DSW DeepTech AI & GenAI Hands-On Masterclass
Practical AI Use Case Development for Enterprises
15 Feb 2025, Saturday | 10:30am - 1:30pm (Irish Standard Time)
At DSW, we recognize that the most effective way to drive AI adoption within enterprises is through practical, hands-on experience. This DeepTech AI & GenAI Masterclass is structured to equip enterprise leaders, data scientists, and innovation teams with the technical and strategic skills required to build, deploy, and scale AI/ML and GenAI use cases ready for production.
Designed to bridge the gap between conceptual understanding and real-world applications, this immersive session covers the entire AI project lifecycle – from experimentation to production readiness. Participants will leave not just with theoretical insights but with practical, deployable AI solutions.
Masterclass Overview
This intensive session will guide participants through hands-on development of AI use cases, empowering them to build and deploy enterprise-grade AI/ML and GenAI solutions that address specific business challenges.
Format
(3 Hour online session)
Certificate
(Certificate for workshop completion)
Limited Seats Available
(No-fees workshop: Sign-up today)
Key Learning Objectives
-
Develop hands-on AI expertise by actively building
and deploying use cases. -
Learn practical approaches to AI adoption at scale,
from experimentation to deployment. -
Gain exposure to AI/ML and GenAI frameworks
used in leading enterprises. -
Understand the AI/ML lifecycle and the transition
to production-ready solutions.
Key Learning Objectives
-
Develop hands-on AI expertise by actively building
and deploying use cases. -
Learn practical approaches to AI adoption at scale,
from experimentation to deployment. -
Gain exposure to AI/ML and GenAI frameworks
used in leading enterprises. -
Understand the AI/ML lifecycle and the transition
to production-ready solutions.
Who Should Attend:
- Chief Data Officers (CDOs)
- AI/ML Team Leaders
- Innovation & Transformation Teams
- Enterprise Architects
- Heads of IT and Data Science Teams
- Data Scientists, Data Engineers, ML Engineers
Agenda
Session 1:
AI, ML, Generative AI and Enterprise Integration
- Introduction to core concepts of AI, ML and Deep Learning.
- Real-world AI/ML enterprise use cases.
- Introduction to Generative AI and Large Language Models (LLMs).
- Real-world applications of GenAI for automation and process optimization.
Session 2:
Hands on experience of AI lifecycle till production
- Exploring components of Machine Learning solution lifecycle like data ingestion, data munging, ML/DL modeling, deployment and monitoring.
- Applying strategies to verify Data drift and Model drift of ML/DL models.
- Lifecycle of AI projects – from ideation to business transformation.
Session 3:
Advanced AI and ML Use Cases for Enterprise Value
- Overview of advanced ML algorithms.
- Applying predictive analytics to enterprise problems.
- Deploying AI solutions at scale – cloud and on-premises strategies.
- Hands-on problem-solving session – Applying advanced techniques to real-world problems.
Session 4:
Building GenAI Use Cases for Enterprise Value
- Identifying high-impact GenAI use cases in business operations.
- Practical framework for developing Generative AI-driven solutions.
- Road mapping GenAI projects from proof of concept to production.
- Hands-on demonstration – Developing and deploying GenAI models.
Session 5:
Enterprise AI Strategy and Scaling
- Building a scalable AI adoption roadmap.
- Structuring sustainable AI frameworks within enterprises.
- Strategies for integrating AI across departments.
- Open Q&A and strategic discussion.
Workshop Outcomes
Hands-On AI & GenAI Development
Deployable AI Use Case Creation
Custom GenAI Agent Built During Session
Roadmap to Scale Enterprise AI
Practical Tools for AI Success
Why This Hands-on Masterclass Matters
Learn how to quickly build real-world solutions that drive tangible business outcomes.
Learn how AI streamlines operations, accelerates time-to-market, and enables scalable, predictable adoption.
Gain insights to better align AI adoption strategies with practical execution as an AI innovation leader.