Building Agentic AI Projects for Machine Learning: A Practical Development Guide

Many people have asked me, "How can Agentic AI be applied to Machine Learning projects?" This presentation will answer that question with a practical example and modern software development techniques. Artificial Intelligence (AI) is rapidly evolving beyond traditional Machine Learning (ML) models. The next generation of intelligent applications combines Agentic AI, AI Agents, Skills (tools), Large Language Models (LLMs), and ML to build intelligent, goal-driven systems capable of orchestrating complex workflows and solving real-world problems. In this presentation, we will explore the latest technologies, architectures, frameworks, and development practices for building Agentic AI applications that manage the complete machine learning lifecycle—from data acquisition and preprocessing to feature engineering, model training, evaluation, explainability, deployment, monitoring, and intelligent decision support. Rather than focusing solely on machine learning models, we will demonstrate how AI agents can reason, plan, invoke specialized skills, collaborate with external tools and services, and coordinate multiple machine learning models to automate complex workflows while keeping humans in control of critical decisions. Attendees will gain a practical understanding of modern Agentic AI frameworks, software architecture patterns, and best practices for developing intelligent systems that extend far beyond traditional ML pipelines. . For more information look at the series of biotech papers at “Artificial Intelligence Applications in Biostatistics, Bioinformatics and Computational Biology by Ernest Bonat, Ph.D.”

Topics include
· Introduction to Agentic AI and intelligent software agents
· AI Agents versus traditional Machine Learning applications
· Skills (tools) and their role in agent-based architectures
· Designing modular, reusable, and scalable Agentic AI systems
· Integrating Large Language Models (LLMs) with Machine Learning models
· Orchestrating end-to-end Machine Learning workflows using AI agents
· Agent planning, reasoning, memory, and tool selection
· Best practices for developing production-ready Agentic AI systems
· Live demonstrations of Agentic AI orchestrating Machine Learning workflows
· Applying Agentic AI to a DNA sequence classification project in bioinformatics

Who should attend:
· Data scientists and AI/ML engineers
· Healthcare and biotech professionals
· Supply chain and operations analysts
· Developers curious about practical AI applications
· Students and researchers exploring applied AI
· Anyone who's wondered how AI can go beyond a basic agent SKILLS

Agenda:
· 5:30 – 6:00 pm: Networking and refreshments
· 6:00 – 6:10 pm: Welcome!
· 6:10 – 7:30 pm: Presentation and open discussion
· 7:30 – 8:00 pm: Networking

Location: Entrepreneur Collaborative Center, 2101 East Palm Avenue, Tampa, FL 33605

Parking: Free parking is available in the lot directly north of the ECC building. Please do not park immediately adjacent to the facility.

RSVP: Seating is limited — please RSVP early. If you're bringing a guest, have them RSVP separately so we can plan accordingly.

Speakers:
Ernest Bonat, Ph.D. — Senior GenAI Engineer specializing in Machine Learning systems and AI assistants for Biostatistics , Bioinformatics, Computational Biology, and Healthcare.