The most important innovations are those turning prototypes into reliable products: grounded LLMs, tool‑using agents, multimodal/embodied models, and the ops stacks that make them safe, fast, and cheap at scale. How to upskill fast (6 projects) Bottom line: prioritize agent frameworks, multimodal/embodied models, advanced retrieval, mature LLMOps, robust evaluation, and secure, efficient deployment—this is the … Read more
The most durable edge comes from shipping reliable AI systems end‑to‑end: LLMs grounded by retrieval, agentic workflows with guardrails, robust data/MLOps, and rigorous evaluation—combined with domain and product sense that ties tech to outcomes. How to prove it in 45 days Bottom line: irreplaceability comes from owning the full lifecycle—LLMs+RAG, agents, multimodal, strong data/MLOps, and … Read more
To be job‑ready, master the stack that ships real AI: LLMs grounded with retrieval, agents that can act, robust data and MLOps pipelines, and evaluation/safety tooling—plus basics in cloud and privacy. How to practice fast (6 mini projects) Bottom line: mastering LLMs, RAG, vector search, agents, multimodal, and MLOps—plus eval, data engineering, governance, and edge—forms … Read more
Every student now needs a blend of AI literacy, data fluency, and hands‑on building skills—plus ethics and product sense—to learn faster and stay employable in an AI‑saturated world. AI and data literacy LLMs, RAG, and multimodal basics MLOps and deployment mindset Evaluation, ethics, and governance Agents and workflow automation Product and domain sense Human skills … Read more
Learn beyond algorithms and DS. The 2026 CS edge is building, evaluating, and safely deploying AI systems—LLMs with RAG, solid MLOps, data plumbing, and responsible AI—proven with deployed projects. 1) LLMs and retrieval (RAG) 2) MLOps and delivery 3) Evaluation and safety 4) Data engineering for AI 5) Multimodal and agents 6) Domain plus product … Read more