ARTiBA Standards | AI Knowledge Framework

ARTiBA AI-BoK™ & AMDEX™

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™.

AMDEX™ Ver.15-1.3

The AMDEX™ Release: 15-1.3 is the knowledge standards framework for engineering and application development roles in Artificial Intelligence and Machine Learning. The AMDEX™ Release: 15-1.3 guides the development of learning curricula and content for academic and professional learning programs in Ai engineering.
Exam for the AiE™ certification carries questions from the knowledge areas and topics etc., contained in the latest version of AMDEX™ Ver.15-1.3. In all, nearly 100 critical subject areas are covered in ARTiBA exams, covering all aspects of Ai engineering and application development.

AMDEX™ Release: 15-1.3

01 AMDEX™ Release: 15-1.3 knowledge framework is entirely research-curated professional excellence assurance model in Ai engineering today. It enunciates the architecture of essential knowledge areas for Ai engineers and technologists.
02 The AMDEX™ framework standardizes what Ai engineers need to know, understand, and be able to do as AiE™ credential holders. It serves as a benchmark for training companies, workforce development agencies, higher-education institutions, universities, business schools, and colleges around the world to calibrate their Ai learning and training programs.
03 Benchmarked to the market and industry needs, the AMDEX™ Release: 15-1.3 serves as a reliable global standards-framework for helping follow an open-source learning approach that encourages engineers go multiplatform and vendor-neutral in their skillsets and knowledge.

Knowledge Specifications – AMDEX™ 15-1.3

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

Standards Control & Management

ARTiBA Credentialing Research & Innovation Committee -

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.
Applicability & Relevance +

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.