our mission is to provide practical training in MLOps, automation, cloud platforms, CI/CD, model governance, and production AI, enabling learners to build secure, scalable, and high-performing machine learning systems.

What is the eligibility?

  • A high school qualification, diploma, bachelor’s degree, or equivalent professional experience is recommended.
  • No prior MLOps experience is required.
  • Basic knowledge of programming, machine learning, cloud computing, or IT is beneficial.
  • Suitable for data scientists, ML engineers, software developers, DevOps engineers, cloud professionals, AI practitioners, and IT professionals.

Who can do?

MLOps
anyone who is interested to learn about following concepts can pursue MLOps:
MLOps, Machine Learning Operations, Model Deployment, Machine Learning Pipelines, CI/CD for Machine Learning, Model Monitoring, Cloud MLOps, Docker, Kubernetes, AI Operations.
individuals with the following designations:
MLOps Engineer, Machine Learning Engineer, AI Engineer, Data Scientist, DevOps Engineer, Cloud Engineer, Platform Engineer, AI Solutions Architect, Data Engineer, Software Engineer, Site Reliability Engineer, AI Infrastructure Engineer, ML Platform Engineer, Technical Lead, Technology Consultant, Solutions Architect, IT Manager, AI Consultant, Cloud Architect, Digital Transformation Manager.

Course Structure

Module 1: Introduction to MLOps and Machine Learning Lifecycle

This module introduces the fundamentals of Machine Learning Operations (MLOps) and the end-to-end machine learning lifecycle. Participants learn how MLOps bridges data science, software engineering, and DevOps to automate model development, deployment, monitoring, and continuous improvement in enterprise AI environments.

Included

MLOps Fundamentals

Included

Machine Learning Lifecycle

Included

MLOps Architecture

Included

Enterprise AI Workflows

Module 2: Data Preparation and Feature Engineering

This module focuses on preparing high-quality data for machine learning models. Participants learn data preprocessing, feature engineering, data validation, and versioning techniques to build reliable datasets that improve model accuracy and reproducibility.

Included

Data Preprocessing

Included

Feature Engineering

Included

Data Validation

Included

Data Versioning

Module 3: Model Development and Experiment Tracking

This module explores the development, training, evaluation, and management of machine learning models. Participants learn experiment tracking, model versioning, hyperparameter tuning, and reproducible workflows to improve model quality and collaboration across AI teams.

Included

Model Development

Included

Experiment Tracking

Included

Model Versioning

Included

Hyperparameter Optimization

Module 4: CI/CD for Machine Learning

This module introduces Continuous Integration and Continuous Deployment (CI/CD) practices for machine learning. Participants learn how to automate model testing, validation, deployment, and updates using modern MLOps pipelines to ensure reliable, scalable, and efficient AI delivery.

Included

Continuous Integration (CI)

Included

Continuous Deployment (CD)

Included

Automated Testing

Included

Pipeline Automation

Module 5: Model Deployment and Containerization

This module focuses on deploying machine learning models into production environments using modern deployment strategies and container technologies. Participants learn model serving, API deployment, Docker, Kubernetes, and scalable deployment practices for enterprise AI applications.

Included

Model Deployment Strategies

Included

Containerization with Docker

Included

Kubernetes Orchestration

Included

Model Serving APIs

Module 6: Cloud Platforms and MLOps Infrastructure

This module introduces cloud-native MLOps infrastructure and services used to build scalable, reliable, and production-ready machine learning systems. Participants learn cloud deployment models, infrastructure management, storage solutions, and enterprise MLOps architectures across modern cloud platforms.

Included

Cloud MLOps Platforms

Included

Infrastructure Management

Included

Cloud Storage Solutions

Included

Scalable AI Infrastructure

Module 7: Model Monitoring and Performance Management

This module focuses on monitoring machine learning models in production to ensure accuracy, reliability, and business value. Participants learn performance monitoring, model drift detection, alerting, logging, and continuous optimization techniques to maintain high-performing AI systems.

Included

Model Performance Monitoring

Included

Model Drift Detection

Included

Logging and Alerting

Included

Continuous Model Optimization

Module 8: Model Governance, Security and Responsible AI

This module introduces governance, security, and responsible AI practices for production machine learning systems. Participants learn model governance frameworks, AI ethics, security controls, compliance requirements, and best practices for developing trustworthy and compliant AI solutions.

Included

Model Governance

Included

AI Security

Included

Responsible AI

Included

Compliance and Risk Management

Module 9: Scaling, Automation and MLOps Best Practices

This module explores advanced MLOps practices for scaling machine learning solutions across enterprise environments. Participants learn workflow automation, resource optimization, infrastructure scaling, collaboration strategies, and operational best practices to ensure reliable, efficient, and maintainable AI systems.

Included

Workflow Automation

Included

Infrastructure Scaling

Included

Resource Optimization

Included

MLOps Best Practices

Module 10: Capstone Project and Final Assessment

This final module enables participants to apply the knowledge and practical skills gained throughout the program by designing, deploying, monitoring, and optimizing a production-ready machine learning solution. Participants demonstrate their competency through a capstone project, technical presentation, and final assessment based on real-world MLOps scenarios.

Included

End-to-End MLOps Project

Included

Production AI Deployment

Included

Project Presentation

Included

Final Assessment

Learning Methodology

Berkeley offers expertly developed learning materials tailored to meet participants' needs, ensuring comprehensive coverage of the syllabus and optimal exam preparation.

‣ Tailored Material: Guides are designed to cover the entire syllabus, offering full preparation and deep understanding.
‣ In-Depth Content: Unlike superficial outlines, our materials provide fully developed theories and concepts, equipping participants with complete knowledge.
‣ Strategic Study: We help participants prioritize study time by indicating the weight of each topic, allowing efficient focus on crucial areas.
‣ Difficulty Levels: Topics are labeled as "Awareness" or "Proficiency," guiding participants to allocate time based on the required depth of knowledge.
‣ Comprehensive Coverage: Our materials include detailed theory and a glossary of technical terms to clarify complex concepts.
‣ Effective Learning Techniques: Visual aids and memorization techniques ensure long-lasting retention, helping candidates succeed.

Berkeley’s methodologies equip participants with the essential knowledge and tools for both exams and future success.

MLOps
Lectures

Our lecture plan integrates structured learning with interactive teaching methods, promoting engagement and collaboration. In addition, this approach ensures a comprehensive understanding of concepts, fostering critical thinking and practical application in real-world scenarios

MLOps
Practice Session

Practice sessions offer hands-on experience through guided exercises, enhancing skills and reinforcing knowledge. Moreover, this practical approach ensures mastery of concepts, promoting confidence and competence in real-world applications

MLOps
Mock Examination

Mock examinations of simulate real test conditions, providing valuable practice and assessment. In addition, this helps identify strengths and weaknesses, ensuring thorough preparation and boosting confidence for actual exams

Berkeley's Performance Standards

Evaluates and ensure the quality of the training program and all its deliverables.  This is measured through the following indicators:
‣ Instructors' experience and style in presenting and explaining topics.
‣ Variety and balance of teaching methods (such as discussions, case studies, mock exams and videos) used in the course to ensure retention and to match the learning objectives.
‣ Level of interactivity.
‣ Feedback from program participants
‣ Full compliance with Institute standards and guidelines for preparation and study requirements and methodology.
‣ Progress reports from the training program provider.

MLOps

Success Stories

“As a strong advocate for education and human development, I commend Berkeley for its exceptional commitment to empowering future leaders. The institution stands as a symbol of excellence, innovation, and opportunity. Students who walk its halls are nurtured with knowledge, values, and vision—qualities that contribute to building a stronger and more prosperous future for our nation.”- H.H. Shaikh Khalifa Al Hamid

Visit Our Alumni

Alumni Benefits

‣ Exclusive Networking Events: Access invitations to industry-leading events and thought-leadership gatherings featuring renowned speakers.


‣ Monthly Updates: Stay informed with a newsletter highlighting the latest research, events, and activities from the school.


‣ LinkedIn Community Access: Join the Executive Education LinkedIn group for networking and professional development opportunities.


‣ Educational Discounts: Enjoy a 20% discount on open-enrollment programs and access to workshops focused on emerging trends.


‣ Global Alumni Network: Connect with a diverse alumni community through the Berkeley School’s online network and engage in country and interest groups.

Is It Worth the Investment?

The MLOps (Machine Learning Operations) programme is a valuable professional qualification for AI professionals, data scientists, machine learning engineers, software developers, and cloud engineers seeking expertise in deploying, automating, monitoring, and managing production-ready machine learning systems.

UK: £60,000–£110,000+ per year
Middle East: AED 220,000–600,000+ per year
USA: USD 110,000–190,000+ per year
Asia & Africa: Competitive salaries based on experience, industry, organizational size, and responsibilities in MLOps, artificial intelligence, cloud computing, and machine learning engineering.

MLOps

Progression Route:

The MLOps (Machine Learning Operations) program provides a clear pathway to careers in artificial intelligence, machine learning, cloud computing, and enterprise AI operations. Graduates can progress into roles such as MLOps Engineer, Machine Learning Engineer, AI Engineer, DevOps Engineer, Cloud Engineer, Data Engineer, AI Platform Engineer, and Solutions Architect. The program also provides a strong foundation for advanced certifications and postgraduate studies in Artificial Intelligence, Data Science, Cloud Computing, Machine Learning, Information Technology, Executive MBA programs, MSc degrees, and doctoral qualifications including a DBA or PhD.
To expand your expertise in artificial intelligence and cloud technologies, you can also explore our Artificial Intelligence Professional Certificate and AWS Certified Cloud Practitioner programs. Furthermore, participants will gain practical knowledge based on industry best practices and technologies supported by Kubernetes, enabling them to build, deploy, monitor, and manage scalable machine learning solutions in production environments.

What You Earn

You will get a certificate of completion, which is highly reputed and accepted by employers.

MLOps

Career Opportunities

Prepare for high-demand roles such as MLOps Engineer, Machine Learning Engineer, AI Engineer, DevOps Engineer, Cloud Engineer, and AI Solutions Architect.

Professional Certification

Earn a recognized certificate that validates your practical MLOps knowledge and enhances your career prospects in artificial intelligence, cloud computing, and machine learning operations.

Cloud-Native AI

Learn to deploy, manage, and scale machine learning solutions using modern cloud infrastructure and cloud-native MLOps tools.

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