Himanshu Ramchandani's Linkedin Analytics

Don't know where to start your AI Engineer Journey? Follow this 7 Day AI Bootcamp Prep Challenge https://lnkd.in/dkNE6UN2 This challenge is for you to get started, you maybe already know about these topics and can complete the challenge in 1 day as well. I am starting AI Engineer HQ, Here is the curriculum we are going to follow: https://lnkd.in/dMQS-DxV Join the waitlist to get your priority spot by filling this FREE form: https://lnkd.in/dbvg2g2v

AI Engineer HQ is Live Now and starting at 30th April Cohort-based Bootcamp https://lnkd.in/ddGQqhtD Access the AI Engineering Roadmap 2025

7-Step Prep Challenge for AI Engineering I created this to help you get started with AI You can access the detailed roadmap here AI Engineer HQ: https://lnkd.in/dMQS-DxV I am starting the Cohort-Based Bootcamp on 30th April. Curriculum: Curriculum 1 - Foundations of AI Engineering 2 - Mastering Large Language Models (LLMs) 3 - Retrieval-Augmented Generation (RAG) 4 - Fine-Tuning LLMs 5 - Reinforcement Learning and Ethical AI 6 - Agentic Workflows 7 - Career Acceleration 8 - Bonus What’s in it for you? - Lifetime access to recordings, notes, templates, cheat sheets - Knowledge Base for quick access to notes and resources - Closed community of professionals with similar mindset - The Project Lab [12 Industry Level Hands-On Projects] - Live Interactive Cohort - 1:1 Mentorship - Personal Portfolio & Brand on LinkedIn - Every Sunday, live Q&A and Guest Session Series - AI Sync - Industry updates in just 10 minutes every day - Showcase your projects/work in front of leaders/members - House of Research - Learn to write & implement research papers This price is for early bird access; only a few spots are available. The price will be $1500 at the launch. (15th April 2025) PS: Join now, Financial aid available (Limited Spots), DM here: https://lnkd.in/dex6W4Re

Do you pronounce it data or data?

What skills you want to gain in 2025? (AI Engineering)

AI Engineer Live Bootcamp [8 Days Left] Details about the HQ here: https://lnkd.in/ddGQqhtD Modules 1. Foundations of AI Engineering 2. Mastering Large Language Models (LLMs) 3. Retrieval-Augmented Generation (RAG) 4. Fine-Tuning LLMs 5. Reinforcement Learning and Ethical AI 6. Agentic Workflows 7. Career Acceleration 8. Bonus Detailed Curriculum Here: https://lnkd.in/dMQS-DxV Live Cohort-Based Bootcamp Start Date: April 30, 2025 Schedule: Wed, Thu, Fri at 8 AM IST (90-min sessions) Sunday: Extra sessions - Guest Series, Q&A, Study Group, Project Demo Only 7 Seats left. [43 / 50] EMI option available. In case of doubt, DM me: https://lnkd.in/dex6W4Re

"I can’t figure out how to build LLMs or RAG systems, and I am a beginner." The first thing you must do is keep your fundamentals in place. Learn these 7 parts before moving forward: 1. Python Basics https://lnkd.in/dzGY9Umz 2. Command Line Basics https://lnkd.in/dsC9sjz9 3. Git Basics https://lnkd.in/dqnjGZx3 4. API Basics https://lnkd.in/d4S55SH5 5. Pickle Files https://lnkd.in/d2bQPxhZ 6. Deployments Basics https://lnkd.in/duejeHJg 7. Docker Basics https://lnkd.in/dn4iNBwT Once you complete these, go for a proper roadmap of AI Engineering. I created one that you can access here: https://lnkd.in/dMQS-DxV PS: Join the waitlist for AI Engineer HQ BootCamp, starting in April, for early bird access. Limited seats and too many requests to join. Fill the form for FREE: https://lnkd.in/dbvg2g2v

AI EngineerHQ Financial Aid – Limited Seats! I’m offering financial aid for AI EngineerHQ to help you kickstart AI Engineering! Live Cohort-Based Bootcamp Start Date: April 30, 2025   Schedule: Wed, Thu, Fri at 8 AM IST (90-min sessions) Sunday: Extra sessions - Guest Series, QnA, Study Group, Project Demo Seats: Limited Seats only—first come, first served. Curriculum: https://lnkd.in/dMQS-DxV Currently, 50 seats are available, and 39 are already taken. How to Join: kindly DM me here: https://lnkd.in/dex6W4Re

Companies need 4 million more AI engineers worldwide - AI engineer salaries are rising globally, especially in the US, Europe, Asia, and India - Entry-level AI engineers in the US earn around $100,000, and lead engineers can make up to $220,000 - AI tools can generate code, but they can’t understand business needs or complex strategies, so human engineers are still crucial - Software engineers must learn AI skills to stay relevant in the job market - Gartner predicts 75% of software engineers will use AI code tools by 2028, up from less than 10% in 2023 - Future software engineers will focus more on strategy and AI integration rather than just coding - AI-powered apps are expected to hit 38.5 billion by 2028, showing massive growth potential - Generative AI is exploding, growing from $36B in 2024 to an expected $356B by 2030 - By 2028, 60% of companies will expect employees to have basic AI skills, increasing demand for AI trainers and educators - AI will replace some jobs but also create new ones, especially in development, ethics, and responsible AI - Cybersecurity for AI is becoming a top concern, increasing the need for engineers with security knowledge Source: ventionteams PS: Want to Master AI Engineering without a PhD? Check this roadmap: https://lnkd.in/dMQS-DxV

Risks of creating Ghibli-style images using ChatGPT As an AI Engineer, I understand how these systems work. You will upload your image, and now you don’t know whether the company is going to use your image. If your image is used for training these AI systems in the future, it can be generated elsewhere without your knowledge. When images are shared with AI systems like ChatGPT, they might be stored and used later. OpenAI claims not to use user images for training, but policies vary across different companies. OpenAI was a non-profit organization initially, but now it has become a for-profit organization. Hayao Miyazaki has strongly opposed AI-generated art. After seeing an early AI animation tool, he called it "an insult to life itself." At the end of the day, it’s your choice whether to upload an image to an AI system. Just remember, once it’s uploaded, you lose control over it. Do you think they are not going to use your image when competitors emerge? You are cute!

I’m launching the waitlist for AI Engineer HQ [BootCamp for AI Engineering] This is for you if you are a software developer, switching careers, freelancing, love tech, or work in a company. Build & Deploy production apps with LLMs, Agentic AI, RAG pipelines, Fine-Tuning LLMs, Reinforcement Learning & Ethical AI. Curriculum here: https://lnkd.in/dMQS-DxV We’re starting in April 2025, But spots are limited, only 40 seats are available at launch. Get early access by joining the waitlist to lock in your priority spot before they are gone. It takes 30 seconds! Fill this form for FREE: https://lnkd.in/dbvg2g2v PS: Start your 7 Days Prep Challenge by commenting "AI Engineer HQ" below.

OpenAI's practical guide to building AI Agents Keep these 7 key pointers in mind as an AI Engineer 1/ Decide if You Really Need an AI Agent Is this task too hard for simple code? Use an agent when: - decisions are complex - rules keep changing - the task uses lots of unstructured data Sometimes, a regular script or program works better. 2/ Set Up the Core Building Blocks Start with a smart LLM to set the baseline Every agent has three parts: - model (LLM) - tools (APIs or other systems) - instructions (rules and workflows) Write simple and clear instructions - break big tasks into smaller steps - use existing documents to guide agents 3/ Add Tools for the Agent to Use Tools help the agent do more, like using APIs or calling databases - agents can even use other agents as tools - make tools reusable and easy to manage - keep the tool design simple and standard 4/ Design for Safe and Reliable Behavior (Guardrails) Add safety from the start, even for early tests Guardrails protect against: - bad or unsafe content - private or personal data leaks - wrong or risky decisions Use: - relevance and safety classifiers - PII filters - rule-based checks - output validation Keep adding guardrails as new edge cases pop up. 5/ Choose the Right Agent Setup Start with a single-agent system: - add tools over time - easier to test and maintain - use prompt templates to manage complexity Use a multi-agent setup only when: - tasks are too hard for one agent - one agent can't choose the right tool - you want to split logic or workflows 6/ Pick the Right Multi-Agent Pattern (if needed) Manager pattern: One agent leads, others follow instructions - best when you want one point of control Decentralized pattern: Agents work as a team - best when different agents fully handle tasks 7/ Plan for Real-World Use and Growth Let humans jump in when: - the agent gets stuck - there’s a high-risk action - failures go over a set limit Start small, test with real users Grow slowly and improve with feedback. PS: I am starting the AI Engineer HQ on 30th April. Join fast; there are Limited seats. What you will get: https://lnkd.in/dMQS-DxV

Ever wonder how these billion-dollar AI tools are free to use? One-word answer is: POWER Big tech is not doing charity business, they are doing business of power. Investments you know about: - OpenAI: $58 billion (Microsoft, SoftBank) - Google DeepMind: $100 billion pipeline - Anthropic: $18 billion (Amazon) The strategy is simple: - Burn billions to undercut ethical competitors. - Flood the market with free tools like ChatGPT to create dependency. - Harvest user data like every prompt, interaction, and idea, to refine models and lock in dominance. Hidden Costs of “Free” AI - Each GPT-4 query costs $0.40 in energy and infrastructure. - Users pay not with cash but with their privacy and intellectual property. Inputs into tools like GitHub Copilot become training data for future models, stripping creators of ownership. Data labelers in Kenya and the Philippines earn $2/hour to filter graphic content, facing PTSD with no mental health support. When something is free, you are the price. PS: I wrote a detailed edition in my newsletter [check comments]

1000+ AI Dataset sources in 50+ Industries 1. LLM DataHub Datasets for LLM training. https://lnkd.in/gsSy2U2d 2. Awesome LLMs Datasets LLM Datasets and research papers https://lnkd.in/gVNqDsQi 3. LLM Datasets High-quality datasets, tools, and concepts for LLM fine-tuning https://lnkd.in/gCyCYhww 4. Awesome Public Datasets This list of topic-centric public data sources is of high quality. https://lnkd.in/dYaTU7sK 5. NLP Datasets Alphabetical list of free/public domain datasets with text data for use in NLP. https://lnkd.in/dX8YEC5Q 6. Awesome Dataset Tools A curated list of awesome dataset tools. https://lnkd.in/dK9nDzaN 7. Awesome time series database A curated list of time series databases. https://lnkd.in/dm2P2PFC 8. Awesome Cybersecurity Datasets A curated list of amazingly awesome Cybersecurity datasets. https://lnkd.in/dhTXzn6k 9. Awesome Robotics Datasets Robotics Dataset Collections. https://lnkd.in/d4yV6Des PS: Join the waitlist for AI Engineer HQ starting in April, for FREE https://lnkd.in/dReww8Fr You will be the first one to get the early bird access. I have 40 seats in the April cohort, and 24 are already taken. Roadmap Here: https://lnkd.in/dMQS-DxV

Become an AI Engineer without Years of struggle or a PhD! Build Production Agentic AI, RAG pipelines, Fine-Tuning LLMs, Reinforcement Learning, with 11 industry-level apps, and transformation from beginner into an AI pro in 12 weeks guaranteed. Get the detailed roadmap here: https://lnkd.in/dMQS-DxV Curriculum 1 - Foundations of AI Engineering 2 - Mastering Large Language Models (LLMs) 3 - Retrieval-Augmented Generation (RAG) 4 - Fine-Tuning LLMs 5 - Reinforcement Learning and Ethical AI 6 - Agentic Workflows 7 - Career Acceleration 8 - Bonus Live Cohort-Based Bootcamp Start Date: April 30, 2025 Schedule: Wed, Thu, Fri at 8 AM IST (90-min sessions) Sunday: Extra sessions - Guest Series, Q&A, Study Group, Project Demo Seats: Limited Seats only—first come, first served. 42 out of 50 are taken already. In case of doubt, DM me: https://lnkd.in/dex6W4Re

Advice for Founders by Google - Use AI to generate revenue and create new opportunities, not just to cut costs. - Align pricing with value delivered, considering usage-based or value-based models. - Move fast, ship quickly, and iterate based on user feedback. - Build point features rather than standalone AI products. - Start with simple, out-of-the-box solutions before tackling broader challenges. - Data is key, collect diverse, high-quality data while ensuring privacy and security. - Develop strong evaluation metrics to measure AI performance. - Use flexible infrastructure that allows for rapid adaptation to changing AI models. - Educate your team on AI fluency and integrate AI into company culture. Need help with this, lets brainstorm: https://lnkd.in/dSdxFp8X - Build software factories rather than one-off solutions. - Ensure AI products deeply integrate into user workflows to create lasting value. - Develop a solid data strategy alongside your AI strategy. - Design user experiences that don’t require complex prompts. PS: Join the waitlist of AI Engineer HQ, and be the first one to get priority spot. Limited seats. https://lnkd.in/dbvg2g2v Curriculum here: https://lnkd.in/dMQS-DxV