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Fine Tuning Open Source LLM

Sessions 1 day
Duration 7.5 hrs
Level Beginner
Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certificate of Achievement from Tertiary Infotech Academy Pte Ltd - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Achievement from Tertiary Infotech Academy Pte Ltd.

Fine Tuning Open Source LLM

Course Code: C922

4 Review(s) | Add Your Review

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.

No funding is available for this course

For WSQ funding, please checkout the details at WSQ - Generative AI Model Development and Fine Tuning

Course Fee

$350.00 (GST-exclusive)

$381.50 (GST-inclusive)

Mode of Training

Physical Classroom Training
Synchronised Teaching using ZOOM
Corporate Training at Company Premise

Course Date * Course Date

Course Time * Course Time: No. of Participants

Sponsorship

Self-Sponsored
Employer-Sponsored

Additional Message

* Required Fields

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

We provide free consultation related to the subject matter after the course.
Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Topic 1: Data Engineering

Topic 2: Exploratory Data Analysis

Topic 3: Modelling

Topic 4: Machine Learning Implementation and Operations

Course Info

Minimum Entry Requirement

Knowledge and Skills

Able to operate using computer functions
Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Basic Python programming is recommended

Attitude

Positive Learning Attitude
Enthusiastic Learner

Experience

Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

A Google account for Google Colab (free T4 GPU runtime) - no local installation needed
A free Hugging Face account

Hardware: Windows or Mac laptop with a modern web browser. No local GPU is required.

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

Truman Ng is a ACTA certified trainer that graduated with Bachelor Degree in Electrical Engineering from NUS in year 2002. He designed Artificial Intelligence (AI) controller for DC-DC Power Convertor by using Fuzzy Logic and Neural Network (NN) as his university Final Year Project.
Truman has over 15 years project experiences across Database & Web Design, PLC machinery, Data Center Design , Structure Cabling System(SCS) and Enterprise Network Design and Implementation. He used to be a network architect for Hewlett Packard, working with a group of virtual team from the US in handling network design and projects in the States.
Truman is the founder of Nexplore (S) Pte Ltd. He provides solutions of Cloud SaaS, IaaS & PaaS and Software Defined Network (SDN), VoIP and Internet Security. He was engaged by Huawei Global Training Center to provide 60+ consultations and trainings for Internet Service Providers(ISP) from Malaysia, Singapore, Brunei, Philippines, Australia, Poland, Iran, South Africa, Swaziland, Cote Dlvoire, Syria, Uzbekistan, New Zealand and countries over the world.As achievement, Truman has successfully completed 100+ IT network projects for Bank, Hotel and Factory within 5 years.Truman is certified in PMP, Cisco CCNP, CCIP, CCDP, HP Ase and Huawei HCNP, HCIE R&S, HCNA Cloud, HCNA Security, etc.

Review

Customer Reviews (4)

will recommend 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

. (Posted on 12/30/2021)

might consider 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

. (Posted on 12/30/2021)

Clear explanation 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

Clear explanation (Posted on 12/30/2021)

Might Consider 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

Would be good to signup for Github, docker hub and aws account before we come for the course to avoid obstacle on signup. Forward the required notes for our reading before the course started.

Will be good to go through some Ubuntu basic command needed for the class. Simply typing some of the commands without knowing what is the command is doing. Also can include comment beside each command for reference. Still very blurred after the course. Course schedule too tight from 10 to 5pm. WIll be good to start early maybe before 9am. (Posted on 11/13/2019)