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In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Methodology, Waterfall, Agile, CRISP-DM, Cognitive Project Management for AI (CPMAI).
In this episde we explain how these terms relate to AI and why it’s important to know about them. Just about every single industry is using AI in some shape or form to help streamline and improve processes, increase productivity, gain a competitive edge, and stand out from others. However, when looking to run an AI project – what’s the best methodology to use?
This podcast helps you understand what a methodology is. We also go through the fundamentals of waterfall methodology, agile methodology, and CPMAI methodology is. We also explain CRISP-DM and how it’s the basis for the Cognitive Project Management for AI (CPMAI) methodology.
By the end of the podcast, you will have a basic understanding of each and see what’s needed and how to apply CPMAI to best approach, run, and manage AI projects for success.
Show Notes:
- FREE Intro to CPMAI mini course
- CPMAI Training and Certification
- What is the Cognitive Project Management for AI (CPMAI) Methodology?
- A Step-by-Step Approach to Running AI and Machine Learning Projects
- AI Glossary
- AI Glossary Series – DevOps, Machine Learning Operations (ML Ops)
- AI Glossary Series – Data Preparation, Data Cleaning, Data Splitting, Data Multiplication, Data Transformation
- AI Glossary Series – Data Augmentation, Data Labeling, Bounding box, Sensor fusion
- AI Glossary Series – Data, Dataset, Big Data, DIKUW Pyramid
- AI Glossary Series – V’s of Big Data, Data Volume, Exabyte / Petabyte / Yottabyte / Zettabyte, Data Variety, Data Velocity, Data Veracity
- AI Glossary Series – Machine Learning, Algorithm, Model
- Glossary Series: Machine Learning Approaches: Supervised Learning, Unsupervised Learning, Reinforcement Learning
- Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary
- Glossary Series: Clustering, Cluster Analysis, K-Means, Gaussian Mixture Model