Develop, train, test, and operationalize AI and machine learning models. Covers the full AI/ML lifecycle from data preparation to production deployment.
Not completed
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Develop, train, test, and operationalize AI and machine learning models. Covers the full AI/ML lifecycle from data preparation to production deployment.
AI Development and Engineering is core for Administrators, Developers, and Architects.
Completed
Build, configure, and customize AI agents using platforms like Agentforce, Microsoft Copilot Studio, and agent frameworks. Includes defining agent actions, topics, instructions, and guardrails.
Completed
Develop responsible AI systems that are transparent, fair, and aligned with human values. Covers bias mitigation, data sovereignty, AI safety standards, and regulatory compliance.
Not completed
Understand how AI agents and generative AI systems work — including their capabilities, limitations, and appropriate use in the workplace. Enables every professional to engage confidently with AI-powered tools and agentic workflows.
Completed
Design and deploy AI systems that act autonomously to complete multi-step tasks, make decisions, and adapt to changing inputs. Includes building single and multi-agent architectures on platforms like Agentforce.
Completed
Work with large language models (LLMs) and generative AI tools to produce content, code, insights, and experiences. Includes prompt engineering, RAG patterns, and fine-tuning workflows.
Not completed
Work effectively alongside AI agents — knowing when to delegate tasks, how to evaluate agent outputs, and when human judgment is required. Covers teaming patterns, trust calibration, and optimizing human-agent workflows.
Not completed
Build systems that understand and generate language, speech, and visual data. Covers NLP fundamentals through to advanced deep learning models for text and image tasks.