AI for Leaders & Program Managers: Strategy to Deployment

Name: AI for Leaders & Program Managers: Strategy to Deployment
Rating: 4.5 (444 reviews)

Avoid the 85% failure rate. Learn to plan, manage, build, & deploy AI projects that succeed in the real world.

Created byProf. Ryan Ahmed, Ph.D., MBA | 600,000+ Students | Best-Selling Instructor, Stemplicity Inc.

Last updated 1/2026

English

What you'll learn

Understand the full lifecycle of AI projects from initial concept and problem definition to model deployment and monitoring.
Build a strong foundation in AI fundamentals, including traditional AI, generative AI, and autonomous AI agents.
Learn the unique challenges and requirements of AI projects compared to conventional software development.
Translate business problems into AI use cases by identifying high-value applications and clearly defining success metrics (KPIs).
Master the art of building effective AI teams, including data scientists, ML engineers, domain experts, and project managers.
Understand differences between structured vs. unstructured, labeled vs. unlabeled and choosing appropriate internal and external data sources.
Design a comprehensive data strategy that includes data collection, governance, access control, and lifecycle management.
Master data cleaning techniques, feature engineering, & dataset versioning. Understand the importance of data quality & labeling accuracy for model performance.
Select suitable AI/ML models based on the problem type and data availability and learn the trade-offs of different architectures.
Apply appropriate metrics (accuracy, F1 score, ROC AUC, etc.) to evaluate models. Use testing strategies and open-source leaderboards to benchmark performance.
Understand MLOps practices such as CI/CD, model serving, monitoring, and automated retraining.
Learn how to set up performance monitoring pipelines to track AI Models drift, errors, and model decay.
Understand the ethical implications of AI. Learn to navigate legal frameworks, ensure fairness and transparency, and prevent bias.
Use tools and Platforms like Pandas, Hugging Face, Kaggle, & Google Teachable Machines.
Understand the differences between Databases, Data Lakes, and Data Warehouses for AI data storage.

Course content

6 sections • 86 lectures • 8h 11m total length

Welcome to the Course, Instructor Intro, & Setup Your Local AI Assistant!7:47
Download the Course Materials0:01
Course Outline & Key Success Tips7:49

What is AI? Generative AI? AI Agents & AI Agents Demo12:44
Explore artificial intelligence fundamentals, the AI life cycle, and generative AI, including AI agents. Build a simple traffic sign classifier and gain hands-on experience with AI tools.
AI Projects Lifecycle9:38
Practice Opportunity Question: AI Project Lifecycle in Healthcare2:09
Practice Opportunity Solution: AI Project Lifecycle in Healthcare4:14
Apply seven-step ai project lifecycle to a healthcare use case, predicting diabetes risk from patient data. Build binary classifiers, deploy to hospital EHRs, and monitor performance for fairness.
Build an AI Model & Key Definitions12:29
Practice Opportunity Question: Build an AI Model & Key Definitions2:01
Practice Opportunity Solution: Build an AI Model & Key Definitions5:12
Explore extending a Teachable Machine model by adding new classes, uploading 100 images per class, retraining, and mastering the end-to-end lifecycle including architecture, hyperparameter tuning, deployment, and monitoring.
Key Challenges in AI Project Lifecycle12:22
AI Future: Artificial General Intelligence (AGI) & Super Intelligence (ASI)11:20
Explore the AI revolution from Eliza to AGI and ASI, tracing milestones like transformers and GPT, and examining levels from conversational AI to autonomous AI.
Summary, Conclusion and Thank You!1:50

Introduction to AI Project Management & Strategic Planning Module3:22
Defining Vision, Scope, and Feasibility Assessment10:30
Define a north star vision, scope, and feasibility for AI projects. Align business value with model KPIs and outline an MVP to prevent scope creep.
Practice Opportunity Question: AI Project Feasibility Assessment3:06
Assess the technical and organizational feasibility of an AI model to flag dental and outpatient fraudulent health claims at UnitedHealth Group, using BigQuery and Vertex AI for a six-month MVP.
Practice Opportunity Solution: AI Project Feasibility Assessment5:49
AI Project Success Metrics: Model, System, and Business7:12
Practice Opportunity Question: Defining AI Project Success Metrics1:06
Practice Opportunity Solution: Defining AI Project Success Metrics4:03
Define ai project success with model metrics like 85% accuracy, under 10% false positives, and 90% recall by Q2, while controlling false negatives and reducing missed payments and cost savings.
Key AI Project Stakeholders and Roles Overview5:34
Key AI Project Stakeholders and Roles: Business and Product Roles5:09
Key AI Project Stakeholders and Roles: Data Science and Technical AI Roles7:18
Key AI Project Stakeholders and Roles: Development/Operations & Data Engineering6:03
Map ai project stakeholders to development, operations, and data engineering roles, and outline pipelines, deployment, and governance for scalable, secure ai systems.
Practice Opportunity Question: Key AI Projects Roles1:00
Assign responsibilities for ai powered customer support chatbot, aligning the ai vision with business goals. Distribute tasks across data engineering and data science roles to enable instant answers and escalation.
Practice Opportunity Solution: Key AI Projects Roles2:06
Waterfall Vs. Agile Development8:14
AI Project Management Frameworks: Crisp-DM Vs. Agile Vs. Hybrid9:09
Compare crisp-dm, agile, and hybrid ai project management frameworks, detailing data understanding, data preparation, modeling, evaluation, deployment, and sprint-driven cycles for rapid prototyping.
AI Project Management Frameworks: Practical Example5:51
Scaled Agile Framework (SAFe)7:02
Risk Management in AI Projects3:49
Key Success Ingredients for AI Projects2:07
AI Project Budgeting6:54
Practice Opportunity Question: AI Project Budgeting1:45
Practice Opportunity Solution: AI Project Budgeting4:17
Concluding Remarks and Thank You Message1:39

Module Agenda and Welcome Message2:18
Data Strategy & Key Components9:31
Define a practical data strategy by detailing data acquisition, exploration, cleaning, wrangling, and feature engineering to power AI models and ensure governance, privacy, and KPI alignment.
Data Strategy Practical Example5:16
Apply a nine-pillar data strategy to a predictive maintenance case by integrating sensor data and maintenance logs, ensuring data discovery, quality, governance, privacy, secure storage, labeling, integration, and KPI tracking.
Practice Opportunity Question: Data Strategy1:14
Brainstorm a high-level data strategy for training, validation, and deployment of autonomous vehicle ai, outlining key components and leveraging llama model, lm studio, and gpt four as references.
Practice Opportunity Solution: Data Strategy6:26
Defining Data Requirements: Structured Vs. Unstructured, Labeled Vs. Unlabeled7:31
Defining Data Requirements: Supervised Vs. Unsupervised Vs. Reinforcement4:45
Compare supervised learning with labeled data, unsupervised learning with pattern discovery and clustering, and reinforcement learning where an agent learns by interacting with an environment to maximize rewards.
Defining Data Requirements: Internal Vs. External Data Sources11:20
Practice Opportunity Question: External Data Sources1:10
Practice Opportunity Solution: External Data Sources2:27
Explore external data sources by browsing Hugging Face datasets, filtering news data by most downloaded samples, and using JSON filters to isolate CNBC news for a practical opportunity.
Data Quality Assessment7:03
Assess data quality by comparing good versus bad data across relevance, representation, accuracy, completeness, balance, consistency, timeliness, and privacy. See clean, balanced data examples and model performance impact.
Practice Opportunity Question: Data Quality Assessment1:16
Practice Opportunity Solution: Data Quality Assessment2:08
Data Wrangling and Feature Engineering5:42
Pandas Library for Data Wrangling and Analysis6:42
Explore the pandas library for data analysis and manipulation in Python, including data frames, CSV, Excel, and SQL inputs, and automation with ChatGPT for scalable, repeatable data tasks.
Practical Example on Data Wrangling and Analysis Overview6:13
Learn data wrangling and analysis in pandas with python by reading, cleaning, merging client demographics and financial details, handling missing values, and performing sorting, filtering, and visualizations.
Task 1. Read Excel Files into Pandas8:39
Task 2. Handle Missing Values8:17
Task 3. Merge Pandas DataFrames2:42
Task 4. Perform Sorting and Filtering6:50
Task 5. Perform Data Visualization7:57
Data Labeling & Pricing7:21
Database Vs. Data Warehouse Vs. Data Lake3:27
Compare databases, data warehouses, and data lakes for storage and analytics: databases support transactions, data warehouses enable BI dashboards, and data lakes store structured and unstructured data.
Data Pipelines, Governance, & Security10:02
Conclusion, Summary, and Thank You!1:47

Module Introduction and Agenda Overview2:43
Introduction to Models and Algorithms & Growth of Computing Power6:19
Neural Networks and Deep Learning 1019:01
Build an Artificial Neural Network to Solve Classification Tasks6:25
Practice Opportunity Question: Build an Artificial Neural Network1:31
Practice Opportunity Solution: Build an Artificial Neural Network7:00
Classical and Advanced AI Models Overview11:38
Visualize Neural Networks4:15
Visualize artificial neural networks from input through convolution, downsampling, pooling, and dense layers, and see how features map to outputs in tensor space with AlexNet.
Practice Opportunity Question: Choosing the right AI/ML Model Architecture1:19
Map problems to neural network architectures—feed-forward, CNNs, RNNs, and LSTMs—and justify the strengths for tasks like electricity forecasting, digit recognition, synthetic face generation, anomaly detection, and English to French translation.
Practice Opportunity Solution: Choosing the right AI/ML Model Architecture3:21
Classification Models Key Performance Indicators (KPIs)11:05
Practice Opportunity Question: Classification Models Key Performance Indicators1:24
Practice Opportunity Solution: Classification Models Key Performance Indicators3:05
Regression 101 & Key Performance Indicators (KPIs) - Part 16:28
Regression 101 & Key Performance Indicators (KPIs) - Part 29:34
Practice Opportunity Question: Regression Models KPIs1:32
Practice Opportunity Solution: Regression Models KPIs1:50
AI Models Training Infrastructure: Cloud, Docker, & Orchestration, Scaling10:45
Learn AI training infrastructure from hardware like GPUs and TPUs to Docker containers and cloud platforms such as AWS SageMaker, Vertex AI, and Azure, with Kubernetes orchestration for scalable training.
Experimentation, Tracking, Version Control & "Weights and Biases" Demo7:53
Transfer Learning3:14
A Framework to Select the right model for your application - Part 113:56
A Framework to Select the right model for your application - Part 27:18
Leaderboard for Open and Closed-Source Models - Part 19:09
Leaderboard for Open and Closed-Source Models - Part 29:29

Requirements

A Laptop and Internet Connection is all you need. No programming experience is required!

Description

Research indicates that over 85% of AI projects fail to deliver on their promise.

This is because teams jump straight to building models without a clear strategy, plan, or understanding of the whole picture and the entire AI lifecycle end-to-end.

That’s where this course comes in.

This course is designed to help you bridge the gap between AI theory and real-world execution.

The course is designed for product managers, engineers, business leaders, or anyone curious about AI. It will give you a practical, step-by-step roadmap to manage AI projects from start to finish.

We’ll start with the fundamentals, like what AI, generative AI, and AI agents are, and walk through each phase of the lifecycle: defining business goals, building a strong data strategy, selecting and validating the right models, and deploying solutions that work in the real world.

We will then learn how to ensure ethical AI use, navigate governance and compliance, and avoid the common pitfalls that derail so many AI projects.

No prior coding or AI experience is needed. You'll gain hands-on exposure to tools like Pandas, SageMaker, Hugging Face, and Teachable Machine, and apply your learning through real-world case studies and practice challenges.

By the end of the course, you won’t just understand AI; you’ll know how to lead it!

Enroll today, and I look forward to seeing you on the other side!

Who this course is for:

Product Managers wanting to define AI product vision, set KPIs, and lead cross-functional teams through the AI lifecycle.
Engineers & Data Scientists who want to master the full AI project lifecycle from data strategy to deployment, beyond just model building.
Business Leaders & Analysts wanting to apply AI to improve decision-making, optimize operations, and gain competitive advantage.
Project Managers who want to manage AI project timelines, risks, and resources. No technical background required.
Executives & Innovation Leads who evaluate AI investments, align them with strategy, and drive successful adoption across the business.
Students & Career Changers wanting to build practical, AI project management skills with no prior experience required.

AI for Leaders & Program Managers: Strategy to Deployment

What you'll learn

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Course content

Welcome Message, Instructor Introduction, & Course Outline3 lectures • 16min

AI Fundamentals and AI Project Lifecycle Overview10 lectures • 1hr 14min

AI Project Management: Stakeholders, Teams, and Strategic Planning23 lectures • 1hr 53min

Data Strategy and Preparation in AI Projects Lifecycle25 lectures • 2hr 18min

AI Models Development, Evaluation, Version Control, & Infrastructure24 lectures • 2hr 30min

Congratulations and Thank You!1 lecture • 1min

Requirements

Description

Who this course is for: