Enable and Enhance your team to Be Successful with AI Initiatives
AI Best practices
Workshop
Take the next step beyond understanding of AI towards
being AI Successful
Interested in CPMAI Certification & Training?
Interested in the CPMAI Training & Certification for yourself instead of this team workshop?
Click here to Enroll in CPMAI Training & CertificationSome of the organizations that have been through Cognilytica training & certification:
Facilitated for Maximum Team Engagement and Value
Customizable, Best Practices Based, and Immediately Applicable
Your team might be in sync on AI concepts, but you’re still running into roadblocks in making your AI projects work. Or perhaps, you haven’t yet started your first production AI project and want to avoid the traps that get other teams and AI projects stuck. There’s no excuse for failing AI projects. Why make many of the same mistakes that others have made?
The AI Best Practices Workshop is an interactive, facilitated deep dive into the best practices of AI project management, including an overview of the Cognitive Project Management for AI (CPMAI) Methodology. The workshop focuses on real-world AI projects, customized to the projects that your team might be working on currently or in planning. This workshop leverages the latest practices, including CPMAI methodology, which extends the well-known CRISP-DM methodology, Agile Methodologies and additional DataOps and MLOps considerations to focus AI efforts on those that are data-first, ROI-centric, highly iterative, and oriented on the right tasks for operational success.
This interactive, facilitated AI Best Practices workshop helps teams understand common reasons why AI projects fail and how to overcome these reasons while simultaneously working through your team’s real world AI, ML, or data project and talking through discussion questions to get your team thinking and acting in the right mindset for team alignment and project success. This workshop assumes a team understanding of fundamental AI concepts and is for teams looking to take the next step beyond understanding of AI towards being AI Successful.
How will this advance my team AI efforts?
Team Alignment on:
- Most common reasons for AI Project failure
- Busting AI Myths
- Dealing with data quantity and quality issues
- Business and project team AI alignment
- Taking the data-centric perspective on AI
- Aligning business objectives and ROI with AI projects
- Determination of AI Pattern(s)
- Building upon CRISP-DM for a data-centric AI
- Overview of CPMAI Methodology
- Connecting Agile Methodologies with CPMAI
- Pilots vs. Proof of Concept
- Successfully dealing with model and data drift
- Model versioning and iteration best practices
- ML Operationalization management (i.e. ML “Ops”) best practices
You Also Get:
Supporting Deliverables:
- Key AI Failure Reasons Cheatsheet
- CPMAI Methodology Overview Handout
- ML Model Lifecycle Overview
- Cognilytica’s AI Vendor Landscape Chart
- Guided Questions & Decision Factors for Key AI Solutions
Facilitated Team Interaction
- Debunking AI Myths
- Current or potential AI Roadblocks for your team
- Identification of team project management practices and alignment with AI best practices
- Current team iteration practices and alignment with MLOps and DataOps best practices
Part I: Causes of AI Project Failure
Takeaways: AI Failure Reasons Cheatsheet Identification of AI roadblocks
Part II: Data-Centric AI Methodologies
Takeaways: CPMAI Methodology Overview Team best practice alignment
Part III: Best Practices in AI Operationalization and Model Lifecycle
Takeaways: ML Model Lifecycle Overview Team iteration practices
Who is this Team Workshop for?
This workshop is for teams of five or more, for a wide range of roles and responsibilities including:
- Executives & Decision-Makers
- Compliance teams
- AI Governance teams
- AI Centers of Excellence
- General Counsel & corporate law
- Data science teams
- Project Managers
- Product Managers
- Capability Managers
- Acquisitions Managers
- Procurement & Contracting Officers
- Developers
- AI & ML engineers
- Quality Assurance teams
- Research Analysts
- Data engineering teams
- Knowledge Operations Managers
- Information Technicians
Live Trainer: In-Person or Virtual Options
Cognilytica workshops are facilitated by a live trainer, offered in either on-site, in-person option or virtual, online workshops depending on needs, schedule, budget, and availability. Inquire for details and enrollment options.
Real Teams at Real Organizations getting Real Value
What is included in this workshop
- Workshops offer a combination of instructor-led, guided instruction as well as facilitated team interaction, all customized to your specific needs and for your industry.
- Key AI Failure Reasons Cheatsheet
- CPMAI Methodology Overview Handout
- ML Model Lifecycle Overview
- Identification of current or potential AI Roadblocks
- Identification of team project management practices and alignment with AI best practices
- Current team iteration practices and alignment with MLOps and DataOps best practices
- Licensable for self-paced replay and viewing
Internal Training
-
Questionably vendor neutral
-
Probably not leveraging expertise across a wide range of projects
-
One-off customization
-
Possibly interactive
-
Not regularly updated
-
Possibly team focused
-
Unknown instructor models
Academic Courses
-
Questionably vendor neutral
-
Probably not leveraging expertise across a wide range of projects
-
Not customizable
-
Possibly interactive
-
Not regularly updated
-
Not team focused
-
Limited instructor models
Cognillytica
-
Vendor-neutral
-
Facilitated by industry experts Leveraging experience from thousands of real-world AI projects
-
Customizable
-
Highly Interactive with Team engagement
-
Regularly updated
-
Team Focused
-
Live trainer with virtual and in-person options
Vendor Training
-
Not vendor neutral
-
Probably not leveraging expertise across a wide range of projects
-
Not customizable
-
Possibly interactive
-
Questionably updated
-
Questionably team focused
-
Limited instructor models
Self-paced Online Training
-
Questionably vendor neutral
-
Probably not leveraging expertise across a wide range of projects
-
Not customizable
-
Not interactive
-
Not regularly updated
-
Not team focused
-
Limited instructor models
