Utilizing Project Management Frameworks

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: 𝗪𝗵𝗲𝗿𝗲 𝗗𝗼 𝗬𝗼𝘂 𝗘𝘃𝗲𝗻 𝗦𝘁𝗮𝗿𝘁? Over the last few months, I’ve been exploring what it really takes to go from a simple chatbot to a fully autonomous 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 — something that can 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗰𝘁, 𝗹𝗲𝗮𝗿𝗻, 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 on its own. And one thing became clear: 𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝗮𝗹𝗹 𝗮𝘁 𝗼𝗻𝗰𝗲. 𝗬𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗶𝘁 𝗶𝗻 𝗹𝗮𝘆𝗲𝗿𝘀. That’s why I created this 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 — to break down the full stack of an agentic AI system into 6 clear modules: ↳ 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 – Web apps, chatbots, APIs using tools like Next.js, FastAPI, Streamlit ↳ 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 – AutoGen, CrewAI, LangGraph coordinating tasks across agents ↳ 𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – Services like Zapier, Make, OpenAI Functions to act in the real world ↳ 𝗖𝗼𝗿𝗲 𝗟𝗼𝗴𝗶𝗰 – Memory, reasoning, and decision-making with LangChain, LlamaIndex ↳ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 – LLMs like GPT-4, Claude, Mistral, and Whisper for intelligence ↳ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 – Cloud, containers, and DBs: AWS, Azure, GCP, SingleStore, Docker ➤ It's not just about plugging in a GPT model. Agentic AI is about combining 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 + 𝗮𝗰𝘁𝗶𝗼𝗻 + 𝗺𝗲𝗺𝗼𝗿𝘆 + 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 — across a coordinated system. If you're a dev, architect, or founder thinking about how to build this — I hope this gives you a clear path forward. Would love to hear from others: Which part of this stack are you working on right now? What challenges are you seeing in building real-world AI agents?

Project Management Cheat Sheet 1. Key Phases of a Project 1.1. Initiation: Define the project scope, goals, and objectives. Identify stakeholders. Develop a business case or project charter. 1.2. Planning: Create a project plan (scope, timeline, budget, resources). Develop a Work Breakdown Structure (WBS). Identify risks and plan mitigation strategies. 1.3. Execution: Assign tasks to team members. Monitor progress and ensure quality deliverables. Manage stakeholder communication. 1.4. Monitoring & Controlling: Track project performance against KPIs (e.g., cost, time, scope). Manage risks and implement changes. Conduct regular status updates and reviews. 1.5. Closure: Deliver the final product or service. Obtain client or stakeholder sign-off. 2. Common Project Management Methodologies Waterfall: Sequential approach (ideal for predictable projects). Agile: Iterative and flexible (ideal for dynamic projects). Scrum: Framework under Agile with sprints. Kanban: Visual task management using boards. PRINCE2: Process-driven framework focused on control. 3. Essential Documents and Tools 3.1. Documents: Project Charter Project Plan Risk Register Gantt Chart Issue Log Stakeholder Register 3.2. Tools: Task Management: Trello, Asana, Jira Timeline Planning: Microsoft Project, Smartsheet Communication: Slack, Microsoft Teams Collaboration: Google Workspace, Miro 4. Project Management Metrics (KPIs) Schedule Performance Index (SPI): Actual progress vs. planned progress. Cost Performance Index (CPI): Earned value vs. actual costs. Burn Rate: Rate of spending project budget. Milestone Completion: Percentage of milestones completed on time. Customer Satisfaction: Stakeholder or client feedback. 5. Risk Management Process Identify risks (brainstorming, checklists). Assess risks (impact and probability). Plan risk responses (mitigate, transfer, accept, avoid). Monitor and control risks throughout the project. 6. Tips for Effective Project Management Define Clear Objectives: Ensure everyone understands the goals. Communicate Often: Keep stakeholders updated. Prioritize Tasks: Focus on high-value activities. Stay Flexible: Be ready to adapt to changes. Document Everything: Maintain proper records for accountability. Use Technology: Leverage tools to streamline workflows. Evaluate Performance: Regularly review team and project performance. 7. Common Challenges and Solutions 7.1. Scope Creep: Solution: Define scope clearly and use a change management process. 7.2. Poor Communication: Solution: Establish clear communication channels and regular updates. 7.3. Budget Overruns: Solution: Monitor spending closely and manage risks proactively. 7.4. Missed Deadlines: Solution: Use detailed planning and track progress frequently. 7.5. Resource Allocation Issues: Solution: Use resource management tools and prioritize tasks. Keep this cheat sheet handy to ensure you stay on top of your project management responsibilities and deliver successful outcomes!

Microsoft's "𝗚𝗼𝗹𝗱𝗲𝗻 𝗣𝗮𝘁𝗵" 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀. If you're building AI agents, this architecture diagram is your blueprint for success. Microsoft has outlined their recommended approach for production-ready agentic AI systems, and it's comprehensive: 1. 𝗖𝗼𝗿𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: Azure AI Foundry as your central hub Built-in AI tools (file search, code interpreter) Seamless observability across your entire stack 2. 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿: Multi-agent orchestration powered by Microsoft Agent Framework, enabling sophisticated agent collaboration and task delegation. 3. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗦𝘁𝗮𝗰𝗸: BYO resources: Cosmos DB, Key Vault, Azure Storage, AI Search AI tool resources: Azure AI Search, Logic Apps, Azure Functions Integration points: Fabric, SharePoint, External APIs, MCP servers, A2A Servers Deployment: Containerized workloads (Azure Container Apps, etc.) 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: This isn't just about building a chatbot. It's about creating scalable, observable, and maintainable AI agent systems that can handle real enterprise workloads. The "golden path" approach means you're following battle-tested patterns that Microsoft has validated across countless implementations. Are you building with AI agents? What's been your biggest challenge so far? #AI #ArtificialIntelligence #Azure #AIAgents

OpenAI CPO: Evals are becoming a core skill for PMs. PM in 2025 is changing fast. PMs need to learn brand new skills: 1. AI Evals (https://lnkd.in/eGbzWMxf) 2. AI PRDs (https://lnkd.in/eMu59p_z) 3. AI Strategy (https://lnkd.in/egemMhMF) 4. AI Discovery (https://lnkd.in/e7Q6mMpc) 5. AI Prototyping (https://lnkd.in/eJujDhBV) And evals is amongst the deepest topics. There's 3 steps to them: 1. Observing (https://lnkd.in/e3eQBdMp) 2. Analyzing Errors (https://lnkd.in/eEG83W5D) 3. Building LLM Judges (https://lnkd.in/ez3stJRm) - - - - - - Here's your simple guide to evals in 5 minutes: (Repost this before anything else ♻️) 𝟭. 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 Start with 100 diverse traces of your LLM pipeline. Use real data if you can, or systematic synthetic data generation across key dimensions if you can't. Quality over quantity here: aggressive filtering beats volume. 𝟮. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗢𝗽𝗲𝗻 𝗖𝗼𝗱𝗶𝗻𝗴 Read every trace carefully and label failure modes without preconceptions. Look for the first upstream failure in each trace. Continue until you hit theoretical saturation, when new traces reveal no fundamentally new error types. 𝟯. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗬𝗼𝘂𝗿 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗠𝗼𝗱𝗲𝘀 Group similar failures into coherent, binary categories through axial coding. Focus on Gulf of Generalization failures (where clear instructions are misapplied) rather than Gulf of Specification issues (ambiguous prompts you can fix easily). 𝟰. 𝗕𝘂𝗶𝗹𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗼𝗿𝘀 Create dedicated evaluators for each failure mode. Use code-based checks when possible (regex, schema validation, execution tests). For subjective judgments, build LLM-as-Judge evaluators with clear Pass/Fail criteria, few-shot examples, and structured JSON outputs. 𝟱. 𝗗𝗲𝗽𝗹𝗼𝘆 𝘁𝗵𝗲 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗙𝗹𝘆𝘄𝗵𝗲𝗲𝗹 Integrate evals into CI/CD, monitor production with bias-corrected success rates, and cycle through Analyze→ Measure→ Improve continuously. New failure modes in production feed back into your evaluation artifacts. Evals are now a core skill for AI PMs. This is your map. - - - - - I learned this from Hamel Husain and Shreya Shankar. Get 35% off their course: https://lnkd.in/e5DSNJtM 📌 Want our step-by-step guide to evals? Comment 'steps' + DM me. Repost to cut the line. ➕ Follow Aakash Gupta to stay on top of AI x PM.

If you want a standout portfolio in 2025 as a beginner Data Scientist or AI Engineer, use this framework👇 1. Select a Meaningful Problem → Choose a real-world issue you're genuinely interested in (e.g., climate change prediction, healthcare improvements, social media analytics) → Clearly define the objective and the potential impact of solving this issue 2. Acquire and Document Data → Use reliable sources (Kaggle, UCI Repository, Hugging Face) → Clearly document your process for selecting and gathering the data 3. Data Preparation → Clean and preprocess the data thoroughly → Outline key steps (handling missing data, normalization, feature engineering) 4. Exploratory Data Analysis (EDA) → Generate visualizations and summary statistics → Clearly state insights and how they guide your modeling decisions 5. Select Appropriate Algorithms → Choose suitable methods (e.g., Transformer models, XGBoost, clustering) → Provide reasoning for your choice based on the problem and data 6. Develop and Optimize Your Model → Write clean, reproducible, and modular code → Clearly document model experimentation, model training, hyperparameter tuning, and validation steps 7. Evaluate Your Model → Use relevant metrics (ROC-AUC, F1-score, RMSE, BLEU, MMLU) → Present your evaluations clearly, including visualizations like ROC curves or confusion matrices 8. Analyze Results Critically Clearly interpret outcomes, discuss strengths, limitations, and biases Suggest realistic improvements and next steps 9. Deploy Your Model (Optional) → Create a simple web app using tools like Streamlit, Hugging Face Spaces, Flask, or FastAPI → Provide a working demo and clearly document its functionality 10. Comprehensive Documentation → Write a professional, detailed README. → Clearly summarize your project's purpose, methodology, results, and real-world relevance 11. Let your work talk → Share the code, data catalog, and documentation to reproduce on GitHub → Write a detailed blog about interesting insights and outcomes from the project, and share it on Substack/ Medium/ LinkedIn article You can use this framework to build as many projects as you like. While doing multiple projects make sure to explore different use-cases and different algorithms, which will help you get a holistic view of the Data & ML space. PS: LinkedIn post has character limit, so I will be sharing a list of portfolio projects I would recommend to start with, in the next post -------- Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources to keep you up-to-date about the AI space!

One of the pros of working with large organizations is learning their processes and frameworks. Over the years they have developed and perfected these processes to manage growth professionally. Procter & Gamble was my only corporate job post MBA before I plunged into entrepreneurship and if I had to pick ONE company to learn marketing and brand building it would be P&G hands down. In the 3 years I spent there, I learnt a lot about marketing concepts but one framework that has stuck with me till today is the P.A.C.E framework for project management. The concept is intuitively simple when you think about it. Every project has multiple stake holders, and contributors in direct or indirect capacity. But ensuring that the project gets executed within time and the desired objective is the responsibility of one person. Here's what the P.A.C.E framework is P - Process Owner The person responsible for owning and delivering the project end to end. And most importantly, within the timeline. He/She has to ensure all team members and external partners are completing their tasks and keep the project moving towards completion. E.g - Brand Manager is the P for Marketing Projects. A - Approver The person who approves the project along with the resources needed to complete it - both team and money. And reviews the project from time to time with the P for any risks. E.g - Brand Director. C - Contributor Internal team members who can contribute for various parts of the project based on their expertise and give multiple points of view on the execution. E.g. Multifunction team (Sales/R&D/Supply) E - Executor The person or an agency responsible for direct execution of the project and making it launch ready. Eg. A Creative/Design agency This is such a simple, yet extremely powerful framework of project management. Not just complex projects, but even smaller projects should use the PACE framework and assign these 4 team members for absolute clarity on project ownership and execution speed. Highly recommend everyone to apply PACE at work and get amazing outputs from your projects within deadlines 🍧

New Project? New Team? Here's How to Hit the Ground Running Starting something new, whether it's a project or a role, can be both exciting and terrifying. As I’m gearing up to take on a new program to expand my current scope, It’s got me thinking about all the lessons I’ve learned about navigating those first few critical steps. Whether you're joining a new company, taking on a new role, or simply starting a new project with a new team, these tips can help you make a strong start: ✨ Embrace the "Beginner's Mind" ✨ Don't be afraid to ask questions, even those that seem "basic." It shows you're engaged and eager to learn. Take notes and do your research offline to deepen your understanding. ✨ Find the Sweet Spot of Knowledge ✨ Read the existing documentation and materials, but don't get stuck on every detail. Focus on understanding the big picture and the key challenges. ✨ Acknowledge Expertise (Yours and Theirs) ✨ Recognize the team's expertise in their respective areas, but also confidently own your own expertise. After all, there's a reason you're leading this project! ✨ Define the "Why" and the Boundaries ✨ Work with the team to create a clear charter that defines the problem you're solving, the project's scope, and, equally importantly, what's NOT in scope. This sets expectations and prevents scope creep. ✨ Build Relationships Early On ✨ Take the time to get to know your team members as individuals. Understand their strengths, their working styles, and their motivations. Strong relationships are the foundation of successful projects. ✨ Don't Be Afraid to Challenge (Respectfully) ✨ You're bringing a fresh perspective. Don't hesitate to challenge assumptions and suggest new approaches. But always do so respectfully and collaboratively. ✨ Overcommunicate (Especially at the Start) ✨ Keep everyone informed about your progress, challenges, and decisions. Transparency builds trust and ensures alignment. Starting a new project is like embarking on an adventure. There will be challenges, surprises, and hopefully, great rewards. By embracing a proactive mindset, building strong relationships, and focusing on clear communication, you can set yourself up for success. I'm eager to put these tips into practice on this new program, and I promise to keep you updated on my progress and lessons learned. Do you have any other advice for me as I ramp up? Anything specific you'd like to know more about? Share your thoughts in the comments! 👇 – 👉 Follow me, Rony Rozen, for real-world insights on tech leadership.

90% of data projects fail because of bad data, not bad models. (Learnt it the hard way!) 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐭𝐡𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Everyone talks about fancy algorithms and cutting-edge models. But your analysis is only as good as your data. And most data? It's a mess. Duplicates. Missing values. Inconsistent formats. Different time zones. 𝐓𝐡𝐞 4-𝐬𝐭𝐞𝐩 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐈 𝐮𝐬𝐞 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭: 𝟏. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐚𝐤𝐞 & 𝐀𝐮𝐝𝐢𝐭 → Check schema, completeness, and validity first → Hunt for duplicates and PII data → Visualize missing patterns (they tell a story) → Master this: Your foundation determines everything 𝟐. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 – 𝐅𝐢𝐱 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 & 𝐄𝐫𝐫𝐨𝐫𝐬 → Standardize labels (yes/Yes/YES → yes) → Merge duplicates the smart way → Fix units and time zones NOW, not later → Pro tip: Document every transformation 𝟑. 𝐈𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧, 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠 & 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐞𝐩 → Handle missing data based on business logic → Encode categoricals without data leakage → Scale numerics appropriately → Engineer features that actually matter 𝟒. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞, 𝐒𝐩𝐥𝐢𝐭 & 𝐏𝐚𝐜𝐤𝐚𝐠𝐞 → Recheck data integrity post-cleaning → Split datasets properly (no leakage!) → Version your outputs → Generate validation reports 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: ↳ Clean data = trustworthy insights ↳ Proper prep saves weeks of debugging ↳ Stakeholders trust consistent, validated data ↳ Your models actually work in production Remember: Great models start with great data. Not the other way around. Master data cleaning. Build analyses that actually deliver value. Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp ♻️ Save this framework for your next data project 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

FP&A and finance teams often talk about “telling a better financial story.” But nobody talks about frameworks to make it a repeatable, fast, and CFO-ready story. Here’s the truth: Most FP&A teams don’t have a storytelling problem. They have a structure problem. Below is a toolkit with 6 frameworks that top FP&A teams use to turn raw data into executive-grade narratives. Transparent, mechanical, repeatable. 1. AIR Framework (Actuals–Insights–Recommendations) Deliver a KPI story by stating what happened, why it happened, and what should happen next. 2. The 3×3 Variance Story Explain any variance with three facts, three drivers, and three actions. 3, FP&A Pyramid (What–So What–Now What) Move from data to impact to required decisions in a tight narrative. 4. Driver–Bridge Framework Break financial movement into quantified drivers from start value to end value. 5. CFO Decision Sheet Present a decision, the options, the financial impacts, and the associated risks. 6. The One-Slide Forecast Story Summarize forecast direction, key drivers, planned actions, and confidence level on a single slide. Hope this helps and let me know in the comments which one you like the most! Also if you want the Excel with the datasets for the visuals in the examples just let me know!

Way too many data projects fail. Not because the analysis was wrong but because the goal was never clear to begin with. Before you dive into the data, make sure you understand what problems you actually try to solve and for whom. 𝗔𝘀𝗸 𝘁𝗵𝗲𝘀𝗲 𝟴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝘆 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁: 1. What is the actual business question? 2. Who are the stakeholders? 3. What decisions will this analysis support? 4. What data is available? 5. What pieces are missing? 6. What format is expected? 7. What does success look like? 8. What is the timeline and urgency? Answering these upfront can save hours of rework and ensure your results will get used. What’s the one question you wish you had asked before your last data project? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find these questions helpful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #dataproject #stakeholdermanagement  #careergrowth