AI & Data Best Practices

What is the CPMAI Certification?

The Cognitive Project Management for AI (CPMAI) methodology is the most widely adopted, vendor-neutral approach to running AI projects. Individuals and organizations are not just learning this approach, but also getting certified to run their AI projects successfully. So, what is the CPMAI certification and how can you put it into practice in your organization today?

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Many organizations that are running AI and advanced data projects are experiencing an alarmly high rate of failure for these projects. According to some estimates, up to 80% of AI projects fail. With all the great technology we have and with so many highly trained developers and data professionals, why do we have such a high rate of failure? Most often, it’s not the technology or the people that are making AI projects fail, but rather the failure to follow a successful, repeatable process. In doing your research on AI projects, you probably stumbled across the CPMAI methodology as a way to run AI projects successfully. The Cognitive Project Management for AI (CPMAI) methodology is the most widely adopted, vendor-neutral approach to running AI projects. Individuals and organizations are not just learning this approach, but also getting certified to run their AI projects successfully. So, what is the CPMAI certification and how can you put it into practice in your organization today?

Many organizations have planned on technology and talent investment, but have failed to invest in the right approach to run their AI projects. Many organizations are either implementing poorly fitting application-centric development approaches, generic project management styles, or even “scientific methodology” to run their AI projects. Or worse, they are using no methodology at all and running AI projects in an ad-hoc manner based on whatever is most important at the time. The lack of good methodology and process is the reason why so many AI projects fail. Without knowing the key problems to address, knowing the availability of data to solve those issues, applicability of AI to solve the main problems, organizations will fail in their AI efforts.

The proven, iterative method by which to reliably run AI projects with a high degree of success is the Cognitive Project Management for AI (CPMAI) methodology. Thousands of organizations have adopted this robust, iterative, agile method over the past decade to run their AI projects with high rates of success. The CPMAI methodology is built upon the well-established, data centric CRISP-DM, and incorporates best-practices agile approaches for short, iterative sprints for projects that serves as a X AI Project approach and guideline to run your AI and machine learning projects.

What is the CPMAI Certification?

The Cognitive Project Management for AI (CPMAI) methodology is a vendor-neutral, data-centric, AI-specific, Agile methodology for AI projects and advanced data projects. The CPMAI  AI project methodology borrows and extends upon previous methodologies and approaches for project management, such as Agile Methodology and CRISP-DM, which have both pioneered methods for running large, complex, and constantly changing projects that can respond to continuous needs while also focusing on the data-centric aspects of those projects. 

CPMAI provides needed enhancements to Agile and CRISP-DM methodologies to meet AI-specific requirements. By extending these methodologies rather than creating a new approach, CPMAI can be implemented in organizations with already-running Agile teams and already established data organizations. Introducing something new and foreign is a sure way to get resistance. So the key is to provide a blended approach that simultaneously delivers the expected results to the organization as well as provide a framework for continued iterative development at the lowest risk possible. Cognilytica offers CPMAI certification to individuals and organizations that want to ground their process in best practices for AI project management.

What are the steps involved in Cognitive Project Management for AI (CPMAI)?

The CPMAI methodology consists of six phases that are followed on each project iteration. Each of these CPMAI phases are iterative with each other and allows for progression backwards or forwards during AI project development depending on the need and challenges met in the real world. This methodology provides a solid project structure to follow. You can see a high level overview of the six primary CPMAI Phases and their objectives below:

What is the CPMAI Certification? An Overview of CPMAI

Source: Cognilytica

Let’s learn more about what the CPMAI certification is all about.

CPMAI Phase I: Business Understanding

The first step of CPMAI methodology for any AI project is  gathering an understanding of the business requirements and understanding the business needs. After all, if you’re not solving a real business problem for your organization then why are you even doing the project at all? In phase one of your project iteration you should focus on understanding the project objectives and requirements from a business perspective, then converting this knowledge into an AI and ML problem definition and a preliminary plan designed to achieve the objectives. 

CPMAI Phase II: Data Understanding

The second phase of CPMAI methodology for any AI project is understanding your data. The most important part here is understanding what data is required to address the business problem, whether or not that data is available, and what format(s) your data is in. Data is what fuels your AI projects so you should make sure you have a firm understanding of your data before getting too far along in your project.

CPMAI Phase III: Data Preparation 

The third step of the CPMAI approach to AI project management is Data Preparation. Once you have figured out what problem you are solving and what data you have, next you need to make sure the data you have is usable for your project. In this step you need to do tasks such as data cleansing, data aggregation, data augmentation, data labeling, data normalization, data transformation and any other activities for data of structured, unstructured, and semi-structured nature. 

CPMAI Phase IV: Model Development 

Once we have a business understanding that lends to a data understanding and the data prepared with pipelines in place, we can finally get to AI model development. In CPMAI, the fourth step of your AI project is the creation and development of machine learning models and supporting AI system artifacts. This includes model technique selection and application, model training, model hyperparameter setting and adjustment, model validation, ensemble model development and testing, algorithm selection, and model optimization. By the time you are ready to build your very first model you’ve already determined the business needs, the data requirements, and gotten the data in the right format and quality. If you haven’t, then you need to revisit these steps before moving forward.

CPMAI Phase V: Model Evaluation 

Developing the model is not the end point but rather the midpoint in a CPMAI iteration. Once a model is created, it needs to be evaluated to make sure it performs according to the business requirements and other factors set in the previous steps of a machine learning project. In this fifth phase of CPMAI, you are now ready for model evaluation. From an AI perspective this includes model metric evaluation, model precision and accuracy, determination of false positive and negative rates, key performance indicator metrics, model performance metrics, model quality measurements, and a determination as to whether or not the model is suitable for meeting the goals or whether earlier phases should be iterated upon to reach those goals.

Phase VI: Model Operationalization

The sixth step of each AI project lifecycle iteration is putting the model you just created into operation and monitoring, managing, and iterating those AI systems to keep the relevant and providing business value as defined in the first phase. CPMAI Phase VI makes sure to address model versioning and iteration, model deployment, model monitoring, model staging in development and production environments, and other aspects of getting the model in a position to provide value to meet the stated purpose. 

What is CPMAI Certification? A Third-Party Endorsement of AI Project Best Practices.

Knowing what is the CPMAI certification and the basics of the CPMAI methodology is not sufficient to putting those best practices into use for your AI and advanced data projects. Cognilytica CPMAI training and certification not only provides the fundamental training in the CPMAI process, but also provides the supporting materials, templates, and workbooks to put CPMAI into practice. The CPMAI certification also provides the necessary third-party endorsement and credential needed to prove to potential employers, managers, and peers. Some of the reasons why organizations choose CPMAI:

  • CPMAI is vendor-neutral. Unlike AI project management approaches from technology or other third party vendors, CPMAI provides a scalable, proven approach that supports technology solutions across the diverse ecosystem. Evolving from hundreds of real-world implementations, CPMAI methodology is optimized for the delivery of in-production, high value, successful AI projects. 
  • CPMAI is data-centric. The CPMAI methodology is best practices based, proven in real-world adoption and based on well respected CRISP-DM and Agile. 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.
  • CPMAI is not theory. Get success on your very first project iteration with tangible results by following a proven method. Get certified in CPMAI today and add the fastest growing, most valuable AI project management certification to your resume and skillset today. With several thousand CPMAI certified and hundreds of organizations looking to hire skilled AI Project managers, the CPMAI certification provides a tangible increase in skills, pay, and contract value to those who have the latest CPMAI certification. 
  • Make Yourself More Competitive. Learn Established Best Practices for AI & Data Project Management that helps anyone involved in project and product management for AI and Data Science. Get a foundation in AI & Data Understanding. Leverage & extend your existing Certifications and skills. The CPMAI certification is trusted, well respected, high value, and provides a high ROI, providing a way to earn a project management certification in AI. To join the quickly growing  community and build this sought after skill set, become a certified project manager in AI with CPMAI!

Frequently Asked Questions about what the CPMAI Certification is About

Still on the fence about whether or not CPMAI certification is right for you? Here’s answers to some commonly asked questions we get about CPMAI.

  1. How long does certification take?
    1. The CPMAI Training and Certification contains around 27 hours of self-paced online instruction plus an additional 16 sets of exercises with at least 20 questions each that are automatically scored. You have up to six months to complete the certification, but many complete the training and certification process in just a few weeks.
  2. How Much Does CPMAI Training & Certification Cost?
    1. The CPMAI Certification is bundled with the Self-Paced, Online Training, which is just $2495 per person. Additional discounts are available for group registrations of five (5) or more individuals who all register and pay at the same time.
  3. Do I need to know anything about or have prior knowledge in AI or data for the CPMAI Training and Certification?
    1. The CPMAI Training and Certification doesn’t require any prior knowledge or experience on AI or advanced data. The CPMAI training and certification course starts with a foundation of AI, foundation of data science.
  4. Can I get my employer to reimburse me for CPMAI training and certification?
    1. Many organizations offer employee skilling and training benefits that may cover your CPMAI certification and training costs as part of their tuition reimbursement or continuing education benefits. 
  5. How long is the CPMAI certification valid?
    1. Once you are CPMAI certified, your certification is valid for three years during and after which it may be maintained by keeping active your CPMAI Community membership and by upgrading to the latest version of CPMAI Certification and/or completing at least forty (40) Continuing Education Units (CEUs).
  6. How is CPMAI delivered?
    1. Cognilytica training is offered primarily as a virtual, self-paced solution that is constantly updated to meet growing and dynamic needs of the market. Cognilytica also offers live instructor models in both virtual and in-person modes depending on needs, schedule, budget, and availability. Inquire for details and required minimum enrollment for live instructor options.

Getting CPMAI certified

Cognilytica’s CPMAI training and certification provides not only the AI and big data project management training needed for organizations, but also the fundamental understanding of key AI, machine learning, and big data concepts that organizations usually get from other AI and big data project management courses. Earning your CPMAI certification will not only help you better manage AI projects but will also enhance your career, potentially increase your pay, and make you more competitive. CPMAI is the fastest growing certification for running and managing AI projects, with 220% annual growth rate. Learn more about CPMAI and this quickly growing community of AI and data project management professionals and be able to put the CPMAI Methodology into practice. Get CPMAI Certified today!

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