Advanced AI Training

Many people often ask me how to learn Agentic AI and where to start. My answer keeps evolving — because the field itself is changing every few months. What I shared six months ago helped many people get started. But today, with newer frameworks, deeper integrations, and more real-world use cases, that learning path looks different. So I’ve put together this updated AI Agents Learning Map — a structured view of how I now see this space progressing. Level 1 – Foundations This is where every learner should begin. The goal is to understand how intelligent systems are built and connected. • Large Language Models – Core models that generate and understand natural language. • Embeddings and Vector Databases – Represent meaning and context for better search and reasoning. • Prompt Engineering – Techniques to guide model responses effectively. • APIs and External Data Access – Allow models to connect to external systems and data sources. At this level, focus on understanding how LLMs interact with structured and unstructured data. Level 2 – System Capabilities At this stage, models evolve into systems. You begin combining memory, context, and reasoning to build early agent behaviors. • Context Management – Managing dialogue and maintaining state across interactions. • Memory and Retrieval – Implementing persistent storage for short- and long-term information. • Function Calling and Tool Use – Letting AI take real actions beyond text generation. • Multi-step Reasoning – Enabling sequential decision-making and logical flow. • Agent Frameworks – Using orchestration tools like LangGraph, CrewAI, and Microsoft AutoGen. This level is where isolated models start becoming intelligent systems. Level 3 – Advanced Autonomy Here, agents collaborate, plan, and execute tasks independently. This is where agentic AI truly begins. • Multi-Agent Collaboration – Building systems where agents work together with defined roles. • Agentic Workflows – Structuring processes that allow autonomous execution. • Planning and Decision-Making – Defining goals, evaluating options, and acting without human prompts. • Reinforcement Learning and Fine-tuning – Improving outcomes based on feedback and experience. • Self-Learning AI – Systems that evolve continuously as they operate. At this level, AI transitions from reactive systems to proactive problem-solvers. Why this learning map matters This map is not about tools or frameworks. It’s about progression — how engineers and organizations move from using AI to building intelligence. Mastering each level leads to better design decisions, deeper understanding, and ultimately, the ability to create autonomous, adaptive systems. Where would you place your current AI understanding on this map?

A clear path into AI engineering using 10 GitHub repos Step-by-step plan you can follow and show as proof of work Foundations 1. Learn the basics of machine learning and deep learning • ML for Beginners, AI for Beginners Output: 3 small projects with short READMEs that explain the goal, data, and result. Go deeper 2) Build neural nets from scratch • Neural Networks: Zero to Hero Output: a tiny GPT trained on a toy dataset, plus notes on what you changed and why. Read papers in code 3) Study real architectures by walking through annotated implementations • DL Paper Implementations Output: pick one model and re-implement a minimal version. Write what you simplified. Ship real software 4) Move from notebooks to apps and services • Made With ML Output: refactor one project with a simple API, tests, and a one-click run script. Work with LLMs 5) Learn the core pieces end to end • Hands-on LLMs Output: a basic RAG app (retrieval augmented generation) that answers questions on a small knowledge base. Make RAG better 6) Compare advanced techniques • Advanced RAG Techniques Output: run A/B tests on 3 settings and report latency, accuracy, and cost in a table. Learn agents 7) Build simple agents that take steps toward a goal • AI Agents for Beginners Output: an agent that checks a site, writes a summary, and files a ticket. Take agents toward production 8) Add memory, orchestration, and basic security • Agents Towards Production Output: logging, retry logic, and input checks. Note what fails and how you fixed it. Round out your portfolio 9) Adapt working examples • AI Engineering Hub Output: 2 more apps that solve real tasks, each with a clear demo and setup guide. How to pace this • One repo per week is a good rhythm. • Keep a single repo called “ai-engineering-journey” with subfolders per step. • After each step, post a short write-up with a 30-second screen recording. What hiring managers look for • Working code that runs on first try. • Clear README, data source, and limits. • Small tests and a simple eval, even if manual. • Changelog that shows steady progress. Save this and start with step 1 today. Repos and links 1. ML for Beginners — https://lnkd.in/dQ6nAJRC 2. AI for Beginners — https://lnkd.in/dXwJJjMm 3. Neural Networks: Zero to Hero — https://lnkd.in/dagQ3kmA 4. DL Paper Implementations — https://lnkd.in/dyw54m73 5. Made With ML — https://lnkd.in/duHjr2CY 6. Hands-On Large Language Models — https://lnkd.in/dxEGzsgc 7. Advanced RAG Techniques — https://lnkd.in/dd2TKA5P 8. AI Agents for Beginners — https://lnkd.in/deznrHdf 9. Agents Towards Production — https://lnkd.in/dz-WgU-3 10. AI Engineering Hub — https://lnkd.in/d9cNqy7c

AI mastery isn’t about learning everything. It’s about knowing what to learn next. Jumping into advanced models without foundations slows you down. Staying in basics too long keeps you stuck. The real progress comes from moving through the right layers at the right time. That’s what separates experimentation from mastery. Here’s a complete roadmap to mastering AI in 2026 - Foundations Start with Python, data structures, math, and statistics to build real understanding. - Machine learning loop Learn core ML concepts, evaluation techniques, and how to iterate on models. - Deep learning Understand neural networks, CNNs, RNNs, transformers, and modern architectures. - Generative AI Work with LLMs, prompt engineering, RAG, embeddings, and multimodal systems. - Applied AI Build real use cases across domains like NLP, vision, recommendation systems, and forecasting. - Tooling and deployment Move models to production with MLOps, APIs, cloud deployment, and monitoring. - Ethics and safety Design systems that are fair, explainable, secure, and aligned with regulations. - Career and ecosystem Turn skills into impact through projects, open source, portfolios, and real opportunities. AI isn’t one skill. It’s a stack. And each layer unlocks the next. Skip layers, and things don’t work. Build them right, and everything compounds. Where are you currently in this roadmap?

Gartner’s social listening analysis found an increase of over 5000% in discussions about using #AI #Agents for #Analytics in the first six months of this year. Augmented analytics capabilities have been evolving and are now able to assist with many parts of analytics development such as data profiling, automated insight generation, natural language querying and natural language generation. In the last five years researching this, I spoken with around 500 clients making use of these features in conjunction with hearing from multiple types of analytics users across the business and technical spectrum. With vendors I’ve been guiding their features and roadmaps to accelerate the augmented analytics workflow for users. Something has changed: Agentic analytics is the evolution of augmented analytics through the application of AI agents to analytics. Agentic analytics enable a shift where multistep tasks within or across workflow segments are executed autonomously, requiring only high-level user intervention. This shortens the analytics workflow completion time, which speeds up the creation of decision-informing insights that ultimately lead to faster execution and positive business impacts. In my research at Gartner this year, I conceived of a label for and defined “agentic analytics” as a process of data analysis that applies AI agents across the data-to-insight workflow, orchestrating tasks semiautonomously or autonomously toward stated goals that support, augment and automate insights. I want end-user organizations to evaluate and challenge vendor claims to provide agentic analytics capabilities. Do this by creating test environments, datasets and analytics tasks that range in complexity to identify limits of usage. Your test queries should be created and range in difficulty of execution, from tasks that can be achieved using a few lines of code generation to multistep tasks that require planning and reasoning regarding the steps that need to be performed. These benchmarks should be created on data that can be provided to vendors and/or anonymized to avoid being charmed by scripted demonstrations. I think one of the most promising #GenAI use cases is task augmentation in the data analysis life cycle, including insight generation and high-level goal fulfillment. This note from myself, Souparna Palit, Anirudh Ganeshan and Afraz Jaffri continued to help head of analytics and BI (and other leaders looking into agentic analytics processes) prepare for this new phase of enterprise insight-generation capabilities. Gartner clients subscribing to our data and analytics research can login and read my contribution to this field in: “Augment D&A Workflows With Agentic Analytics” https://lnkd.in/e6k-sXbY [Published 15 October 2024] What business benefits and risks have emerged for you as an organisation adopting agentic analytics? Are you an agentic analytics vendor that has proven case studies?

𝗘𝘃𝗲𝗿 𝗪𝗼𝗻𝗱𝗲𝗿𝗲𝗱 𝗵𝗼𝘄 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗨𝘀𝗲𝘀 𝗚𝗲𝗻𝗔𝗜 𝘁𝗼 𝗘𝗻𝗵𝗮𝗻𝗰𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 LinkedIn's search engine doesn’t just look for keywords—it 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝘀 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀, 𝗲𝘃𝗲𝗻 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅, like "how to ask for a raise?" or "dropout in AI." Here’s a peek into the innovative GenAI-powered content search engine that makes it happen: ------------------ 🚀 What’s New? LinkedIn introduced 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗲𝗮𝗿𝗰𝗵 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 to go beyond exact keyword matches. This enables understanding the meaning of queries, improving results for complex, natural language searches. ------------------ 🛠️ How It Works 1️⃣ Two-Layer Architecture: 📍 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗟𝗮𝘆𝗲𝗿: Combines keyword-based (TBR) and AI-powered semantic search (EBR) to fetch relevant posts efficiently. 📍 𝗥𝗮𝗻𝗸𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿: Uses advanced models to score and rank posts based on quality and engagement metrics. 2️⃣ AI-Powered Matching: 💥 𝗔 𝘁𝘄𝗼-𝘁𝗼𝘄𝗲𝗿 𝗺𝗼𝗱𝗲𝗹 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 (conceptual representations) of both queries and posts. 💥 These embeddings are compared to match posts that truly address your query, even if they don’t contain the exact keywords. 3️⃣ Metrics Driving Success: 💡 𝗢𝗻-𝗧𝗼𝗽𝗶𝗰 𝗥𝗮𝘁𝗲: Measures if the content answers the query. 💡 𝗟𝗼𝗻𝗴-𝗗𝘄𝗲𝗹𝗹𝘀: Tracks engagement based on how much time users spend on the content. ------------------ 🎉 Results? 🎯 10%+ improvement in search accuracy and engagement. 🎯 Better answers to complex questions, more relevant content, and enhanced user satisfaction. 💡 What’s Next? ▶ LinkedIn is exploring 𝗟𝗟𝗠-𝗯𝗮𝘀𝗲𝗱 𝗿𝗮𝗻𝗸𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 to refine its understanding of user queries and elevate the quality of search results even further. 🔗 Have you noticed better search results on LinkedIn? Let us know your thoughts below! ------------------ Link to the Engineering Blog: https://lnkd.in/gjvKunZh Sarveshwaran Rajagopal ------------------ #GenAI #ArtificialIntelligence #SemanticSearch #MachineLearning #AIInnovation #LinkedInUpdates #NaturalLanguageProcessing

LinkedIn's AI training settings don't affect all users equally. Did you notice that LinkedIn will share UK users' data with Microsoft, but not EEA users? In this video, I look at the background, the broader context, and the details. LinkedIn first floated the idea of training its AI models on users' personal data last summer and has since encountered several bumps in the road. Complaints were submitted to regulators in Ireland and the UK, and the company responded by putting the project on hold for EEA and UK users in September 2024. Incidentally, the EDPB published an opinion in December on the use of "legitimate interests" to train AI models. The Board did not rule out that this could be lawful on a case-by-case basis, with safeguards attached, and people's reasonable expectations taken into account. -- Other social media platforms have met with similar issues. Meta's AI training saga is too complicated to recount here in full, but suffice to say that the company also paused and has now resumed its policy of relying on "legitimate interests" for this processing. X faced court action from the Irish DPC and gave an undertaking confirming that it would not use EU users' data in this way. -- There are some interesting details around how LinkedIn plans to share data with Microsoft: • UK: Profile data and public content may be shared with Microsoft for model training, unless the user opts out. • EU/EEA/Switzerland: The data will be used by LinkedIn itself for AI training, but there's no mention of any sharing with Microsoft. • Canada/Hong Kong: There's a much more expansive approach to sharing data with Microsoft, including for advertising purposes (profile data, feed activity, ad engagement), but there's an opt-out available. • Other countries (including the US): There's no explicit opt-out. If you don't like it, close your account. -- As a LinkedIn user, you have until 3 November to opt out. If you opted out the last time LinkedIn tried this, check your settings. As a data protection professional, you now have another interesting test case about whether "legitimate interests" will stand up for large-scale AI training.

At 45, I had to start over. After almost 20 years at Google and Meta, after serving as CEO of Google for Colombia, Central America and the Caribbean, I had to ask myself the question many people are facing right now: What now? I'm writing this for the people in that moment. The ones who left their company after 10, 15, 20 years. The ones who feel AI just passed them by. The ones who don't know where to begin. Listen: you're not too old. You're in the best possible moment. You have something the 25-year-olds don't — judgment, context, networks, and the ability to tell what matters from what's just noise. What you need isn't another MBA. You need to know where to start. And everything that matters today is free. My map: 1. Start from scratch - Anthropic Academy — free certified courses, no code required https://lnkd.in/dVJMkjKN - Google AI Essentials on Coursera (audit mode is free) https://lnkd.in/djkJ-TQm 2. Learn how to talkto an AI (Prompting) - Anthropic's Interactive Prompt Engineering Tutorial https://lnkd.in/eNsk68qu - DAIR.AI Prompt Engineering Guide — the best manual out there, always current https://lnkd.in/dwrYdVfC 3. Master Claude (the one I use daily) - Anthropic Skilljar — Projects, Skills, Cowork, Claude+Excel and Claude Code https://lnkd.in/dVJMkjKN - Anthropic Cookbooks — practical examples for analysis, automation and data https://lnkd.in/drD3pyyr - Official Claude documentation https://docs.claude.com 4. ChatGPT - OpenAI Academy — free official courses https://academy.openai.com - OpenAI Cookbook — code examples and use cases https://lnkd.in/djk4hwAt 5. Gemini - Google AI Studio — to experiment for free https://lnkd.in/dGxQfFb7 - Google Cloud Skills Boost — "Generative AI Fundamentals" path https://lnkd.in/dUDqr5zw 6. Build Agents - Hugging Face AI Agents Course — 72,000 enrolled, free certificate, best in the market https://lnkd.in/eVwatiq3 7. Automate your work - n8n Academy — free https://lnkd.in/dPqiMMTf - Make Academy — free https://academy.make.com Two warnings before you dive in: One. 80% of the value is in steps 1, 2, and 3. Don't skip to "build agents" before you know how to talk to a model. That's like trying to conduct an orchestra without learning to read music. Two. No one will hire you for completing courses. They'll hire you for solving a real problem with what you learned. Start by automating something in your own routine this week. That's your first demo. And your best portfolio. Age, in this case, is an asset. Not a liability. Which one are you starting this week? Follow me Giovanni Stella | IA Con Canas | GLAIX #IAConCanas #ArtificialIntelligence #Reinvention

Innovation without guardrails is just disruption waiting to happen. Everywhere you look, AI is rewriting how we run businesses - accounting automation, predictive analytics, even decision-making. The upside? Faster processes, sharper insights, fewer repetitive tasks. The risk? Data privacy gaps, bias in algorithms, and decisions made faster than regulations can catch up. As a CEO, I’m excited about what AI brings to accounting and automation. At VNC Australia, tools like AI-driven reconciliation and predictive reporting are already saving hours each week. But here’s the reality: if we adopt AI without responsibility, we invite risk we can’t fully control. Governments will take years to create universal rules. That means it’s on us - business owners, tech adopters, finance leaders, to create our own ethical frameworks. 1. Audit your AI tools for bias. 2. Define clear data privacy policies. 3. Keep humans in the loop for critical financial decisions. Innovation is only powerful if it’s trusted. How are you balancing speed with responsibility in your AI journey?

Rethinking AI Compute: The Disruption Groq Is Leading AI is pushing the limits of traditional compute architectures — and companies like Groq aren’t just making incremental improvements; they’re redefining the entire approach. While most AI workloads rely on GPUs optimized for parallelism, Groq’s Tensor Streaming Processor (TSP) takes a different path — delivering ultra-low latency, deterministic performance, and greater efficiency. This shift isn’t just technical; it’s strategic. Here’s why it matters: ✅ Real-Time Decisioning: In sectors like financial trading, autonomous vehicles, and defense, milliseconds count. Groq’s architecture cuts latency down to its core. ✅ LLM Optimization at Scale: Running large language models (LLMs) like GPT or LLaMA isn’t just about speed — it’s about predictable performance and cost efficiency. Groq delivers both. ✅ Energy & Cost Efficiency: With rising concerns around AI’s carbon footprint and escalating compute costs, Groq offers a leaner, greener alternative. ✅ Deterministic AI: In applications where consistency is critical — think healthcare diagnostics or industrial automation — Groq ensures reliable outputs. The bigger picture? This is a reminder that real disruption happens when we rethink the fundamentals — not when we just optimize what already exists. As AI models become more complex and compute-intensive, companies that innovate at the hardware level will shape the next generation of AI capabilities. And those that don’t? They’ll be playing catch-up. The question isn’t whether this shift is happening — it’s how quickly industries will adapt. #AI #Groq #Disruption #Innovation #Compute #LLM #DeepLearning #FutureOfTech #Efficiency #Scalability

LinkedIn algorithms will deprioritize this post but important to know the data privacy changes that Linkedin announced recently. And what can you do stay safe. 1. LinkedIn will use member data by default starting November 3, 2025, to train generative AI models that power platform features like content creation and job recommendations. Users can opt out in settings. 2. Types of data used for AI training include profile details (job title, skills, education), public posts, comments, articles, group activities, and job application-related data (resumes, screening questions). Private messages and sensitive information (passwords, payment data) are excluded. 3. LinkedIn expanded data sharing with Microsoft and other LinkedIn affiliates for AI development, advertising, and service improvements. Shared data includes profile information, platform usage, and activity data, under updated legal and privacy terms. 4. Users maintain control over their data through privacy settings, including options to opt out of AI training data use and control affiliate data sharing. Regional privacy laws apply, with stronger protections in the EU, UK, Canada, and other jurisdictions. Here's how to Opt-Out: #1 Disable 'Data for Generative AI Improvement' from: https://lnkd.in/gJPaXew9 #2 Disable 'Share data with affiliates and partners' from: https://lnkd.in/g5fF5x7n #AILiteracy #DataPrivacy #LinkedInChanges #OptOut AI&Beyond Jaspreet Bindra