The AI Learner Journey training program combines many aspects of AI - from AI Technology to AI Project Management. It is a hands-on, extensive, holistic program with high value for companies, as participants will make a jump in their business-relevant AI knowledge.
Who should attend
The program was designed for a heterogeneous target group: it entails employees from different departments, from very technical ones (e.g. production) to support departments (e.g. sales, HR, etc.), different seniority levels, different backgrounds, and different pre-existing AI knowledge.
- A holistic view of what AI is and can do
- The value AI brings to their company
- The components of AI strategy
- The phases of AI project management
- The relevance of the ML lifecycle
- Applying methods for identifying use cases at their company
- Evaluating the strategic value of an AI use case
- Evaluating the technical feasibility of an AI use case
- Planning the implementation of an AI use case - from Scoping to Deployment
- Developing a road map for implementing an AI use case at Infineon
Benefits for Participants
As a participant, you will gain a holistic understanding of AI. This includes learning not only about the technical side of AI, but also how to build a comprehensive AI strategy, manage AI projects, and ideate and plan the implementation of AI projects. Through this hands-on, interactive offering, you will be best equipped to grow into an important AI-related role in your company.
Benefits for Companies
Empower your employees to take on new roles related to AI. This program will train them to become AI Ambassadors, driving value generation through AI and becoming key spokespeople for your strategic AI endeavors. They will also become experts in AI and provide valuable insights to others in your organization. Participants will help identify new opportunities for growth and innovation, and will help you stay competitive in today's business environment.
Phase 1 - Understanding AI
This phase is laying the knowledge foundation for AI and the following training program. This includes elements from AI technology, AI project management, and AI strategy.
Gives an easy introduction into the technology driving ML. Here, topics such as ML learning types, neural nets, and ethical considerations, are covered. In addition, a Google Colab implementation of an NLP twitter clustering use case is shown. Additional topics can be added in this Module, such as information on generative AI.
AI Project Management
In this module the basics of AI project management are covered. Starting from well-known approaches such as SCRUM and Kanban specific uncertainty-based approaches to ML projects are covered. Participants learn agile project management methods with hands-on exercises. Further, this module covers the ML-Lifecycle in great detail.
This module gives an introduction to AI strategy. It focuses on the AI strategy house and the importance of a holistic AI strategy within a company. Here, some customer specific topics are covered.
Phase 2 - Use Case Ideation
Equipped with lots of AI knowledge, participants (in groups) start to ideate multiple use cases for their company. Then, they assess and prioritize said use cases. Phase 2 ends in the joint decision which use case each team will work on in Phase 3.
Use Case Ideation
Participants learn how to find relevant AI use cases that are in line with the company’s AI ambition. They get to practice different approaches on a case study, before they apply the methods to find relevant use cases.
Use Case Assessment
Participants learn how to assess AI use cases along the dimensions of value & easy of implementation. They apply these assessment techniques to the ideated use cases from the previous session.
Use Case Prioritization
Participants learn how to choose (prioritize) from their ideated and assessed AI use cases. The goal: Each group selects one use case to proceed to phase 3.
Phase 3 - Accelerator Spring
The accelerator sprint is a deep dive in every use case. It entails the detailed planning of the AI use case, a suggested ML-pipeline for implementation, dealing with uncertainty, and an implementation plan along project phases.
Participants work in their groups on the use case they chose in phase 2. In this session, participants are confronted with many questions that are relevant to be answered before use case implementation. The questions are organized along the ML-Lifecycle.
Uncertainty and ML-Pipeline
Participants design a suiting ML-Pipeline for future implementation. Furthermore, they are confronted with events that could potentially happen during implementation. Participants need to find solutions to these tasks.
Participants need to define a road map based on the ML project phases for implementation. They derive a rough timeline for the implementation of the AI use case.