Mulțumim pentru trimiterea solicitării! Un membru al echipei noastre vă va contacta în curând.
Mulțumim pentru trimiterea rezervării! Un membru al echipei noastre vă va contacta în curând.
Schița de curs
Module 1: Introduction & AI Theory
- The Model-Based Approach: AI as an engineering problem.
- Demystifying the "Ghost in the Machine": What AI is vs. what it is not.
- The Evolution of Tech: From BERT to Transformers.
- Generative Domains: Analysis, Creative, Research, Image, Music, and Video.
- Data Governance: Pillars, audits, and the research trends (Multimodality, Agents, RAG, LLM vs. SLM).
- The Dark Side: Ethics, IP, bias, hallucinations, and social engineering.
- Risk Assessment: Data poisoning, Nepenthes, and the risk of "dumbing down" human talent.
- Model Taxonomy: Foundation vs. Task-specific; Closed vs. Open-weight models.
Module 2: Current Landscape & Toolset
- The Language Models Arena: Comparing performance and benchmarks.
- Professional Purchase Criteria: Cost, latency, privacy, and vendor lock-in.
- Big Models Overview: OpenAI ChatGPT, Perplexity, Gemini, and Grok.
- Niche & Small Models: Manus, SpecKit.
- Graphical Generation: Perchance
- Technical Constraints: Context rot vs. Token cost.
Module 3: Interaction - Prompt & Context Engineering
- The Verification Framework: Completeness, consistency, and verifiability.
- The RAG Strategy: When to use Retrieval-Augmented Generation vs. fine-tuning.
- ROI of AI: Maintenance costs vs. productivity gains.
- Advanced Techniques: 20+ Prompt & RAG methods with real-world examples.
- Experimental Frontiers: Triangulation, Map & Terrain overview, and Model-based generation.
Module 4: AI in Agile Project Management
- The Supercomputer Pilot: AI as an automation engine.
- Decision Making: Human responsibility vs. AI assistance.
- AIOps & GitOps: Integrating AI into the operational workflow.
- Toolchains & Pipelines: Creating a seamless AI-driven environment.
- Agile Artifacts: Backlog, roadmap, and requirements engineering.
- Precision Management: Capacity planning and estimation (Accuracy vs. Precision).
- Product Ownership: Ideation, feature analysis, and Vibe-coding risks.
- Risk & Scenarios: Planning for "What Ifs" and automated risk management.
- Refinement: Use Case and User Story description & refinement.
Cerințe
- Basic understanding of the Agile Manifesto and Scrum framework.
- Experience in project management, product ownership, or team leadership.
- No prior programming or AI engineering experience is required, though a general familiarity with digital tools is recommended.
Audience
- Agile Project Managers and Scrum Masters.
- Product Owners and Product Managers.
- IT Team Leaders and Delivery Managers.
- Business Analysts working in Agile environments.
- Operations Managers interested in AIOps.
7 Ore
Mărturii (2)
Exemple practice
Ryan Brookman - The Shaw Group Limited
Curs - Introduction to Artificial Intelligence for Non-technical users
Tradus de catre o masina
Ne am folosit instrumentele.
Victor Aguero - PNUD/MICI
Curs - Aplicaciones Prácticas de Inteligencia Artificial para Personal Administrativo
Tradus de catre o masina
