by rschmelzer

CPMAI™ AI & ML Project Management Training & Certification

Cognilytica’s AI & ML Project Management Training & Certification Boot Camp is recognized around the world as one of the best AI & ML as related to Project Management training courses available anywhere.

The Boot Camp is an intensive, interactive, real-world based “fire hose” that prepares you to succeed with your AI & ML efforts, whether you’re just beginning them or are well down the road with implementation, reflecting the best thinking and research that Cognilytica produces.

Want to learn how to successfully run AI projects at any scale? Learn the latest  best-practices methodology for AI & ML projects with Cognilytica’s Cognitive Project Management for AI (CPMAI) Methodology .

The CPMAI methodology is the industry’s best practice for AI & ML projects. Cognilytica’s CPMAI training and certification prepares you to succeed with your AI & ML efforts, whether you’re just beginning them or are well down the road with implementation. CPMAI Training and Certification is offered as a virtual self-paced course where individuals or small groups can sign up and take the virtual courses around your busy schedule.


You’ll learn the following:

  • Fundamentals of AI and ML Terminology and concepts
  • The Seven Patterns of AI
  • AI Project Management Best Practices
  • Deep dive into actual AI projects using CPMAI
  • Understanding of supervised, unsupervised, and reinforcement learning methods, approaches, concepts, and algorithms
  • Most important aspects of Data Science relevant to AI
  • How business understanding, data understanding, data preparation, model development, model evaluation, and model operationalization fit together
  • Iterative and agile methods for AI
  • How to build Ethical and Responsible AI systems
  • How to craft an ideal AI team
Cognilytica’s CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise. This course is valuable for anyone who wants in-depth knowledge about how to succeed with AI & ML related projects.

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What makes Cognilytica Training so special?

  • Vendor independent
  • End User focused
  • Content updated multiple times per year
  • AI & ML Project Management Focused
  • Provides the enterprise context
  • Offers detailed case studies
  • Worldwide proven, International training audience
  • Led by AI & ML thought leaders
  • Not too technical, not too high-level
  • Includes certification

No Prerequisites!

Cognilytica’s CPMAI AI & ML Project Management Certification has no prerequisites, and is designed for people managing AI & ML projects but appropriate for people with different roles and levels of expertise. This course is valuable for anyone who wants in-depth knowledge about how to succeed with AI & ML related projects.

Agenda 5.0

Module 1: Foundations of AI
  • The Promise of AI
  • Why does AI matter
  • AI Reality vs. AI Myth
  • A Brief History of AI
  • Why is AI a “thing” now?
  • Definitions of AI – Terminology and Definitions
  • Narrow vs. Strong AI
  • The Core Aspects of Intelligence
  • Machine Learning and Cognitive Technology
  • Supervised, Unsupervised, and Reinforcement Learning
  • Deep Learning: A Revolution in Neural Networks
  • Augmented Intelligence
  • Our AI-Enabled Vision of the Future
  • The DIKUW Pyramid (AI is Based on Data)
  • What is Data Science and why is it relevant to AI
  • Realizing the Promise of AI: The AI-Enabled Vision of the Future
  • Data-centric methodologies for AI
  • Leveraging CRISP-DM Methodology
  • Agile Methodology & CPMAI : Dual Iteration Loops
  • Introducing CPMAI
Module 2: Patterns of AI
  • Do We Even Need AI?
  • Where AI is Best Suited
  • The 7 Patterns of AI
  • Pattern: Conversations & Content
    • Conversational Use Cases
    • Conversational Industry Examples
    • Conversational Case Study
  • Pattern: Recognition
    • Recognition Use Cases
    • Recognition Industry Examples
    • Recognition Case Study
  • Pattern: Autonomous Systems
    • Autonomous Use Cases
    • Autonomous Industry Examples
    • Autonomous Case Study
  • Pattern: Hypersonalization
    • Hyperpersonalization Use Cases
    • Hyperpersonalization Industry Examples
    • Hyperpersonalization Case Study
  • Pattern: Pattern & Anomaly Detection
    • Pattern & Anomaly Detection Use Cases
    • Pattern & Anomaly Detection Industry Examples
    • Pattern & Anomaly Detection Case Study
  • Pattern: Predictive Analytics & Decision Support
    • Predictive Analytics & Decision Support Use Cases
    • Predictive Analytics & Decision Support Industry Examples
    • Predictive Analytics & Decision Support Case Study
  • Pattern: Goal-Driven Systems
    • Goal-Driven Systems Use Cases
    • Goal-Driven Systems Industry Examples
    • Goal-Driven Systems Case Study
  • Tackling AI Projects as Combinations of Patterns
    • Examples with multiple patterns in a single project
Module 3: CPMAI Phase I: Business Understanding
  • ML Anti-Patterns
  • Case Studies in AI
  • Real-World Examples and Use Cases
  • Identifying Value Propositions for AI & Cognitive Tech
  • Developing Business-Centric ROI
  • Autonomous vs. Augmented / Assisted Intelligence Approaches
  • Probabilistic vs. Deterministic Systems
  • The AI Go/No-Go Decision
  • Scoping AI Projects
  • CPMAI Phase I Deliverables
  • Taking a Data-Centric Mentality on AI
Module 4: CPMAI Phase II: Data Understanding
  • AI Projects Need a Foundation in Big Data
  • Moving our way up the DIKUW Pyramid
  • Structured, Unstructured, Semi-Structured data
  • What is Data Science?
  • What is Data Engineering?
  • Data Scientists vs. Data Engineers
  • Relationship between ML & Data Science

  • 80% of AI Projects are Data Engineering
  • What Did We Learn from Big Data?
  • Big Data Infrastructure
  • Big Data Methodologies
  • Applying what we learned from Big Data Projects
  • Do you even need Big Data for AI?
  • Taking a Data-Centric Mentality on AI
  • CPMAI Phase II Deliverables
Module 5: CPMAI Phase III: Data Preparation
  • Data engineering and preparation
  • Data Preparation: Questions to Answer
  • Data Engineering Tasks
  • AI-Specific Needs of Data Preparation
  • Dataset Preparation & Pre-Processing

  • Cleaning & Enhancing Data
  • Data Selection / Sampling
  • Labeling Data
  • CPMAI Data Preparation Documents & Artifacts
  • CPMAI Phase 3 Worksheet
Module 6: CPMAI Phase IV: Model Development
  • Intelligence requires learning
  • Machine Learning: A Technology Definition
  • How do we encode experience?
  • Machine Learning Terminology
  • ML Concept: Dimensions
  • The Curse of Dimensionality: Why Machine Learning is Hard
  • ML Concept: Classification
  • ML Concept: Regression
  • Diving Deeper into the Three Forms of Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Machine Learning Algorithms & Models
  • Supervised Learning & ML Concepts Visually Explained
  • Finding the Line: Cost Functions, Gradient Descent and More
  • Neural Networks: Mimicking the Brain
  • Deep Learning: Making Neural Networks Work
  • ML Model Training: Tuning the Knobs
  • Flavors of Deep Learning Neural Networks
  • What exactly is (in) a ML model?
  • Transfer Learning: Speeding Things Up
  • The Pre-Trained Model



  • Using Pre-Trained Models (Networks)
  • Other approaches to Supervised Learning
  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forests
  • Bayesian Classifiers
  • K-Nearest Neighbors
  • Machine Learning Challenge: The Bias-Variance Tradeoff
  • Resolving the Bias-Variance Tradeoff: Cross-Validation
  • Improving Performance with Ensemble Methods
  • Unsupervised Learning & ML Concepts Visually Explained
  • Neural Network approaches to Unsupervised Learning
  • K-Means Clustering
  • Gaussian Mixture Models
  • t-SNE Example
  • Autoencoders and GANs
  • Reinforcement Learning & ML Concepts Visually Explained
  • Q-Learning
  • DeepMind Examples
  • The Limits of Machine Learning
  • Machine Reasoning & Common Sense
  • Knowledge Graphs
  • AI is still evolving
Module 7: CPMAI Phase V: Model Evaluation
  • Model Evaluation & Testing
  • The Confusion Matrix
  • Business / KPI evaluation
  • Model iteration
  • Performance Evaluation
  • Business evaluation
  • Learning the curves
  • Evaluating vs. the heuristic
Module 8: CPMAI Phase VI: Model Operationalization
  • AI Vendor Classification
  • Big Data infrastructure and AI
  • The four “platforms” of AI
  • ML as a Service and Cloud ML
  • AI operationalization
  • The Data Science Environment
  • The Data Science Notebook
  • Data Science Notebook Choices: Jupytr, Colab, and More
  • Setting up a Good Data Notebook Environment
  • Data Visualization and AI
  • Python, R, MATLAB, Mathematica, and More
  • ML Frameworks / Toolkits Compared: Keras, TensorFlow, Caffe(2), Pytorch, MXNet, and more
  • Model Development: Python Scikit-Learn
  • Model Training
  • Accelerating Model Training: GPUs, TPUs, and More
  • AutoML
  • Vendors in the Data Science Environment
  • Pre-Trained Models and Networks
  • The Big Data & Data Engineering Environment
  • Hadoop & Spark

  • Data Preparation Tools
  • Data Labeling Solutions
  • Vendors in the Data Engineering Environment
  • Model “Scaffolding” Environment
  • Machine Learning as a Service (MLaaS)
  • Cloud ML Services
  • Cloud Training vs. Cloud Inference
  • Point-Solution AI Providers: Recognition
  • Point-Solution AI Providers: Predictive Analytics & Decision Support
  • Point-Solution AI Providers: Conversation & Content Generation
  • Point-Solution AI Providers: Industry-Specific
  • Point-Solution AI Providers: Patterns & Anomalies
  • Vendors in the “Middle” AI Environment
  • The Operational Environment
  • Operationalizing ML at the “Edge”
  • Operationalizing ML in the Server
  • Operationalizing ML in the Cloud
  • The Problem with Pseudo AI
Module 9: Responsible and Ethical AI
  • For AI to have Lasting Positive Impact it Must be Done Responsibly
  • Transformative technologies are disruptive.
  • Tackling Fears of AI
  • Tackling Real Concerns about AI
  • AI is not a Job Killer, but it is a Job Category Killer
  • Automation & The Amazon Paradox
  • Making AI Transparent & Explainable
  • Detecting Informational Bias in Datasets
  • AI Introduces new Threat Vectors: Malicious AI
  • AI Can Make the Fake Seem Real… and the reverse
  • The “Uncanny Valley”
  • Privacy in an Era of AI-Enhanced Big Data
  • Pervasive surveillance

  • The rise of Adversarial attacks on Computer Vision systems
  • Concerns over Artificial General Intelligence (AGI)
  • The “Singularity”
  • Resolving these Issues: Keep Humans in the Loop
  • The Future or Dystopia?
  • AI Laws & Regulations
  • Organizational AI Ethics & Governance
  • Resolving these Issues: Provide Transparency
  • GDPR and AI Transparency
  • Resolving these Issues: Prioritize Fairness and Dignity
  • The Limits of Current Cognitive Tech
  • Group Discussion & Exercises

How Does Cognilytica’s Training Compare?


Other Online TrainingVendor Training
Course Price
(Self-paced online, per person)



Certification & Exam Cost Included




Project-Management Focused

Training offered in person and online



Content Updated Many Times per Year



Online, Self-Paced

We offer our CPMAI Training & Certification as a self-paced virtual offering that you can complete on your time around your busy schedule. No traveling required for this offering!  Each session can be completed in around 2 hours, with exercises provided after each module. Sign up on the link below!

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