BERKELEY SCHOOL OF BUSINESS, ARTS & SCIENCES

Overview

The MLOps program develops practical expertise in deploying, automating, monitoring, and managing machine learning models in production. Participants learn ML lifecycle management, data and model versioning, CI/CD, containerization, Kubernetes, cloud deployment, model monitoring, security, governance, and responsible AI. Furthermore, the program includes a capstone project focused on enterprise AI deployment, enabling learners to build reliable, scalable, and production-ready AI solutions.

Offered By

Berkeley School of Business, Arts & Sciences

Course Focus

Machine learning deployment, model management, automation, monitoring, and scalable AI operations.

Learning Format

Hands-on modules covering practical MLOps workflows, tools, deployment techniques, and real-world AI projects.

Key Skills

Model deployment, CI/CD for ML, cloud platforms, model monitoring, data pipelines, version control, and ML automation.

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?

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 covers MLOps fundamentals and the machine learning lifecycle, focusing on model development, deployment, monitoring, and continuous improvement in enterprise AI.

MLOps Fundamentals

Machine Learning Lifecycle

MLOps Architecture

Enterprise AI Workflows

Module 2: Data Preparation and Feature Engineering

This module covers data preprocessing, feature engineering, validation, and versioning to create reliable datasets for accurate and reproducible machine learning models.

Data Preprocessing

Feature Engineering

Data Validation

Data Versioning

Module 3: Model Development and Experiment Tracking

This module covers model development, training, evaluation, versioning, experiment tracking, and hyperparameter tuning to improve machine learning quality and reproducibility.

Model Development

Experiment Tracking

Model Versioning

Hyperparameter Optimization

Module 4: CI/CD for Machine Learning

This module covers CI/CD for machine learning, focusing on automated testing, validation, deployment, and model updates through scalable MLOps pipelines.

Continuous Integration (CI)

Continuous Deployment (CD)

Automated Testing

Pipeline Automation

Module 5: Model Deployment and Containerization

This module covers machine learning model deployment using model serving, APIs, Docker, Kubernetes, and scalable deployment strategies for enterprise AI applications.

Model Deployment Strategies

Containerization with Docker

Kubernetes Orchestration

Model Serving APIs

Module 6: Cloud Platforms and MLOps Infrastructure

This module covers cloud-native MLOps infrastructure, focusing on cloud deployment, infrastructure management, storage, and scalable architectures for production machine learning systems.

Cloud MLOps Platforms

Infrastructure Management

Cloud Storage Solutions

Scalable AI Infrastructure

Module 7: Model Monitoring and Performance Management

This module covers AI governance, security, ethics, compliance, and responsible AI practices to develop trustworthy and compliant machine learning systems.

Model Performance Monitoring

Model Drift Detection

Logging and Alerting

Continuous Model Optimization

Module 8: Model Governance, Security and Responsible AI

This module covers AI governance, security, ethics, compliance, and responsible AI practices for developing trustworthy and compliant machine learning systems.

Model Governance

AI Security

Responsible AI

Compliance and Risk Management

Module 9: Scaling, Automation and MLOps Best Practices

This module covers advanced MLOps practices, including workflow automation, resource optimization, infrastructure scaling, collaboration, and operational best practices for efficient enterprise AI systems.

Workflow Automation

Infrastructure Scaling

Resource Optimization

MLOps Best Practices

Module 10: Capstone Project and Final Assessment

This final module applies MLOps skills through a capstone project, covering the design, deployment, monitoring, and optimization of a production-ready machine learning solution, followed by a technical presentation and final assessment.

End-to-End MLOps Project

Production AI Deployment

Project Presentation

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.

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

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

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.

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

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.

Visit Our Alumni

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.

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.

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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FAQ: MLOps

What is MLOps?

MLOps (Machine Learning Operations) is the practice of automating, deploying, monitoring, and managing machine learning models throughout their lifecycle. By integrating development, operations, and machine learning workflows, organizations can improve the reliability, scalability, and efficiency of AI systems. As a result, they can accelerate model deployment, streamline operations, and ensure continuous monitoring, governance, and performance optimization in production environments.

Who should enroll in this program?

This programme is ideal for data scientists, machine learning engineers, software developers, DevOps engineers, cloud professionals, AI practitioners, and IT professionals interested in production AI. Whether you are starting your career or expanding your existing expertise, the curriculum provides practical, industry-relevant skills for deploying and managing machine learning solutions. Furthermore, participants gain hands-on experience with automation, cloud platforms, model monitoring, and scalable AI workflows. As a result, they are well prepared to implement and maintain production-ready machine learning systems in modern enterprise environments.

Do I need prior machine learning experience?

Basic knowledge of programming, machine learning, or data science is helpful but not mandatory. However, the programme is designed to accommodate both beginners and experienced professionals. Through a structured learning approach, participants progressively develop the skills needed to build, deploy, monitor, and manage machine learning models in production. As a result, learners gain the confidence and practical expertise required to implement scalable AI solutions across modern enterprise environments.

What topics are covered in the program?

Will I learn cloud-based MLOps?

Does the program include practical training?

Which tools and technologies will I learn?

Is MLOps a good career choice?

What career opportunities are available after completing this program?

Is there an assessment or examination?

Will I receive a certificate after completing the program?

How does MLOps support enterprise AI?

What career opportunities are available after completing the MLOps program?

Upon successful completion of the programme, graduates can pursue roles such as MLOps Engineer, Machine Learning Engineer, AI Engineer, Data Engineer, Cloud Engineer, DevOps Engineer, AI Platform Engineer, and ML Infrastructure Engineer. Additionally, the skills gained are highly valued across industries adopting artificial intelligence, cloud computing, and data-driven technologies.