Learn Agentic AI in Live Online Classes by Experts

A Certified Agentic and Robotic AI Engineer is a professional skilled in developing autonomous AI systems. These systems can act and make decisions independently (agentic AI) and interact with the physical world (robotic or physical AI).

This Panaversity certification program equips you with the expertise to build and deploy:

• Autonomous software agents
• Humanoid robots
• Fine-tuned large language models (LLMs)

The program covers:

• Agentic AI: Building AI systems capable of autonomous learning, perception, reasoning, and action. This includes multi-agent AI systems, AI-powered SaaS solutions, and knowledge graphs.
• Humanoid Robotics and Physical AI: Designing, simulating, and deploying robots capable of natural interactions with humans using platforms like ROS 2 and NVIDIA Isaac.
• Cloud Native and Distributed Computing: Constructing scalable AI-powered microservices using Docker, Kubernetes, and Ray for cloud-native and distributed applications.
• Custom GPT and LLM Fine-Tuning: Customizing LLMs for specific applications using tools like PyTorch and FastAI.
• AI Ethics and Governance: Understanding principles and practices for responsible AI use.

This curriculum prepares you to become a leader in the expanding AI field.

This program offers several compelling reasons to enroll:

• High-Demand Skills: Develop sought-after skills in intelligent, scalable cloud applications using Agentic AI and Cloud Native technologies.
• Industry-Ready: Prepare for global certifications, startup and freelance opportunities after just six months.
• Future-Proof Your Career: Stay ahead of the curve in a rapidly evolving tech landscape.

You'll gain expertise in:

• Multi-Agent Systems and Custom GPTs: Fine-tuning foundational AI models for specific tasks and automating business processes.
• Physical AI and Humanoid Robotics: Designing, simulating, and deploying advanced humanoid robots.
• AI-Powered Microservices: Mastering Python, building APIs, and leveraging cutting-edge GenAI APIs.
• Cloud Native Expertise: Designing and deploying cloud-native applications using Docker, Kubernetes, Terraform, and GitHub Actions.
• Distributed System Design: Building systems that run on multiple computers using Ray.
• AI Solution Design: Leveraging Design Thinking and Behavior Driven Development (BDD) to create user-centric AI solutions.
• Fine-Tuning Open-Source LLMs: Fine-tuning models like Meta LLaMA 3 using PyTorch, Ray, and Fast AI for cloud-native training and deployment.
• Flexible Learning: Earn while you learn by freelancing or contributing to projects after the third quarter.

This certification positions you as a prime candidate for launching successful AI startups:

• Emerging Market Demand: There's a growing deman for tailored AI solutions, and certified engineers can develop autonomous agents and AI systems for niche markets.
• Differentiation: Your skills in agentic and physical AI allow you to create products that bridge digital and physical experiences, setting you apart from the competition.
• Lean Startup Operations: Your full-stack AI knowledge enables you to handle much of the development yourself, reducing costs and enabling rapid prototyping.
• Funding Opportunities: Investors are actively seeking AI and robotics startups, and your expertise makes your venture attractive to them.
• Market Adaptability: The program's breadth allows you to adapt to market demands and pivot your offerings based on feedback.
• Scalability and Innovation: You can build robust, scalable AI systems using cloud-native development and distributed computing tools.
• Lowered Barriers to Entry: Cloud platforms and generative AI APIs reduce the resource intensity of robotics and AI development, making it easier to launch your startup.
• Long-Term Vision: The program equips you to anticipate and address future industry transformations driven by AI.

While TypeScript is gaining popularity for web development, Python remains the dominant language in the AI community. This is due to:

• Python's AI Dominance: Python is the de facto standard for AI development, with a vast ecosystem of AI-specific libraries and frameworks.
• Library Updates: New libraries and updates are frequently released for Python first, allowing you to leverage the latest advancements.
• Community and Resources: Python has a larger and more established AI community, offering ample resources and support.

These technologies offer significant advantages for development, testing, and deployment:

Docker Containers:

• Consistent Environment: Package software into a portable unit that runs consistently across different machines and operating systems.
• Cross-Platform Flexibility: Ideal for deploying applications on various platforms, including cloud services.

Docker Compose:

• Multi-Container Management: Define and manage multi-container applications with a single YAML file.
• Simplified Orchestration: Describe services, configurations, dependencies, and interactions in a single file, making it easier to manage complex applications.

Kubernetes:

• Automated Container Orchestration: Automates container deployment, scaling, and management across a cluster of machines.
• High Availability and Scalability: Ensures application uptime and handles increased traffic by distributing containers across multiple nodes.
• Resource Optimization: Efficiently manages resources, such as CPU and memory allocation, across the cluster.

Open technologies offer several benefits compared to relying solely on proprietary cloud services:

Portability and Flexibility:

• Vendor Agnostic: Operate across any cloud provider or on-premises infrastructure without vendor lock-in.
• Easy Migration: Move applications seamlessly across environments thanks to containerization and Kubernetes' consistent orchestration.

Cost Efficiency:

• Avoid Vendor Lock-In: Leverage competitive pricing from multiple providers or use on-premises resources.
• Optimized Resource Utilization: Kubernetes manages resources efficiently, potentially reducing costs.

Community and Ecosystem:

• Open Source: Supported by active communities that continually enhance the software, offer support, and share best practices.
• Rich Ecosystem: Access a wide range of tools and integrations to meet your specific needs.

Standardisation and Consistency:

• Unified Platform: Docker, Kubernetes, and Terraform provide a unified platform for container orchestration, infrastructure management, and consistent development.
• Simplified Management: Manage infrastructure as code with Terraform, ensuring reproducibility and consistency.

FastAPI and its associated technologies offer several advantages for API development:

FastAPI:

• High Performance: A high-performance framework designed for speed and efficiency.
• Lightweight and Easy to Use: Simplifies API development with a clear and intuitive syntax.
• Scalable and Secure: Built for scalability and security, meeting the demands of modern applications.
• Broad Compatibility: Easily integrates with various tools, databases, and external services.

Related Technologies:

• Pydantic: Enables data validation and serialization, ensuring data integrity and improving code quality.
• GQL (Graph Query Language): An ISO standard query language for querying property graphs, allowing standardized graph data management.
• Neo4j: A scalable graph database management system widely used for handling complex data relationships.

Docker Containers offer several advantages throughout the software development lifecycle:

Development:

• Isolated Environments: Set up isolated environments for different projects, avoiding dependency conflicts and ensuring consistency.
• Reproducibility: Achieve consistent development environments across teams, simplifying collaboration and reducing "works on my machine" issues.
• Faster Setup: Rapidly set up and tear down development environments, accelerating the development workflow.

Testing:

• Consistent Testing Environments: Run tests in isolated containers that mimic the production environment, improving test reliability and accuracy of test results.
• Parallel Testing: Execute tests concurrently across multiple containers to speed up the testing process.

Deployment:

• Simplified Deployment: Package applications and dependencies into containers, enabling straightforward deployment to various environments.
• Version Control: Easily manage different application versions through Docker images.
• Scalability: Scale applications efficiently by deploying multiple containers across a cluster as needed.

Physical AI integrates artificial intelligence with physical entities, enabling them to operate and interact in the real world.

Key characteristics:

• Real-World Interaction: Perceiving the environment through sensors and taking actions using actuators.
• Embodiment: AI embedded in physical bodies, like robots, allowing interaction with the physical world.
• Understanding Physics: Comprehending and adhering to physical laws like gravity and friction.
• Human-like Functionality: Performing tasks in human-designed environments using human-like form factors.
• Data-Driven Training: Leveraging real-world data to improve performance through machine learning.

Applications:

• Healthcare: Assistive robots supporting patient care, rehabilitation, and surgical tasks.
• Service Industry: Robots for cleaning, delivery, and customer service roles./li>
• Manufacturing: Industrial robots for assembly, inventory management, and quality control.
• Exploration: Robots for exploring challenging environments like space or underwater.