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Enhance Your Employability with Certified Skills and Courses in Singapore - WSQ , IBF-STS, Skills Certification

Building Multi-Agent AI Systems

This course is not yet approved for WSQ funding

This advanced course is designed for professionals eager to dive deep into the realm of building sophisticated multi-agent AI systems. The journey begins with an introduction to Large Language Model (LLM) AI orchestration, where participants will learn about the operational intricacies of running local LLMs, building, and debugging LLM applications. This foundation sets the stage for a detailed exploration of Retrieval-Augmented Generation (RAG) algorithms, a critical component for enhancing AI system efficiency through techniques like text embedding, similarity search, and the construction of vector databases.

As the course progresses, participants will delve into the practical aspects of implementing a multi-agent AI workflow, leveraging the ReAct agent framework to equip agents with the necessary tools and skills for complex operations. The course also provides insights into various multi-agent AI frameworks such as LangGraph, CrewAI, and AutoGen, guiding learners through the setup and execution of their first multi-agent workflow. By the end of this course, attendees will be adept at evaluating LLM AI models, analyzing RAG algorithms for improved efficiency, and assessing the feasibility of implementing multi-agent AI applications, thereby positioning themselves as experts in the field of AI development.

Learning Outcomes

By end of the course, learners should be able to:

  • LO1: Evaluate Large Language Model (LLM) AI models by identifying their strengths and limitations.
  • LO2: Analyze Retrieval-augmented generation (RAG) algorithms to improve efficiency .
  • LO3: Assess the feasibility of implementing multi-agent AI applications.

Course Brochure

TBD

Skills Framework

This course follows the guideline of Artificial Intelligence Application AER-TEM-4026-1.1 under ICT Skills Framework

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant Applications

No funding is available for this course

Course Code: TPG-2024062171

Course Booking

The course fee listed below is before subsidy/grant, if applicable. We will apply for the grant and send you the invoice with nett fee.

$900.00 (GST-exclusive)
$981.00 (GST-inclusive)

Course Date

* Required Fields

Post-Course Support

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 and get back to you asap.

Course 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.
Note the minimal class size to start a class is 3 Pax.


Course Details

Topic 1. Introduction to Large Language Model (LLM) AI Orchestration

  • Overview of LLM AI orchestration
  • Running local LLM
  • Building an LLM app
  • Debugging LLM app

Topic 2 Retrieval-augmented generation (RAG) 

  • Overview of Retrieval-augmented generation (RAG)
  • Text Embedding
  • Vector database
  • Similarity Search
  • Building an RAG

Topic 3. Implementing a Multi-Agent AI Workflow

  • Introduction to the ReAct agent framework
  • Implementing a ReAct agent
  • Equipping agent with tools and skills
  • Overview of multi-agent AI frameworks - LangGraph, CrewAI, AutoGen etc
  • Setting up and running your first multi agent workflow

Course Admin

Promotion Code

Promo or discount cannot be applied to WSQ courses

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)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Minimum Software/Hardware Requirement

Softtware: Windows / Mac

Hardware: Laptop

About Progressive Wage Model (PWM)

The Progressive Wage Model (PWM) helps to increase wages of workers through upgrading skills and improving productivity. 

Employers must ensure that their Singapore citizen and PR workers meet the PWM training requirements of attaining at least 1 Workforce Skills Qualification (WSQ) Statement of Attainment, out of the list of approved WSQ training modules.

For more information on PWM, please visit MOM site.

Funding Eligility Criteria

Individual Sponsored Trainee Employer Sponsored Trainee
  • Singapore Citizens or Singapore Permanent Residents aged 21 and above
  • From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.​
  • Trainee must pass all prescribed tests / assessments and attain 100% competency.
  • We reserves the right to claw back the funded amount from trainee if he/she did not meet the eligibility criteria.
  • Singapore Citizens or Singapore Permanent Residents who are DIRECT EMPLOYEE of the sponsoring company.
  • From 1 October 2023, attendance-taking for SkillsFuture Singapore's (SSG) funded courses must be done digitally via the Singpass App. This applies to both physical and synchronous e-learning courses.​
  • Trainee must pass all prescribed tests / assessments and attain 100% competency.
  • We reserves the right to claw back the funded amount from the employer if trainee did not meet the eligibility criteria.

 SkillsFuture Credit: 

  • Eligible Singapore Citizens can use their SkillsFuture Credit to offset course fee payable after funding.

 PSEA:

  • To check for Post-Secondary Education Account (PSEA) eligibility, goto mySkillsFuture portal and search for this course code.
  • Scroll down to "Keyword Tags" to verify for PSEA eligibility.
  • If there is “PSEA” under keyword tags, the course is eligible for PSEA.  
  • And if there is no “PSEA” under keyword tags, the course is ineligible for PSEA. 
  • Not all courses are eligible for PSEA funding.

 Absentee Payroll (AP) Funding: 

  • $4.50 per hour, capped at $100,000 per enterprise per calendar year.
  • AP funding will be computed based on the actual number of training hours attended by the trainee.

 SFEC:

  • If the Training Provider has submitted an enrolment for course fee grant claim in Training Partners Gateway (TPGateway), SSG would be able to derive SFEC funding based on this record. There is no need for enterprise to submit any claim request and the SFEC claim will be automatically generated and disbursed.
  • Where there is no such record, eligible employers are required to submit an SFEC claim after course completion via the SFEC microsite.
  • SkillsFuture Enterprise Credit (SFEC) Microsite 

Who Should Attend

  • AI Systems Architect
  • Multi-Agent Systems Developer
  • LLM AI Specialist
  • RAG Algorithm Engineer
  • AI Orchestration Consultant
  • Machine Learning Engineer
  • Data Scientist
  • AI Application Developer
  • AI Research Scientist
  • Software Developer for AI Systems
  • AI Strategy Consultant
  • Technology Innovation Manager
  • AI Project Manager
  • Natural Language Processing Engineer
  • AI Solutions Architect
  • Cloud Computing Specialist
  • AI Workflow Analyst
  • Embedded Systems Engineer
  • Robotics Engineer
  • Digital Transformation Advisor

Trainers

Apache Spark TrainerSiva Kumar is a Bigdata solution architect with 10 years of IT

Agus Salim :  I am professional with more than 10 years of experience in Project Management, IT Solutions Management, and Systems Integration both in waterfall and agile methodology. He started out his career as a Web Developer before moving on to Business Analyst/Project Manager. He has strong leadership and the capability of leading a team with a proven ability to deliver projects with tight timelines. Besides his experiences in managing projects, he has good knowledge in Cybersecurity and hands-on experience in Next Generation Firewall such as Check Point. During his free time, he likes to explore Cloud Technology, especially on Microsoft Azure. Agus Salam is AWS cloud practitioner certified. I am also ACLP certied trainer.

Quah Chee Yong : Start-up experience in the field of Data Analytics and Digital Assistant/Chatbot. Blockchain Enthusiast. Lead Trainer (ACLP) in the field of AI/Data Science. General Management experience in Sales, Marketing, Business Development, Technical Services, Shipping/Marine & Supply Chain, HSSE/Risk Management and Support Functions Combination of Commercial, Operational and Technical background in MNCs covering the Asia Pacific region Staff/Stakeholders Management in a virtual matrix organizational setup spanning diverse cultures globally. I have obtained AWS certification.

Customer Reviews (3)

Might 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
Maybe can try using more complex data, because real life data is not usually clean (Posted on 12/9/2018)
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
The module is excellent for industrial orientated applications. It is suggested to have the case study details in the optional module. (Posted on 12/9/2018)
Nil 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
Nil (Posted on 8/7/2017)

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