ARTiBA’s credentialing mechanism is hinged to the ARTiBA Ai & ML Design & Engineering Excellence Framework (AMDEX™ Release: 15-1.3) and the AiE™ certification exam is aligned to the AMDEX™.
The AMDEX™ standards enunciate knowledge-level specifications for the 22 components of the five AMDEX™ prongs. The exam curriculum of the AiE™ certification exam is fleshed along these components, and is revised and updated periodically in terms of inclusions of technologies, applications, emerging concepts and developer skills and practices.
AMDEX™1.3/1: ESSENTIALS OF ARTIFICAL INTELLIGENCE & MACHINE LEARNING
Spec Level
Essential Concepts, Principles, Frameworks & Ecosystem of Machine Learning & Artificial Intelligence
Expert Level
Essential Concepts, Principles, Frameworks, Applications and Tools of Supervised Learning
Expert Level
Essential Concepts, Principles, Frameworks, Applications and Tools of Ensemble Learning
Expert Level
Essential Concepts, Principles, Frameworks, Applications and Tools of Unsupervised Learning
Expert Level
AMDEX™1.3/2: ESSENTIALS OF Ai & ML PROGRAMMING
Spec Level
Essential Concepts, Principles, Practical Frameworks and Methods of Developing Recommender Systems
Expert Level
Essential Concepts, Principles, and Methods of Logic Programming for Ai/ ML Applications
Moderate Level
Principles, techniques and tools for Developing Heuristic Search Applications
Basic Level
Essential Concepts, Principles and Practical Frameworks in Genetic Algorithms
Basic Level
Essentials of Developing Games and other Applications with Ai
Expert Level
AMDEX™1.3/3: ESSENTIALS OF NATURAL LANGUAGE PROCESSING
Spec Level
Essential Concepts, Principles, Theories and Frameworks in Natural Language Processing
Expert Level
Essential Techniques, Tools & Platforms for NLP Development & Applications
Expert Level
Application of Probabilistic Reasoning for Sequential Data Analysis
Expert Level
Essential Concepts, Principles, Theories and Frameworks in Speech Recognition
Moderate Level
Essential Concepts, Principles, Theories and Frameworks in Object Detection and Tracking
Expert Level
AMDEX™1.3/4: ESSENTIALS OF NEURAL NETWORKS & DEEP LEARNING
Spec Level
Essential Concepts, Principles, Types & Frameworks in Neural Networks
Expert Level
Important platforms, techniques and tools of Neural Network applications
Moderate Level
Principles and practical frameworks of Reinforcement Learning Through Neural Networks
Moderate Level
Essential Concepts, Principles, Frameworks & Applications of Deep Learning
Moderate Level
AMDEX™1.3/5: Ai/ ML PROFESSIONS, WORKPLACE & CAREER ENVIRONMENT
Spec Level
The Roles in Ai/ ML Engineering Careers
Moderate Level
The Responsibilities/ KRAs of Ai/ ML Engineers
Expert Level
The Growth in Ai/ ML Engineering Careers
Moderate Level
The profession of Ai/ ML Science
Moderate Level
The Infographic: This ARTiBA theoretical framework lists 6 chief approach-linked factors that demand the attention of Ai engineers to ensure positive innovation and results oriented Ai performance metrics. These 6 factors have been selected out of a list of two dozen to build the DSM hypothesis using a combination of Delphi and other qualitative and quantitative methods. The next round of validation tests for this model is expected to get underway by the middle of 2019.
Enterprise and Social Ai implementation projects face severe lack of competent engineers with strong fundamental understanding of concepts like CNNs, Fuzzy Logic and Semantic Analysis, who can simultaneously create endpoint solutions. Machine learning and neural networks expertise in large scale computing environments are must have skills today. AMDEX™ framework for excellence in Ai/ ML design and engineering covers every major application function, while being enterprise solution centric for the complete and proficient Ai professional.
The AMDEX™ framework is an industry best-practices based solution created by the Artificial Intelligence Board of America after analyzing the design, engineering and prototyping of use cases spanning almost 70 companies across 9 countries.
Aspiring and forward thinking Ai professionals of today choose the ARTiBA knowledge framework for its even as every industry continues to step into an Ai driven world.
The AMDEX™ Release: 15-1.3 knowledge framework is the model encompassing technologies and practices for machine learning and artificial intelligence professionals across elements of deep learning, unsupervised learning, advanced neural networks, heuristics and sophisticated self-training algorithms and structures. The ARTiBA credentialing philosophy The AiE™ credential provides insights into the aspiring Ai professional’s abilities to handle Ai implementation initiatives in diverse business environments because this is what separates true technology innovators from the rest.
Artificial Intelligence is driving the world. ARTiBA certified professionals earn prestige because their specialized credential proves they can work across industries, sectors and organizations of all types. Qualifications and credentials that are not dependent on a specific technology platform, company or sector have much wider acceptability among recruiters, and are valuable for professionals as they can be deployed across any vertical or project.
ARTiBA credentials undergo strict and regular upgradation and rationalization of standards, policies, and content to retain their key feature of industry and sector independence. The AMDEX™ framework along with the ARTiBA certification exam curricula are regularly adjusted and calibrated on knowledge areas, assessment mechanisms, and current global job role-needs in the Ai space internationally.
The CRIC, formed of leading international researchers, thinkers, practitioners, and influencers in the domain of Artificial Intelligence, Machine Learning, Deep Learning and others, validates and accredits all ARTiBA frameworks, standards, credentialing policies, and certification exam structure and procedures. CRIC’s quarterly audits, reviews, and recommendations help keep the ARTiBA knowledge body on Artificial Intelligence current, updated, and marked-to-industry.
CRIC's chief responsibilities include spearheading research on emerging and disruptive Artificial Intelligence trends and impacts on professional knowledge requirements; industry validation of ARTiBA credentials and related exam-curricula, prescribing and advising on training and content-related issues.
The ARTiBA credential remains constantly and comprehensively marked-to-industry. CRIC’s ongoing research tabs trend in six major global business geographies, sweeping across continents, economies, and geo-political zones. The AMDEX™ framework as well as the ARTiBA certification exam curricula undergo regular updates, upgrades, and adjustments in terms of knowledge areas covered; assessment mechanisms deployed; and professional role-needs addressed to ensure ARTiBA stays mapped to the skills, knowledge and talent needs of the artificial intelligence and machine learning community internationally. CRIC manages the ARTiBA credentialing ecosystem deploying three principles of pan-vertical, cross-sectoral relevance & suitability; geographic-independence & worldwide applicability and cross-platform vendor-neutrality of developer skills.
While cross-platform vendor neutrality of the knowledge framework ensures that ARTiBA validates and credentials professional capacities, promise, and potential of professionals without any bias for specific technology platforms or digital solution vendors; the pan-vertical, cross-sectoral relevance & suitability principle assures industry that the ARTiBA credentialed professional can work equally well across the product-service, business-social, and SME-large divides. The fact that ARTiBA credentials are available around the world punctuates the geographic-independence & pan-world applicability advantage for professionals and companies in every region.
