Building Agentic AI Systems with Open-Source Models Course

The course Building Agentic AI Systems with Open-Source Models is intended to enable learners to understand how to design an intelligent, autonomous agent that decides, learns, and adapts interactively. This end-to-end solution provides learning on the architecture, systems, tools, and frameworks which can be leveraged to implement agentic AI systems using open-source solutions. Attendees will also analyze the convergence of Large Language Models (LLMs), reinforcement learning techniques, and natural language processing (NLP) into techniques for constructing perceiving, reasoning, and acting systems in dynamic settings.

In the course, Learners will work with leading open-source AI frameworks, develop multi-agent collaboration, and apply these concepts to practical applications, including automation, data analysis, and human-AI interaction. Participants who complete this course will be able to build and deploy scalable, high- performance, and ethical AI systems that meet the demands of today’s enterprises.  

SSDN Technologies, a premier Training company in India, has developed this course specifically for professionals and to help organizations adapt and prepare for a fast-changing AI landscape. With knowledgeable trainers and experiential learning focus, participants will learn to build strong agentic AI systems utilizing open-source models. 


Download Content
bannerImg

Learning Options for You

  • Live Training (Duration : 48 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

  • Basic knowledge of Python programming and machine learning
  • Familiarity with APIs, AI frameworks, and neural network concepts
  • Understanding of prompt engineering is an added advantage

Learning Objectives

This course offers an in-depth exploration of building agentic AI systems using open-source models like Llama, Mistral, and Falcon. Learners will discover how to create autonomous agents capable of reasoning, planning, and interacting with APIs and tools. The curriculum includes multi-agent orchestration, memory management, contextual reasoning, and evaluation of agent performance. Participants will also work with open-source frameworks such as LangChain, AutoGen, and Hugging Face for end-to-end implementation. By the end of the course, learners will be equipped to design scalable, efficient, and ethical AI agents ready for deployment across diverse real-world domains.

Target Audience

  • AI and ML engineers exploring agentic system design
  • Developers building autonomous or task-oriented AI applications
  • Data scientists and researchers interested in open-source LLMs
  • Tech professionals seeking to integrate AI agents into workflows

Register Your Interest

captcha

Our Learners Say About Our Courses

underline
testimonialImg