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AI Devops with Jenkins

Course Information

  • Sessions 1 day
  • Duration 7.5 hrs
  • Level Beginner
  • Assessment NA

Venue

Course Brochure

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Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.

Fine Tuning Open Source LLM

Course Code: C922

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What's This Course About

In the era of advanced artificial intelligence, the ability to develop and fine-tune Generative AI (GenAI) models is critical for building high-performance, domain-specific solutions. This course, Fine Tuning Open Source LLM, equips learners with the practical skills and technical knowledge to design, optimize, and evaluate modern AI models using cloud-based tools and frameworks.

Learners will begin by exploring techniques for data ingestion and transformation, including the use of synthetic data to enhance model performance and address data limitations. The course then focuses on building efficient data pipelines and feature engineering workflows, applying optimization strategies to improve model training and scalability.

Participants will gain hands-on experience in fine-tuning pre-trained multi-modal models, leveraging advanced training approaches, loss functions, and parameter optimization techniques to adapt models for specific use cases. Emphasis is placed on improving model accuracy, efficiency, and robustness in real-world deployment scenarios.

In addition, the course addresses critical considerations in modern AI development, including bias detection, explainability, and alignment with performance benchmarks. Learners will evaluate AI solutions to ensure they are reliable, ethical, and aligned with organizational and regulatory expectations.

By the end of the course, learners will be able to design end-to-end GenAI workflows—from data preparation to model fine-tuning and evaluation—enabling them to develop scalable, responsible, and high-performing AI solutions for a wide range of applications.

This course is suitable for data professionals, AI practitioners, and developers seeking to deepen their expertise in Generative AI model development, optimization, and fine-tuning techniques.

Why Take This Course?

  • Hands-On Learning Learn by doing. Every topic is practised hands-on, so you leave with skills you can use at work straight away.
  • Live Coaching Experienced instructors coach you live, answer your questions and guide you step by step.
  • Free Post-Course Consultation Stuck after class? Message your trainer any time for guidance as you apply what you have learned.
  • Always-Updated Materials Materials are refreshed to stay relevant. Log in to our LMS any time to get the latest version.

Course Fee

₵1,500.00

Course Date

Course Time

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to info@tertiarycourses.com.gh and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

Topic 1: Data Engineering

Topic 2: Exploratory Data Analysis

Topic 3: Modelling

Topic 4: Machine Learning Implementation and Operations

Course Info

Prerequisite

Basic computer and file-management skills are expected. Familiarity with basic command-line, Linux and networking concepts will help you follow the exercises.

Software Requirement

Install and test the listed software before class. Confirm the required version and account or licence access in your joining instructions. Cloud and AI services may charge subscription or usage fees.

Also prepare the tools needed for the practical exercises:

Confirm the version, account permissions and any API usage budget with the trainer before class.

Hardware Requirement

Bring a laptop that can run the trainer-specified lab environment. For virtual-machine or container labs, 16 GB RAM is recommended, with hardware virtualisation enabled where required.

Bring your own computer and charger, with a reliable internet connection and permission to install the required software. For live online classes, also prepare a microphone or headset.

If you need help arranging a suitable device, contact us before the class to confirm availability.

Job Roles

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • AI Engineer
  • Data Engineer
  • Cloud Machine Learning Architect
  • ML Operations Engineer
  • AI/ML Consultant
  • Applied Scientist
  • Deep Learning Engineer
  • Cloud Solutions Architect
  • Data Analyst
  • DevOps Engineer (ML-focused)
  • Technical Product Manager (AI/ML)
  • AI Research Engineer
  • Software Engineer (ML Integration)
  • Automation Engineer (AI/ML)

Trainers

Trainers

Dr. Muhammed Siraj is a cybersecurity specialist and academic with 15 years of experience, serving as Senior Lecturer in the Department of Informatics and Director of Graduate and Continuous Education at Academic City University in Accra. He holds a PhD in Security in Computing from Universiti Putra Malaysia, where his research focused on blockchain security, an MSc in Information and Communication Technology from Aalborg University in Denmark, and a degree from the University of Ghana. He is a licensed independent assessor with Ghana's Cyber Security Authority, holds leadership roles in national cybersecurity and privacy professional bodies, and is certified as a Certified Ethical Hacker (CEH), Computer Hacking Forensic Investigator (CHFI) and Certified Data Protection Supervisor (CDPS). Before joining Academic City, he served as Acting Director of ICT and Data Protection Supervisor at Wisconsin International University College. His expertise spans penetration testing, digital forensics, incident response, malware analysis, cryptography, cloud and IoT security, zero trust architecture, cyber threat intelligence, data protection, and governance, risk and compliance.

Dr. Siraj's research interests cover blockchain, cybersecurity, distributed computing, consensus protocols and machine learning, with published work on blockchain consensus security, smart-contract privacy preservation for sensitive records in the cloud, and new consensus mechanisms. This background gives him a distinctive perspective on artificial intelligence: as organisations adopt AI tools, agents and data-driven automation, he helps learners understand how to protect the data these systems depend on, recognise new attack surfaces, and put sound governance, privacy and access controls in place so that AI can be used safely and responsibly. In the classroom he connects technical practice with regulation, privacy and human factors, drawing on real-world security and data-protection cases to show how concepts apply in professional settings, and his experiential, hands-on teaching style has made him a trusted trainer for students and working professionals alike.

Automated build and deployment pipelines keep Dr. Alfred Ang's many projects shipping, and that experience informs this Jenkins course. Dr. Ang is a full-stack AI developer, Agentic AI solution architect and corporate trainer, and serves as Chief Technology Officer of Tertiary Infotech Academy Pte Ltd in Singapore, the training organisation behind Tertiary Courses. The course-registration platform Dr. Ang built for the Tertiary Courses websites in Singapore, Malaysia and Ghana is packaged with Docker and redeployed automatically through Coolify whenever code is pushed, with database migrations applied as each container starts and the previous version kept running if anything fails. Dr. Ang has also set up continuous delivery for native iOS apps that builds, signs, uploads and submits each release to the App Store automatically using GitHub Actions. A training environment platform launches Docker-based WordPress, Ubuntu and Kali Linux labs on demand, and a public collection of Claude Code skills includes tools for pushing images to Docker Hub and scanning code for leaked secrets before it is committed. Jenkins is not the pipeline tool used in Dr. Ang's public portfolio, but the stages, credentials, containers and safety checks are the same ideas Jenkins pipelines express, and the course builds on that common ground. Dr. Ang works end to end, from front end and back end to databases, cloud deployment and native mobile apps, using AI coding assistants extensively, and shares this work across more than 300 public repositories on GitHub.

AI Devops with Jenkins brings those pipeline lessons together with AI-assisted configuration. Dr. Ang begins with the principles of continuous integration and continuous delivery, how Jenkins fits into them and what AI-assisted DevOps looks like in practice. Learners set up Jenkins alongside AI coding assistants such as Cursor, GitHub Copilot and Claude, then explore jobs, pipelines and the Jenkinsfile, using AI to generate pipeline code and to explain each stage in plain language. The second topic builds real CI/CD pipelines. Learners compare declarative and scripted pipelines, integrate build, test and deployment stages, and work with Docker, Jenkins agents and credentials. Dr. Ang places strong emphasis on reviewing, securing and optimising AI-generated configuration, because pipelines hold powerful credentials and a small mistake can leak secrets or deploy broken code. Lessons from running automated deployments are shared throughout: keeping credentials out of source control, failing fast when tests break, making every stage observable and ensuring there is always a safe way back to the last working release. Teaching is hands-on and incremental, with each pipeline stage generated, run, inspected and improved before the next is added, so learners understand what the AI has written rather than copying it blindly. By the end of the course, every learner has a working pipeline that builds, tests and deploys an application, plus a set of prompts for extending it. To see the containerised and automated projects behind this course, visit https://github.com/alfredang

Review

Customer Reviews (5)

Highly recommended Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Well organised course with clear objectives. The step-by-step approach made it easy to follow. (Posted on 1/15/2026)
Hands-on and practical Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Well organised course with clear objectives. The step-by-step approach made it easy to follow. (Posted on 5/17/2025)
Very informative Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
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I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 1/4/2025)
Fantastic experience Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 4/12/2024)
Engaging trainer Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The trainer is an expert in the subject and shared many practical insights from real projects. (Posted on 1/20/2024)

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