Final Exam: AI Architect Overview/Description Expected Duration Lesson Objectives Course Number Expertise Level Overview/Description Final Exam: AI Architect will test your knowledge and application of the topics presented throughout the AI Architect track of the Skillsoft Aspire AI Apprentice to AI Architect Journey.
Expected Duration (hours)
0.0
Lesson Objectives Final Exam: AI Architect
compare/contrast ai technologies, frameworks, and platforms
contrast AI Enterprise Planning with IT Enterprise Planning with plain Enterprise Planning
contrast and compare AI applications in different industries
define ai architect work
define the ai architect role
describe AI libraries and their advantages/disadvantages
describe AI platforms and their advantages/disadvantages
describe anti-patterns commonly found in AI architectures
describe axiomatic attribution at a high level
describe differences between tactical, strategic, and tactical-strategic planning
describe explainable ai and its significance
describe feature visualization at a high level
describe how AI Accelerators reduce the complexity of projects while shortening their timelines
describe how Closed-Loop Intelligence pattern works
describe how Daisy Architecture pattern works
describe how DASE pattern works
describe how Data Lake pattern works
describe how DBLMLM pattern works
describe how Federated Learning pattern works
describe how GRA pattern works
describe how Kappa & Lambda Architecture patterns work
describe how Microservices and EDMLM patterns work
describe how the ai architect interacts with different groups in the enterprise
describe intelligible models at a high level
describe monotonicity at a high level
describe pre-trained models and their advantages/disadvantages
describe rationalization at a high level
describe "right to explanation" regulations
describe the cntk framework and its advantages/disadvantages
describe the counterfactual method at a high level
describe the interpretability problem and its importance
describe the Keras framework and its advantages/disadvantages
describe the mxnet framework and its advantages/disadvantages
describe the pytorch framework and its advantages/disadvantages
describe the theano framework and its advantages/disadvantages
describe three standard AI applications in manufacturing
describe three standard AI applications in marketing
describe three standard AI applications in sales
describe three standard AI applications in the cybersecurity industry
describe three standard AI applications in the financial industry
describe three standard AI applications in the pharmaceutical industry
describe three standard AI applications in the telecommunications industry
describe three standard AI applications in the transportation industry
describe three standard AI applications in the utility industry
describe what is a Discovery Map, its sections, and its role in AI Enterprise Planning
describe what organizations the ai architect participates in as a member
discover what is an AI Accelerator, and identify some of the main AI accelerators
explain differences between ai architect role and other its roles
identify AI platforms and their advantages/disadvantages
identify and contrast AI Architecture and Design Patterns
identify artificial intelligence data set types
identify how Current Projects vs AI Accelerators Dependency Maps are used to create an AI Enterprise Roadmap
identify how Current Projects vs. AI Accelerators Dependency Maps are used to create an AI Enterprise Roadmap
identify the relationships between the AI Maturity Model, AI Maturity Assessment, tools, metrics % KPIs, and Analytic Dashboards
identify three standard AI applications in the pharmaceutical industry
recognize differences between tactical, strategic, and tactical-strategic planning
recognize rationalization at a high level
recognize the importance of Complexity vs. Business Value plots in AI Enterprise Planning
recognize the relationships between the AI Maturity Model, AI Maturity Assessment, tools, metrics % KPIs, and Analytic Dashboards
which Patterns are used in each AI Development Phase
Course Number: it_feaia_04_enus
Expertise Level
Intermediate