The New AI Tech Stack: AI Literacy for Tech Leaders

Course details

This course acts as a comprehensive resource designed to equip tech professionals with a deep understanding of the AI technology landscape and how the AI tech stack works. This course provides a practical and structured approach to mastering AI concepts, technologies, and implementation strategies, enabling you to effectively lead and manage AI initiatives within your organization. Through a combination of proven knowledge and real-world case studies, gain the skills and confidence to navigate the rapidly evolving AI ecosystem.

Learning objectives
Understand the foundational AI concepts, including machine learning, generative AI and deep learning, enabling effective communication and decision-making.
Explore a wide range of AI technologies, such as natural language processing, computer vision, robotics, and autonomous systems, and their applications across various industries.
Understand the data lifecycle in AI, including data acquisition, governance, storage, processing, and monetization strategies.
Explore MLOps (machine learning operations) concepts and platforms for streamlined deployment, monitoring, and lifecycle management of AI models.
Learn best practices for building and managing AI teams and projects including AI strategy development, budgeting, and effective planning and execution of AI projects.

Concepts

0. Introduction

01 - Welcome to the world of AI
02 - Embedding yourself in AI

1. How to Build AI Solutions

03 - Steps to build AI solutions
04 - Other AI implementation options
05 - Acquiring AI software
06 - CRISP-DM and AI project management
07 - Connecting AI models to systems
08 - Data science stack

2. AI Building Blocks

09 - Gartner Hype Cycle
10 - RPA (robotic process automation)
11 - Robotics
12 - Fraud detection
13 - Predictions
14 - Optimization
15 - Recommendations
16 - Computer vision
17 - Smell, taste, touch
18 - Voice
19 - Autonomous vehicles
20 - IoT and smart cities
21 - AR, VR, and the metaverse
22 - NLP

3. GenAI World

23 - GenAI revolution
24 - Large language models (LLMs)
25 - Fine-tuning and chatbots
26 - RAG architecture
27 - Agents and multimodal LLMs
28 - Practical GenAI applications for companies
29 - GenAI risks

4. Data

30 - Data is the new oil
31 - Sources of data and synthetic data
32 - Storing data
33 - Processing data with GPU and QPU (quantum processor)
34 - Data lifecycle in AI projects
35 - Big data vs. small data
36 - Data monetization
37 - Working with data - Challenges and best practices
38 - Data governance and data management

5. Building an AI Portfolio

39 - Selecting valuable AI projects
40 - Selecting criteria for your AI portfolio
41 - Calculating ROI for AI project

6. Digital Transformation

42 - Digital transformation - Process and roles
43 - Building AI strategy
44 - Best and worst digital transformation practices

7. Building AI Teams

45 - AI team roles
46 - AI team roles on the project lifecycle
47 - Recruiting and motivating your AI team
48 - Communication and other team-building challenges
49 - Selecting your team size

8. Metrics

50 - Strategic metrics
51 - Project metrics
52 - Data science metrics

9. Retraining and MLOps

53 - Retraining AI models
54 - Retraining, rebuilding, or model unlearning
55 - Best time to retrain
56 - Automatic training loop and human-in-the-loop
57 - MLOps, production, and AI maintenance
58 - MLOps platforms

10. AI Management and Presenting AI Concepts

59 - Solving AI management challenges
60 - Selling your AI concept to decision-makers
61 - Answering decision-makers' questions
62 - Working with AI suppliers
63 - Scheduling and budgeting AI projects

11. Capstone Project

64 - Introduction to the case study
65 - Sample solution and presentation
66 - Your turn - Work on your own case study

Conclusion

67 - Use it wisely