Applying CRISP-DM and CPMAI to AI and Big Data Projects In our digital age, where data is as vast as the ocean, every business, big or small, is trying to ride the wave of artificial intelligence (AI) and big data. These big tech movements, AI & big data, are not just buzzwords; they’re revolutionizing how […]
How to apply CRISP-DM to AI and big data projects Read More »
Up to 80% of AI projects are failing, even in tech-forward firms that have implemented Agile in its most advanced and deepest forms. So, does Agile work for Machine learning?
Does Agile Work for Machine Learning? Read More »
In the fast-paced world of AI and data projects, choosing the right project management method is like picking the right tool for a space mission – it can make or break the journey.
Why Can’t I use Agile or CRISP-DM to manage AI and Data Projects? Read More »
CPMAI Certification: A Strategic Investment in Your Future
The ROI of a CPMAI Certification Read More »
Why do so many AI projects crash and burn?
Top 10 Reasons Why AI Projects Fail Read More »
Earn PDUs for PMP and other PMI Certifications with CPMAI training and certification.
How can I earn PDUs for PMP and other PMI Certifications with CPMAI? Read More »
CPMAI certification is your key to mastering AI project management
How does a CPMAI certification enhance my career? Read More »
Is the CPMAI Certification Worth it? We might be slightly biased, but the answer is absolutely, resoundingly YES! What is CPMAI? A Game-Changer in AI Management The Cognitive Project Management for AI (CPMAI) methodology certification is a vendor-neutral, data-centric, AI-specific, iterative methodology for running and managing AI, ML, and cognitive technology projects. The CPMAI methodology
Is the CPMAI Certification worth it? Read More »
AI & Data Best Practices The Cognitive Project Management for AI (CPMAI) methodology provides a step by step approach to AI project management that has seen significant success in enterprise, government, academic, consulting, and technology vendor implementations. This infographic provides an overview of CPMAI at a glance, but read below for more details. What
Cognitive Project Management for AI – CPMAI At a Glance [Infographic] Read More »
Simplifying ML Project Management for Maximum Success Are you in charge of a machine learning project and don’t know where to start? Or maybe you know some of the technology for making machine learning work, but don’t know the steps you should follow to optimize your chances of success. Leading a Machine Learning (ML) project
The Steps for a Machine Learning Project Read More »
How do you plan an AI project?
Infographic: The Steps for an AI Project Read More »
What are the steps in an AI project?
A Step-by-Step Approach to Running AI and Machine Learning Projects Read More »
Vendor-neutral, data-centric, AI-specific, Iterative methodology for running and managing AI, ML, and cognitive technology projects
What is the Cognitive Project Management for AI (CPMAI) Methodology? Read More »
Machine learning training data is not always readily available. In many cases, “ground truth” data is unavailable, can be difficult to collect, or is considered private, making its use difficult. Synthetic data is often used in scenarios where there isn’t sufficient training data available to be used in machine learning algorithms, especially in supervised machine
Synthetic Data Generation Market: Research Snapshot Feb. 2022 Read More »
In order for machine learning systems to be able to create accurate generalizations, they must be trained on data. Advanced forms of machine learning, especially deep learning neural networks, require significant volumes of data to be able to create models with desired levels of accuracy. Customers often don’t have the resources to label large data
Data Labeling Market: Research Snapshot Dec. 2021 Read More »
The topic of ethics and responsibility comes up frequently in the context of Artificial Intelligence, and for good reason. The concept of intelligent machines is scary to many people. Others are just as concerned about the increasing power that humans have over data and the potential for malicious use of powerful technology. To address these
State of Ethical AI Frameworks Read More »
The pace of adoption for artificial intelligence (AI) and cognitive technologies continues unabated with widespread, worldwide, rapid adoption of AI and its various patterns. As a result, governments around the world are moving quickly to ensure that existing laws, regulations, and legal constructs remain relevant in the face of technology change and can deal with
Worldwide AI Laws and Regulations 2021 Read More »
The race for competitive advantage in artificial intelligence (AI) is not just the domain of companies and organizations. Countries vie with each other for dominance in the space of AI, seeking competitive advantages for their industrial ecosystem, military and defense, academic institutions, and areas of private and public sector enterprise. Countries that can take advantage
Worldwide AI Country Strategies and Competitiveness 2021 Read More »
Document ID: CGR-MLP20 | Last Updated: Dec. 8, 2020 Machine learning systems are core to enabling each of the seven patterns of AI. Machine learning platforms facilitate and accelerate the development of machine learning models by providing functionality that combines many necessary activities for model development and deployment. In this report, Cognilytica evaluates five major categories
Machine Learning Platforms 2020 Read More »
Document ID: CGR-DIG20 | Last Updated: Jun. 23, 2020 Digitization focuses on the capturing and extraction of value from non-digital data by converting it to a usable digital state. Digitalization expands upon the idea of digitization by addressing human-bound processes and processes that have previously been dependent on non-digital information. Digitization and Digitalization are seen
Digitization and Digitalization Report (June 2020) Read More »
The pace of worldwide adoption continues to accelerate. Not only are companies and researchers competing with each other for advantage in the world of artificial intelligence and machine learning, so too are entire countries. In this podcast, Cognilytica analysts Kathleen Walch and Ronald Schmelzer go over research on how different countries are approaching AI and
AI Today Podcast #131: AI Around the World: Strategies, Laws, and Regulations (2020) Read More »
Document ID: CGR-MOM20 | Last Updated: Mar. 11, 2020 As the markets for AI shift from those organizations that have the technical expertise required to build models from scratch to those enterprises and organizations looking to consume models built by others, the focus shifts from tooling and platforms focused solely on model development to tools
ML Model Management and Operations 2020 (“MLOps”) Read More »
While the Robotic Process Automation (RPA) market is heating up, there are many that say that RPA is not AI per se, but rather a gateway on the path to cognitive automation. Automation Anywhere certainly agrees with this perspective, and in this interview, Manish Rai, VP of Product Marketing at Automation Anywhere shares insights into
Document ID: CGR-REG20 | Last Updated: Feb. 20, 2020 The pace of adoption for artificial intelligence (AI) and cognitive technologies continues unabated with widespread, worldwide, rapid adoption of AI and its various patterns. As a result, governments around the world are moving quickly to ensure that existing laws, regulations, and legal constructs remain relevant in
Worldwide AI Laws and Regulations 2020 Read More »
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