Achieve Success with Best Practices

Cognitive Project Management for AI

Vendor-Neutral Best-Practice Methodology for Data Analytics & Cognitive Projects of Any Size

CPMAI

Methodology for Implementing AI & Advanced Analytics Projects Successfully.

There’s no excuse for failing AI projects. If you’re not successful, you’re not doing it right.

Why CPMAI WORKS So Well

Vendor-Neutral

Methodology that supports technology solutions across the diverse ecosystem

Data-Centric

AI and advanced analytics are not about systems or functionality - they're about extracting value from data.

Iterative and Agile

Established data methodologies updated with today's modern agile approaches

Best Practices Based

Proven over and over in real-world adoption. Learn from other's successes and failures.

Cognitive Technology & Advanced Analytics

CPMAI Methodology is focused on extracting big value from big data

REAL-WORLD PROVEN

CPMAI is not theory. Get success on your very first project iteration with tangible results by following a proven method.

The Core Elements of CPMAI Methodology

Best Practices Based

CPMAI extends the well-known CRISP-DM methodology with AI and ML specific documents, processes, and tasks. The CPMAI methodology also incorporates the latest practices in Agile Methodologies and adds additional DataOps activities that aim to make CPMAI data-first, AI-relevant, highly iterative, and focused on the right tasks for operational success. The CPMAI methodology is composed of six primary phases listed below:

PhAse I: Business Understanding

Meeting business objectives

Assessment of Business Situation

Cognitive Requirements

AI Go / No Go

AI Pattern Identification

Agile Release Planning

AI Project Success Criteria

Transparency Requirements & Responsible AI

And other criteria for business success

Phase II: Data Understanding

Identifying critical data needs

What are the core data requirements and objectives?

Where are the AI-relevant data sources?

What type of data is available for AI projects to meet business objectives?

What is the quality of the available data?

How can we iterate data needs for success?

PhAse III: Data Preparation

Data Quality is kEy

Methods to clean, prune, shape, and optimize data to make modeling more accurate and effective

Provide transparency for methods of data selection, pruning, feature enhancement

Creation of data pipelines

Assuring data quality throughout the model lifecycle

PhAse IV: Model Development

extracting value from data

Modeling Techniques

Algorithm selection

Modeling Assumptions

Generate Test Design

Model development and iteration

Model optimization techniques

Phase V: Model Evaluation

Making Sure models actually work

Does the model really work against real-world data?

Is the model right/wrong in acceptable ways?

Model evaluation considerations

Real-world model testing and validation

Model iteration and improvement

Business KPI Evaluation

Phase VI: Model Operationalization

Putting Models & Analytics to Work

Putting AI models into operation

The inference phase of AI

Model deployment configuration

Real-world model monitoring and management

AI operationalization considerations

Continuous model management and evaluation

Real People and Real Organizations getting Real Value

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Improvement in iteration time

International CPG company adopted CPMAI methodology, cutting model iteration time from 3 months to 4 weeks per iteration cycle

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Project costs saved

Large retailer implemented CPMAI utilizing actionable AI project success criteria to save over $6M in AI project costs

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Time reduction

International telecommunications company put into practice CPMAI, standardizing data project efforts, and reducing data identification and selection time by 60%

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Project Speed improvement

Large healthcare provider adopted CPMAI for AI and data analytics project and it provided 8x project speed improvement

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Single project ROI

International construction firm realized over $2.5M in saved data preparation and engineering costs through CPMAI adoption

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Improvement in compliance

Large financial institution adopted CPMAI and increased compliance and reduced risk by 500%

"Cognilytica’s CPMAI training has had a direct and immediate impact on our team. The course gave an excellent overview of the fundamentals of AI as well as provided a common framework for our teams to communicate and collaborate in the management of AI projects. I'm a believer and champion of the value of this training. I am already seeing the value and impact of the knowledge and insight gained by my direct reports. I recommend this course for anyone who is looking to train or upskill their workforce around AI project management efforts."

“Cognilytica’s CPMAI project management training and certification is a very effective program for executives and managers. Having taken this training early on in our AI journey, it provided me with a solid framework needed to successfully guide and provide meaningful oversight to our AI and ML projects. Ron and Kathleen clearly illustrated their strong fundamentals in understanding AI & machine learning with a big emphasis on how to do AI projects right. This training and certification is a must for anyone looking to run and manage AI projects of any size.”

"The CPMAI methodology and Cognilytica's AI & ML training verbal and visual aspect of the training were very informative and definitely kept my attention. The exercises helped to re-emphasize what we learned in the class, rendering them very useful."

"Our management team, consisting of clinical science, data science, data engineering, software and IT ops team leads, completed Cognilytica’s CPMAI certification. The training has put us in a good position to launch several projects in the next month. I would highly recommend the course for those seeking to rapidly accelerate their knowledge and readiness to successfully manage AI projects."

"The CPMAI methodology and Cognilytica’s training helped break down the fundamentals of AI including various algorithms, why deep learning has become so popular, and why data is at the heart of AI. I highly recommend this training for anyone looking to learn how to do AI right, and who wants to learn the data and AI centric CPMAI methodology."

"Cognilytica’s AI & ML project management training provided me with a deep dive into the fundamentals of AI & machine learning with a brief exposure to project management. This course is a must attend for someone who has little/no prior experience on AI – and is interested to learn an overview of AI, how to do AI right, bring his/her knowledge to the next level, and gain in-depth knowledge into some of the major algorithms of supervised, unsupervised, & reinforced learning approaches to AI & ML"

Why are you Running your most important DATA projects without a standard methodology in place?

Cognilytica has worked with dozens of organizations and through hundreds of real-world implementations to develop a methodology optimized for the deliver of in-production, high value, low risk AI projects. The Cognitive Project Management for AI Methodology leverages decades of real-world methodology experience for running big data projects combined with the latest best practices expertise learned from running real-world AI projects. AI and big data project failure is rooted in a failure of planning, expectation setting, and establishment of practices.