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CPMAI Training & Certification

by rschmelzer

Cognitive Project Management for Artificial Intelligence (CPMAI) Methodology

AI & ML Project Management Training & Certification

The Leading Vendor Independent, AI & ML Project Management Training

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 course 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.

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.

Upcoming CPMAI Training & Certification Sessions:

What makes the Cognilytica Training so special?

  • Vendor independent
  • End User focused
  • Updated frequently
  • AI & ML Project Management Focused
  • Provides the enterprise context
  • Offers detailed case studies
  • Globally recognized certification
  • Led by AI & ML thought leaders
  • Not too technical, not too high-level
  • Includes certification

Offered as in both in-person (on-site and open enrollment) and online (live virtual) Training.

Highlights of Cognilytica AI & ML Project Management Training & Certification Boot Camp

Foundations of AI

  • The Promise of AI
  • Why does AI matter
  • AI Reality vs. AI Myth
  • The AI-Enabled Vision of the Future
  • The Core Aspects of Intelligence
  • AI Terminology & Definitions

AI Technology Fundamentals

  • Machine learning fundamentals
  • Unsupervised, Supervised, and Reinforcement learning approaches
  • Relationship between AI & Data Science
  • AI in the Context of Big Data

AI Best Practices & Use Cases

  • The Seven Patterns of AI
  • ML Anti-Patterns
  • Case Studies in AI
  • Real-World Examples and Use Cases

Responsible & Ethical AI

  • Keeping the human in the loop
  • Addressing fears and concerns about AI
  • Transparent & explainable AI (XAI)
  • Emerging laws and regulations
  • Addressing issues of bias and human induced error

Cognilytica’s Project Management for AI Methodology (CPMAI)

    • Data-centric methodologies for AI
    • Leveraging best practices from in-production AI implemetnations
    • Data engineering and preparation
    • ML model training approahes
    • ML model evaluation
    • Operationalization strategies
  • The AI Vendor Landscape

    • AI Vendor Classification
    • Big Data infrastructure and AI
    • The four “platforms” of AI
    • ML as a Service and Cloud ML
    • AI operationalization

How Does Cognilytica Compare?


Code AcademiesVendor Training
Course Price
(US Events, per person)




Certification & Exam Cost





Implementation Focused

Developer FocusedMarketing Focused
Training offered in person and online

Certification Granted in Course

Content Updated

4-6 times a year

1-2 times a year???
Taught by Recognized Experts

Hired instructorsTaught by Marketing or Product People

Cognilytica AI & ML Project Management Training & Certification is offered as public and private courses, as well as custom-designed courses for your specific and special needs!

Interesting in enrolling? Contact us!

We offer our CPMAI AI & ML Training & Certification in four different ways

  • In-person

  • Online

    • We offer our CPMAI Training & Certification as a series of 8 online sessions, each lasting around 3 hours, with group discussions and exercises. These are led by live instructors that directly interact with you to make sure your questions are answered and needs met. Live virtual sessions start at least once a month and run for 4 consecutive weeks. Inquire for next available date and to sign up!

Agenda v4.0

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
  • 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
  • Cognilytica’s 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
  • Group Discussion & Exercises


Module 2: AI Patterns, Use Cases, and Real-World Examples
  • 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: AI & Big Data
  • 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


Module 4: Approaches to AI & ML (How Does AI Work?)
  • 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
  • Group Discussion & Exercises


Module 5: Running a Successful AI Project: The CPMAI Methodology
  • Why AI & ML Projects are Not Like Application Development Projects
  • Determining AI Project Fit
  • Using the DIKUW Pyramid to find the Sweet Spot
  • 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
  • AI requires Data-Centric Methodologies
  • ML Projects as Data Management and Processing Projects
  • Revisiting the Agile Manifesto and Agile Methodologies
  • Updating Agile Approaches with Data Science / Data Management Methodologies
  • Agile Methodologies & AI Projects
  • Data-First Methodologies: KDD, CRISP-DM, SEMMA, TDSP
  • Cross-industry Standard Process for Data Mining (CRISP-DM)
  • CRISP-DM: Phases
  • CRISP-DM: Levels
  • Microsoft’s Team Data Science Process
  • Combining Agile + CRISP-DM + TDSP with an AI Twist: Cognilytica’s AI Project Management Methodology (CPMAI)
  • CPMAI Phase 1: Business Understanding
  • Business Strategy – Initial Problem Identification & Appropriate Fit / Scope
  • CPMAI Phase 1: Examples
  • CPMAI Phase 1 Worksheet
  • CPMAI Phase 2: Data Understanding
  • AI & ML Project Data Needs: Training Set Data
  • CPMAI Phase 2: Examples
  • CPMAI Phase 2 Worksheet

  • CPMAI Phase 3: Data Preparation
  • Dataset Preparation & Pre-Processing
  • Dataset Splitting
  • CPMAI Phase 3: Examples
  • CPMAI Phase 3 Worksheet
  • CPMAI Phase 4: AI Modeling
  • The Training Phase
  • Algorithm Selection, Model Development, Ensemble Development
  • Model Training
  • CPMAI Phase 4: Examples
  • CPMAI Phase 4 Worksheet
  • CPMAI Phase 5: Model Evaluation
  • Model Evaluation & Testing
  • The Confusion Matrix
  • Business / KPI evaluation
  • Model iteration
  • CPMAI Phase 5: Examples
  • CPMAI Phase 5 Worksheet
  • CPMAI Phase 6: AI Operationalization
  • The Inference Phase
  • Where does the Model Go?
  • Model Deployment with Governance Framework
  • Cloud vs. on-premise considerations
  • CPMAI Phase 6: Examples
  • CPMAI Phase 6 Worksheet
  • Building the AI Project Team
  • Growing Data-Centric Talent
  • Group Discussions & Exercises


Module 6: Building & Operationalizing AI
  • Data-centric AI “Development” vs. Application Development
  • Four Environments: Data Science & Model Development, Big Data / Data Engineering, Model Scaffolding, Operational
  • The Machine Learning “Platform” Doesn’t Exist
  • The Cognilytica AI Vendor Landscape
  • 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
  • Group Discussion & Exercises


Module 7: 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

Offered as both Open Enrollment at our Training Locations, or as private, on-site training for groups of 20 or more!

Interested in on-site training? Contact us!

Upcoming Live Virtual Trainings:

CPMAI AI & ML Project Management Training & Certification – Live Virtual (online) – July 15, 2019 Start Date

CPMAI AI & ML Project Management Training & Certification – Live Virtual (online) – Sept 16, 2019 Start Date

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