Platform link: https://career.krishnaik.in/dashboard/hackathons?h=smartreco-build-challenge-2026 his is not a simple "related products" widget. You are building an agentic recommendation system: a backend agent that continuously observes a user's activity, understands their interests, retrieves the most relevant products from a knowledge base, and generates personalized, convincing recommendations that update as the user's behavior changes. A user lands on your platform and starts exploring — browsing products, searching, clicking around. Every meaningful action is tracked. Behind the scenes, an AI agent watches this activity build up, reasons over that behavior, retrieves the most relevant products, and generates a personalized recommendation — not a bare list, but a compelling message tailored to that user's journey. These recommendations are stored, shown on the site, and refresh as the user's behavior evolves

https://www.udemy.com/course/ai-security-bootcamp-guardrailsllm-gatewaysobservability/?couponCode=JULY399 This comprehensive bootcamp is designed to teach you how to build secure, observable, and production-ready AI applications using the latest tools, frameworks, and best practices adopted across the industry. Unlike traditional Generative AI courses that focus only on prompting or building chatbots, this course dives deep into the engineering practices required to deploy AI systems safely in real-world environments. You'll learn how to protect LLM applications against prompt injection, jailbreak attacks, data leakage, hallucinations, unsafe outputs, and other common security risks while implementing enterprise-grade monitoring, evaluation, and governance.

🎁 Get Started with Hyperagent Hyperagent is giving away $500 in free credits to the first 500 people who sign up using my link. 👉 https://www.hyperagent.com/krish ⏱️ What You'll Learn Building autonomous AI teammates Recurring AI workflows AI agent memory and skills Slack & Telegram integrations Google Docs automation Enterprise AI agents Multi-agent workflows Production AI systems

Bootcamp Enrollment Link: https://www.krishnaik.in/liveclass2/Advanded-Route?id=12 This 8-month, industry-focused program is designed for experienced practitioners who want to move beyond foundational theory and master the architecture of high-scale, real-world AI systems. This is not an introductory course; it is a deep dive into the complete modern LLM stack—from transformer core mechanics and advanced fine-tuning to multi-agent orchestration and production-grade LLMOps.

Enrollment Link: https://www.krishnaik.in/liveclass2/Advanded-Route?id=12 This 8-month, industry-focused program is designed for experienced practitioners who want to move beyond foundational theory and master the architecture of high-scale, real-world AI systems. This is not an introductory course; it is a deep dive into the complete modern LLM stack—from transformer core mechanics and advanced fine-tuning to multi-agent orchestration and production-grade LLMOps. By the end of this journey, you will not just be building prototypes, you will be shipping enterprise-ready LLM systems that reason, retrieve, and collaborate autonomously. You will master the deployment of complex architectures, including Graph RAG portals with Neo4j, vision-language models, and multi-agent DevOps pipelines** integrated with human-in-the-loop safety gates and automated CI/CD rollbacks on AWS. 📞 Have questions or need guidance? Reach out to Krish Naik's counselling team: +91 91115 33440 +91 84848 37781

https://fandf.co/4ehEnq9 Enterprise intelligence is shifting to an agent-first model, and Work IQ is the layer that powers it. It provides a workplace intelligence layer that enables agents to access and reason over organizational data, context, and tools, continuously building a semantic understanding across Microsoft 365 and external systems with built-in, permission-aware governance. Combining chat, context, tools, and workspaces, Work IQ supports high-volume, multi-step interactions. It works across frameworks and runtimes through standard protocols and provides the foundation for building agents, applications, and workflows that deliver faster, more intelligent, efficient outcomes.

Code Materials: https://docs.google.com/document/d/1wMPQL2NJTzT70GLBVYr3hKrObCmYrwhwvTgoEb0PLWk/edit?tab=t.0 Join our Advanced Route-Production AI And LLM Engineering-Frontier AI From Research To Production Bootcamp starting from July 19th 2026 https://www.krishnaik.in/liveclass2/Advanded-Route?id=12 Timestamps 00:00:04 — Stream Introduction & Agenda Setup 00:09:05 — Advanced Production AI Boot Camp Announcement Main sections 00:11:19 — Core Agentic RAG Codebase Review 00:17:53 — LangGraph & FlashRank Re-ranking Mechanics 00:23:02 — Qdrant Vector DB Configuration 00:25:58 — Pydantic Logfire Observability & Tracing 00:33:33 — Off-Topic Input Problem & Security Goals 00:35:07 — LLM Security: Guardrails & Gateways Overview 00:38:20 — Nemo Guardrails & Colang Rule Architecture 00:41:57 — Portkey Gateway Load Balancing & Virtual Keys 01:17:41 — Implementing Nemo Guardrails in Fast API Backend 01:36:26 — Configuring Portkey Multi-Model Fallback Routine 01:54:59 — Integrating Gateway Caching into Query Module 02:29:29 — Live Demo: Guardrails Blocking Off-Topic Requests 02:35:46 — Introduction to LLM Evaluation Pipelines 02:41:53 — Walkthrough of Evaluation Streaming Dashboard 02:53:55 — Core Eval Terms: Ground Truth & Goldens 03:04:16 — Student-Teacher Examination Framework Analogy 03:21:52 — Scalability: Automated LLM-as-a-Judge Reasoning 03:36:25 — Integrating Evals into CI/CD Pipelines 03:44:44 — RAG Metrics Deep Dive: Faithfulness 03:52:56 — RAG Metrics Deep Dive: Answer Relevancy 04:00:02 — RAG Metrics Deep Dive: Context Recall 04:21:55 — Coding Ragas & Deepeval Into the Module 05:06:13 — Moving Beyond Local: Enterprise Cloud Architecture 05:20:40 — Transitioning to Jina AI Embedding Models 05:33:33 — Persisting LangGraph State to Neon PostGreSQL 05:37:23 — Adding Serverless Semantic Cache with Upstash Redis 05:44:26 — Optimizing Dockerfiles Using UV Package Manager 06:21:52 — AWS Console Access & IAM Configuration 06:24:15 — AWS ECS Fargate, VPC, & Load Balancer Setup 06:41:15 — Structuring GitHub Actions for Automatic ECR Push 06:49:45 — Managing Environment Secrets in AWS Secret Manager 07:32:46 — Introduction to Multi-Modal Parsing Challenges 07:36:58 — Three Multi-Modal Document Parsing Paradigms 07:43:58 — Analyzing ColPali Multi-Vector Projections 07:50:51 — Code Execution: ColQwen 2.5 on Cloud GPU 08:04:09 — Single-Stage Models: Neotron Parse & Unlimited OCR 08:11:07 — Dual-Stage Pipelines: PP-DocLayout & GLM-OCR 08:24:30 — Live Bounding Box Comparison Using Neotron Parse 08:40:35 — Serving the Unlimited OCR Model on an L4 GPU 08:52:41 — Benchmark Analysis & Final Output Review

Check out WorkIQ: https://fandf.co/4fLiwdb Enterprise intelligence is shifting to an agent-first model, and Work IQ is the layer that powers it. It provides a workplace intelligence layer that enables agents to access and reason over organizational data, context, and tools, continuously building a semantic understanding across Microsoft 365 and external systems with built-in, permission-aware governance. Combining chat, context, tools, and workspaces, Work IQ supports high-volume, multi-step interactions. It works across frameworks and runtimes through standard protocols and provides the foundation for building agents, applications, and workflows that deliver faster, more intelligent, efficient outcomes.

Check out Microsoft Foundry https://fandf.co/4vpw7Lq Microsoft Foundry is the enterprise AI platform to build, ground, and govern AI apps and agents at scale. It brings together your full agent lifecycle with open development, built-in intelligence, and consistent security, compliance, and policy controls across every agent.

Materials : https://docs.google.com/document/d/1wMPQL2NJTzT70GLBVYr3hKrObCmYrwhwvTgoEb0PLWk/edit?usp=sharing Here are the detailed timestamps for the session: (0:00:00 - 0:19:45) Introduction and Mentors' Overview (0:19:45 - 0:31:15) Architecture of Agentic AI Application (0:31:15 - 0:56:49) Observability, Tracing, and Evaluation Layer (0:56:49 - 1:11:35) Project Demo (Trailer) (1:11:35 - 1:28:46) Break and Setup (1:28:46 - 1:38:32) Data Ingestion Introduction (True vs Noisy Data) (1:38:32 - 1:45:34) Chunking Strategies (Size and Overlap) (1:45:34 - 1:49:27) Embedding Models and Vector Stores (1:49:27 - 2:51:46) Q&A and Embedding Strategies (2:51:46 - 3:13:40) Retrieval Pipeline and Fallback Mechanisms (3:13:40 - 3:52:33) Deployment Discussion and Multimodal Context (3:52:33 - 4:10:00) Ingestion Pipeline Implementation (True Data) (4:10:00 - 4:13:23) Ingestion Pipeline Implementation (Noisy Data) (4:13:23 - 4:21:00) Q&A and Break (4:21:00 - 4:32:17) Retrieval Pipeline Deep Dive (4:32:17 - 4:33:54) Reranking Models Implementation (4:33:54 - 4:58:26) Q&A on Latency and Reranking (4:58:26 - 5:07:00) Recap and Documentation Review (5:07:00 - 6:11:39) Logfire Configuration and Observability Concepts (6:11:39 - 6:25:20) Spans, Traces, and Waterfall Explained (6:25:20 - 6:59:07) UI Integration and Prompt Security (6:59:07 - 7:02:00) Jailbreak Protection and Guardrails (7:02:00 - 7:13:13) Sensitive Topic Filtering and Dialogue Management (7:13:13 - 7:21:18) Implementation of Guardrails (Topic Guard) (7:21:18 - 7:30:12) Break and LLM Gateway Overview (7:30:12 - 8:11:19) Trace Analysis and Error Debugging (8:11:19 - 8:17:24) Caching, Final Q&A, and Next Steps

Enrollment Link:https://www.krishnaik.in/liveclass2/Advanded-Route?id=12 📞 Have questions or need guidance? Reach out to Krish Naik's counselling team: +91 91115 33440 +91 84848 37781 This 8-month, industry-focused program is designed for experienced practitioners who want to move beyond foundational theory and master the architecture of high-scale, real-world AI systems. This is not an introductory course; it is a deep dive into the complete modern LLM stack—from transformer core mechanics and advanced fine-tuning to multi-agent orchestration and production-grade LLMOps. By the end of this journey, you will not just be building prototypes, you will be shipping enterprise-ready LLM systems that reason, retrieve, and collaborate autonomously. You will master the deployment of complex architectures, including Graph RAG portals with Neo4j, vision-language models, and multi-agent DevOps pipelines** integrated with human-in-the-loop safety gates and automated CI/CD rollbacks on AWS.

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Enrollment Link: https://www.krishnaik.in/liveclass2/AgenticAi?id=11

🚀 AI Security, Agentic Memory & AgentOps Masterclass A huge thanks to our amazing mentors — Divesh, Yash, Chirantan, and Paul — for sharing their expertise and making this masterclass possible. Linkedin Profiles Chrantan : https://www.linkedin.com/in/chirantanlonkar/?skipRedirect=true Divesh: https://www.linkedin.com/in/dhackmt/?skipRedirect=true Yash :https://www.linkedin.com/in/yash-patil-ai/ 📚 Resources & Materials 🔹 LLM Gateways GitHub: https://github.com/d-hackmt/LIVE-WEBINAR-25-MAY-GATEWAYS Demo App: https://letsgateway.streamlit.app/ 🔹 NVIDIA NeMo Guardrails GitHub: https://github.com/d-hackmt/guardrails-webinar Demo App: https://guardthisrag.streamlit.app/ 🔹 LLM Evaluation Materials Demo App: https://ragasz.streamlit.app/ GitHub: https://github.com/divesh-sse/ragas/blob/main/app.py 🔹 AgentOps & Agentic RAG GitHub: https://github.com/sourangshupal/Agentic-RAG-project ━━━━━━━━━━━━━━━━━━━━━━ As AI Agents move from prototypes to production, building intelligent systems is not enough. Modern AI systems must be secure, reliable, observable, scalable, and capable of maintaining long-term context. In this masterclass, we cover four critical pillars of production AI: ✅ AI Guardrails ✅ LLM Evaluations (Evals) ✅ Agentic Memory Systems ✅ AgentOps & Production Deployment 🎯 Key Topics Covered • Prompt Injection & Jailbreak Protection • PII & Data Security • LLM & RAG Evaluation Frameworks • Hallucination Detection • Agentic Memory Architectures • Short-Term & Long-Term Memory • Monitoring & Observability • Cost & Performance Optimization • Production Deployment of AI Agents • Scaling Autonomous AI Systems Whether you're building AI Agents, RAG applications, or enterprise GenAI solutions, this session will help you understand the foundations of production-ready AI systems. Timestamp 00:00:00 Welcome and Crash Course Overview 00:03:08 Introduction to LLM Security & AI Guardrails Module 1: AI Guardrails & LLM Security 00:16:38 Guardrail Frameworks (Nemo Guardrails, Meta Llama Firewall, AWS Bedrock) 00:20:50 Demo: Handling Prompt Injections, Off-topic Queries, and Jailbreaks 00:36:20 Nemo Guardrails Deep Dive & Colang Expression Language 00:51:04 LLM Observability with Pydantic Logfire 01:03:01 Setting up API Keys (Groq & Pydantic Logfire) Module 2: LLM Evaluations (Evals) 01:13:30 Transition to Evals & Evaluating Production-Grade RAG 01:23:18 Custom Evaluations vs. Benchmarks 01:30:44 Defining "Goldens" (Truth Datasets for Evals) 01:49:54 Using LLMs as a Judge 01:52:46 Understanding the Ragas Framework Metrics 02:04:58 Metric 1: Faithfulness (Groundedness) 02:12:35 Metric 2: Answer Relevancy 02:18:02 Metric 3: Context Precision (Ranking Evaluation) 02:24:48 Metric 4: Context Recall 02:30:46 Metric 5: Answer Correctness (Factual & Semantic Similarity) 02:40:31 Reviewing Automated Test Results and Dashboards Module 3: Agentic Memory Techniques 02:47:50 Introduction to Agentic Memory Systems 03:01:00 Conversational Buffer Memory & Token Bloating 03:12:43 Sliding Window Memory 03:37:24 Summary Memory (Abstractive & Progressive Summarization) 03:56:30 Summary Buffer Memory 04:20:05 Token Buffer Memory 04:24:41 Vector Store Memory (Long-term Context) 04:41:29 Entity Memory (Structured Named Entity Extraction) 04:56:29 Episodic Memory (Time-aware Session Recall) 05:15:54 Semantic Memory (Distilled Facts & Behavioral Patterns) 05:20:14 Procedural Memory (Dynamic System Instruction Updates) 05:25:56 Self-Reflection Memory (Agent Postmortems) 05:33:13 Memory Routing (Intent Classification) 05:40:23 Forgetting and Decay (Half-Life & Ebbinghaus Curve) Module 4: AgentOps & Production Workflows 05:50:47 AgentOps Overview: From Prototype to Production 05:55:27 Infrastructure Setup: Airflow, Neon DB (PostgreSQL), and OpenSearch 06:08:10 Fast API Setup & Agentic Endpoints 06:10:58 Langfuse Integration for Deep Agent Tracing 06:19:11 Implementing AWS Bedrock Guardrails 06:40:41 Dense Vector Search vs. BM25 Hybrid Search Implementation 06:45:01 Redis Caching for RAG Pipelines 06:53:12 Model Context Protocol (MCP) Server Integration 07:08:50 Deploying the Application on Amazon EKS (Kubernetes) 07:22:25 Load Testing with Locust (Handling Concurrent Users) 07:31:42 Horizontal Pod Autoscaling (HPA) & Vertical Scaling

Last 5 days left. Super excited to launch our new Bootcamp 3.0 Agentic AI Specialization With AgentOps From June 21st 2026. Happy Learning!!! Visit : https://www.krishnaik.in/liveclass2/AgenticAi?id=11 📞 Have questions or need guidance? Reach out to Krish Naik's counselling team: +91 91115 33440 +91 84848 37781

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github: https://github.com/krishnaik06/Deep-agents-With-Langchain The easiest way to start building agents and applications powered by LLMs—with built-in capabilities for task planning, file systems for context management, subagent-spawning, and long-term memory. You can use deep agents for any task, including complex, multi-step tasks. Deep Agents is an “agent harness”. It is the same core tool calling loop as other agent frameworks, but with built-in capabilities that make agents reliable for real tasks. Timestamp 00:00:00 Introduction 00:02:31 what are Deep Agents 00:16:20 Project Implementation 00:21:56 Building Deep Agents With Langchain 00:43:45 Customize Deep Agents 00:46:04 Deep Agents vs Claude Agent SDK 00:59:34 Deep Agents Backend Agents.md 01:19:05 Context Engineering And Memory In Deep Agents 01:53:15 Skills In Deep Agents 02:17:55 Subagents In Deep agents 02:34:22 End To End Deep Agents Project ---------------------------------------------------------------------------------------------------- Learn from the best to be the best https://krishnaik.in/liveclasses

Get started with the platform and explore the developer documentation https://www.uipath.com/developers/coding-agents?utm_source=youtube&utm_medium=paid_social&utm_campaign=advocacy&utm_content=coding_agents1&utm_term=krishnaik&utm_team=spr&utm_team_geo=global UiPath for Coding Agents: Building Enterprise-Grade AI Agents 🚀 Hello all, my name is Krishna and welcome back to the channel! In this video, we’re diving deep into a game-changing framework: UiPath for Coding Agents. A common misconception is that UiPath is trying to compete with coding assistants like GitHub Copilot or Claude Code. It’s actually the opposite! UiPath sits underneath your coding agents, providing the infrastructure, governance, and enterprise-level "trust" that businesses need to actually deploy AI at scale. Whether you're building with LangChain, LangGraph, or OpenAI, this video shows you how to turn those scripts into production-ready enterprise applications. 📌 What's in this video: The "Underneath" Strategy: Understanding how UiPath enhances your existing coding agents. Governance & Trust: How to handle compliance, security, and human-in-the-loop (HITL) for AI. Hands-on Demo: * Setting up the UiPath Agent Skills library. Syncing your local IDE (Cursor/VS Code) with UiPath Studio Web. Building a Web Search Agentic Workflow from scratch using Claude Code. Debugging & Evals: How to test your agents and monitor execution trails in real-time. 🛠️ Key Commands Used: To install the UiPath Agent Skills: pip install uipath-as To initialize the skills in your terminal: uipath-skills install ⏱️ Timestamps: 0:00 - Introduction to UiPath for Coding Agents 1:15 - Enterprise Scale: Governance & Automation 3:35 - Exploring Studio Web & Orchestrator 5:25 - Installing UiPath Agent Skills 8:00 - Creating a Coded Agent (Local Sync) 12:35 - Debugging the Workflow (Weather & Time Demo) 17:35 - Building a Web Search Agent with Claude Code 24:30 - Running the Final Enterprise Workflow 26:30 - Conclusion & Next Steps If you found this video helpful, please Like, Share, and Subscribe! We have a full series coming up on advanced AI governance and human-feedback loops.

Enrollment links: https://www.krishnaik.in/liveclass2/AgenticAi?id=11 Agentic AI & GenAI with Cloud 3.0 is the modern, production-focused path to mastering Agentic AI, taking you from core concepts to enterprise-scale multi-agent deployment. Over 5 months, you'll build with every framework shaping the field today: LangChain, LangGraph, OpenAI Agents SDK, Google ADK, AWS Strands, CrewAI, LlamaIndex, Claude Code, AutoGen, n8n, and LangFlow. You'll master the two foundational protocols of 2026, MCP and A2A, along with advanced Agentic RAG, Context Engineering, Agent Security, and the full AgentOps lifecycle using AgentOps SDK, LangSmith, Opik, and Langfuse. The course culminates in four end-to-end portfolio projects across AWS, GCP, Azure, and a fully local stack, each built on a distinct framework, architecture, and deployment strategy. By the end, you won't just understand Agentic AI. You'll have shipped it in production. 📞 Have questions or need guidance? Reach out to Krish Naik's counselling team: +91 91115 33440 +91 84848 37781

Learn from us and be the best in industries Visit https://krishnaik.in/liveclasses Use coupon Krish15 for 15% off Till June 7th 2026

Every enterprise has critical data locked inside Oracle databases — accessible only to SQL-literate engineers. Oracle AI Database Select AI eliminates this bottleneck. https://fandf.co/49alXFS - ai developer hub https://fandf.co/4vdxRrl - (https://github.com/sourangshupal/oracle-yt) This tutorial demonstrates a fully self-contained, reproducible demo where: 🏪 A realistic retail sales database is built from scratch (4 tables, ~1,750 rows) 🤖 An LLM provider (OpenAI GPT-4o or Cohere Command R+) is registered directly inside Oracle 💬 Business users type plain English and get instant, accurate SQL results 📊 All 4 Select AI modes are demonstrated live in a Jupyter Notebook ✅ 100% free — runs entirely on Oracle Cloud Always Free Tier. No credit card required.

Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools. Available in your terminal, IDE, desktop app, and browser. Claude Code is an AI-powered coding assistant that helps you build features, fix bugs, and automate development tasks. It understands your entire codebase and can work across multiple files and tools to get things done. TimeStamp 00:00:00 Introduction 00:01:56 Claude Ecosystem 00:22:02 Claude Code Installation And Integration 00:55:00 Building Agents With Claude Code 01:13:03 AgentViews In Claude Code 01:22:54 Agent Teams In Claude Code 01:37:18 Claude Skills And Plugins ----------------------------------------------------------------------------- Learn from the best Visit https://krishnaik.in/liveclasses

This is a complete course on learning Generative ai and agentic with Langchain and Langgraph. We have included all the topics from RAG, vectorless rag, Deep agents, Guardrails, LLM Evaluation and LLM Gateways Techniques Github Links Langchain : https://github.com/krishnaik06/Langchain-V1-Crash-Course Langgraph: https://github.com/krishnaik06/Agentic-LanggraphCrash-course RAG: https://github.com/krishnaik06/RAG-Tutorials Vectorless RAG: https://github.com/krishnaik06/RAG-Tutorials/blob/main/PageIndex_Vectorless_RAG_CrashCourse%20(1).ipynb Deep Agents: https://drive.google.com/file/d/1SVjvgqvKfF-FPAIqpEZdKMQLhe13NLFD/view Guardrails : https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/updatedlangchain/langchain_guardrails_crash_course.ipynb LLM Evals : https://github.com/krishnaik06/RAG-Tutorials/blob/main/1-rag_evaluation.ipynb LLM Gateways: https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/llm_gateway_tutorial.ipynb Timestamp 00:00:00 Introduction 00:02:31 Langchain Course 02:35:12 Langraph Course 05:02:29 RAG Course 07:10:43 Vectorless RAG 08:02:11 Deep Agents 08:45:43 Guardrails 09:22:55 LLM Evaluation 10:30:25 LLM Gateways ------------------------------------------------------------------- Learn from us visit https://krishnaik.in/liveclasses

github: https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/llm_gateway_tutorial.ipynb Check out BetterDB: https://betterdb.com/b/nVN8k Timestamp 00:00:00 Introduction 00:04:12 LLM Gateways 00:07:44 LLM Gateways Core Capabilities 00:13:26 LLM Gateways Implementation 00:16:22 Simplest LiteLLM Example 00:19:23 Automatic Fallbacks Impleemntation 00:22:11 Cost Tracking With LLM Gateways 00:23:35 Caching With LLM Gateways 00:28:40 Load Balancing Across LLM Providers 00:31:42 Integrating Gateway With Langchain 00:34:38 Smart Router LLM Gateway 00:39:21 Guardrails LLM Gateways An LLM Gateway is a smart middleware layer that sits between your application and one or more Large Language Model providers, giving you a single unified control point to access, manage, and govern all your LLM traffic. --------------------------------------------------------------------- Learn from us Visit: https://krishnaik.in/liveclasses

Enrollment Link: https://www.krishnaik.in/liveclass2/nocode?id=10

Learn from us Visit https://krishnaik.in/liveclasses • Bachelor’s or Master’s degree in Computer Science, AI/ML, or equivalent practical experience • Proficiency of Python development experience with strong software engineering fundamentals • Hands-on experience building applications with LLM APIs (OpenAI, Anthropic, Google, etc.) • Deep understanding of transformer architectures, attention mechanisms, and model capabilities • Experience with vector databases and embedding models for semantic search • Proficiency with ML frameworks (PyTorch, TensorFlow) and Hugging Face ecosystem • Strong knowledge of prompt engineering techniques and in-context learning • Experience with MLOps practices including model versioning, monitoring, and deployment • Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) • Understanding of AI safety, alignment, and ethical considerations • Excellent problem-solving skills and ability to work with ambiguous requirements • Strong communication skills to explain complex AI concepts to various stakeholders

Last 3 days and Last 80 seats left Enrollment link: https://www.krishnaik.in/liveclass2/nocode?id=10 Use coupon code KRISH to avail 10% off The AI For Everyone program is a comprehensive 3-month, hands-on program designed for beginners, professionals, and leaders across all domains: Engineers, Product Managers, Sales Professionals, Marketing Teams, HR Leaders, Operations Managers, Consultants, Entrepreneurs, and students. The course begins with foundational AI concepts explained through visual learning, then rapidly progresses to hands-on building of content systems, applications, automations, and AI-powered workflows using no-code tools. 📞 Have questions or need guidance? Reach out to Krish Naik's counselling team: +91 91115 33440 +91 84848 37781