Aishwarya Srinivasan's Linkedin Analytics

I have an exciting project coming up ‼️ 👇 I recently wrapped up my 3-week Maven course, where I had the opportunity to teach 30+ professionals from companies like Google, Cohere, Comcast, eBay, HP, Unilever, Walmart, and Paramount how to build an AI career—without coding. People from several different industries learned how to apply AI tools, build AI automation, and develop AI agents tailored to their unique business use cases. The one big takeaway is:  AI training and upskilling are more vital than ever—especially for business leaders bridging the skill gap to implement AI in real-world scenarios. To address this skill gap, I am working on a new project to train teams at organizations to be able to transform their workflows and to be more efficient using AI tools and AI Agents! My request is simple: if your team (or the one you lead) needs AI upskilling, please fill out this quick intake form to share your specific training needs. I welcome all industries. I’m excited to bring this project to fruition soon. Thank you for your support 💙 👉 https://lnkd.in/dQPEnidd ------------- Share this with your network of AI leaders ♻️

During my time at Google, I had the unique opportunity to collaborate directly with executives and CIOs, guiding their AI strategies. Here are some powerful insights from that journey that significantly accelerated my career growth in AI: 1️⃣ Business leaders think in outcomes, not models. Executives rarely want to delve into the intricacies of model architectures—they prioritize tangible business outcomes. Mastering the art of translating technical complexities into clear, actionable insights makes you indispensable. ✨ Here's my 2 cents: Develop strong storytelling abilities with data. Clearly articulate how your AI initiatives address specific business challenges. 2️⃣ Managing Up and Aligning Leadership is Crucial—and Challenging. Introducing new initiatives, especially in AI, requires significant leadership alignment, visibility, and proactive communication. Often, the hardest part is not technical but navigating organizational dynamics to secure executive buy-in. ✨ Here's my 2 cents: Proactively communicate with leadership, anticipate potential objections, and demonstrate clearly how the initiative aligns with broader organizational goals. Maintain visibility by consistently updating stakeholders and highlighting incremental wins. 3️⃣ Be a Generalist AND a Specialist. Having a broad perspective of AI enables strategic conversations across different business units, while deep domain expertise distinguishes you as a critical resource. Balancing these two dimensions uniquely positions you to connect dots others may overlook. ✨ Here's my 2 cents: Continuously broaden your AI knowledge while concurrently cultivating deep expertise in a particular area. What lessons from your journey have accelerated your growth? #AI #CareerGrowth #TechLeadership

Is AI replacing certain roles? Short-answer, YES! If your current role involves repetitive tasks like data entry, routine financial analysis, or basic customer service, you could be on the front lines of AI-driven disruption. But here’s the good news: AI doesn’t replace people—it replaces tasks. So how can you future-proof your career? 1️⃣ Master AI Tools – Become the go-to person who knows how to leverage AI in your field. 2️⃣ Shift to AI-Augmented Decision Making – Let AI handle the grunt work, and focus on the strategic thinking only humans can do. 3️⃣ Automate Your Own Tasks – Stay indispensable by automating processes before someone else does. 4️⃣ Double Down on Human Skills – Sharpen your creativity, empathy, leadership, and communication—these are irreplaceable. 5️⃣ Keep Learning – New AI innovations pop up daily—commit to learning one new skill each quarter. Here's what you should have as an action plan 👇 → Identify where AI is already changing your industry, start experimenting with the tools, and level up the skills that only humans can bring to the table. → Embrace AI as your ally, not your competitor. The more you use, the better you understand how to use AI best for your role/ business! 👇👇👇 Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and, educational content that will help you stay up-to-date with the data & AI space

If you want to be a Data Scientist or AI Engineer in 2025, start here 👇 This is all you need! 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: 📝 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀: → Advanced ML: Master transformers & self-supervised learning → AutoML: Automate model selection & tuning → Data Viz: Build interactive dashboards & ensure explainability → Cloud: Use serverless & GPU-accelerated analytics → Unstructured Data: Process text, images, video & multimodal data → Specialized Areas: Federated learning, XAI, responsible AI, synthetic data, knowledge graphs, time series 🧰 𝗧𝗼𝗽 𝗧𝗼𝗼𝗹𝘀: → ML Frameworks: TensorFlow, PyTorch, Scikit-learn, JAX, XGBoost, LightGBM, CatBoost, Flax, DeepLearning4j → AutoML: H2O.ai, Google AutoML, Azure AutoML, Auto-sklearn, TPOT, DataRobot, EvalML → Data Viz & BI: Tableau, Power BI, Apache Superset, Looker, Matplotlib, Seaborn, Plotly, Qlik Sense, Grafana → Data Platforms: Snowflake, Databricks, KNIME, Apache Spark, Dask, RAPIDS, BigQuery, Redshift, Apache Flink → Gen AI: ChatGPT, Claude, Hugging Face (has all OSS), LangChain, Llama (Meta), Grok (xAI), DeepSeek → MLOps & Feature Eng.: MLflow, Featuretools, DVC, Kedro, Kubeflow, Flyte, Weights & Biases → Data Annotation: Label Studio, Prodigy, Snorkel, SuperAnnotate, MakeSense, CVAT 𝗙𝗼𝗿 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: 📝 𝗞𝗲𝘆 𝗦𝗸𝗶𝗹𝗹𝘀: → LLMs: Fine-tune & innovate with generative AI → Agentic AI: Build autonomous agents like AutoGPT → Scalable Deployment: Optimize inference via quantization & compression → Edge AI: Deploy models on IoT & mobile devices → Multimodal AI: Combine text, images & video for full-spectrum solutions → Specialized Areas: RAG, AI security, model optimization, orchestration 🧰 𝗧𝗼𝗽 𝗧𝗼𝗼𝗹𝘀: → AI Frameworks: TFX, PyTorch Lightning, FastAI, JAX, Hugging Face, Keras, MXNet, OpenVINO → Cloud AI: AWS SageMaker, Google Cloud AI, Azure AI, IBM Watson, CoreWeave, Vertex AI, Paperspace → Gen AI: OpenAI APIs, Stability AI, Mistral AI, MidJourney, RunwayML, LLaMA, xAI’s Grok, LlamaIndex, Ollama, LangChain → Deployment: NVIDIA Triton, TorchServe, Ray Serve, BentoML, ModelMesh, Seldon Core, KServe, ONNX Runtime → AI Agents: AutoGPT, BabyAGI, CrewAI, LangChain, Haystack, JARVIS (Hugging Face), Semantic Kernel → Dashboards: Plotly Dash, Redash, Domo, Streamlit, Gradio, Voila, Panel, Flask → Data Pipelines: Apache Airflow, Prefect, Dagster, Luigi, Argo Workflows, Mage AI → Optimization: TensorRT, ONNX, Apache TVM, DeepSpeed, Habana Gaudi, Neo (NVIDIA), QAT → Security: Adversarial Robustness Toolbox, Differential Privacy, PySyft, CleverHans, Flower (Federated Learning) Remember: You need to not just keep learning new topics, but you need to apply them with hands-on projects! ↳ In the next post, I will share the portfolio projects you can build that you can add to your resume. -------- 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!

Excited to announce that I’m partnering with Raghav Gupta, the inspiring co-founder of 1% Club and the founder of Futurense Technologies, as an advisor on the incredible Futurense US Pathway Program! This unique program helps students navigate the journey from selecting the right university in the US to launching successful, high-paying careers. You get to experience the best of both worlds, where you start Start with prestigious IITs/IIMs in India and complete onsite at top-ranked US universities. With guaranteed scholarships, end-to-end assistance, extensive loan support, and direct access to top-ranked universities, it’s designed to transform educational dreams into reality. Proud to be part of a mission that’s making education and career success more accessible and achievable. More exciting details coming soon—stay tuned!

2025 isn't just about AI agents—it's about the synergy of Human × AI. This decade's real innovation? Humans and AI collaborating to achieve unprecedented productivity and strategic impact. As an Adobe Summit Virtual Insider in partnership with Adobe Express, I've seen firsthand how Adobe Summit 2025 emphasizes a future driven by collaborative intelligence, where human creativity is amplified by powerful AI technologies. Here’s what's shaping the next era of AI: 1️⃣ Agentic AI Revolution Adobe's agentic AI introduces autonomous agents capable of strategic planning and execution, automating tasks like content resizing, data cleansing, and audience targeting. This lets marketing and creative teams shift focus to higher-level, strategic innovation. 2️⃣ Unified AI Orchestration Adobe’s Experience Platform Agent Orchestrator serves as a unified control center, managing AI agents seamlessly across platforms to deliver consistent, cohesive, and personalized customer journeys at scale. 3️⃣ Advanced Generative AI Adobe's generative AI advancements in video and 3D content production—such as automated video localization, intelligent reframing, and natural language-driven 3D material creation—dramatically enhance creative efficiency and accessibility. 4️⃣ Conversational Analytics Adobe's Data Insights Agent transforms analytics through conversational interfaces, democratizing data insights and empowering teams across all skill levels to make informed, strategic decisions. The future belongs to those who master the art of Human × AI collaboration—combining human ingenuity with AI’s transformative power. PS: I was at Adobe Summit in-person last year, and it was such a great experience meeting Adobe CTO, Eliot Greenfield and chat about Human X AI collaboration, and ethical AI. This pic is from then 👇 #AdobeSummit #AI #GenerativeAI #AdobeInsider #AIExpert #MarketingInnovation #AdobeExpressPartner

I often hear ambitious people say, "I want to be the next Steve Jobs," or "I'm going to be the next Jensen Huang," or even "I aspire to be the next Mark Zuckerberg." While it's admirable to draw inspiration from great minds, there's a fundamental flaw in this mindset. None of these groundbreaking leaders became who they are by trying to replicate someone else's path. They became great precisely because they embraced their unique perspectives, leveraged their own superpowers, and built authentic personal brands. Trying to become the next someone else robs you of your greatest strength: being authentically YOU. Your value isn't found in being a shadow of another person's greatness—it's discovered when you boldly express your own ideas, your own talents, and your own distinct vision. Don't strive to become the next somebody. Commit to becoming the first YOU.

Let's talk about AI Agents vs. Agentic AI (Most people are getting this wrong.) These terminologies are very vague at this time, so let's make it more comprehensive To be clear: AI agents ≠ agentic AI. AI agents have existed for decades. They perform specific tasks but still require human input. They are "instruction following". Examples include: 📍 Waymo self-driving cars 📍 Tesla’s Autopilot 📍 Siri, Alexa, Google Assistant Agentic AI is different. It’s a system of AI agents that operate without human intervention. 🔹 It learns and adapts. 🔹 It optimizes its own performance. 🔹 It makes autonomous decisions. Unlike traditional AI agents, agentic AI includes judges and critics to refine outputs: ✔️ Judges evaluate accuracy. ✔️ Critics identify flaws, biases, and ethical risks. TLDR; 🚀 AI agents = Instruction following AI. 🤖 Agentic AI = autonomous systems that think and act independently. Somewhere AI Agents are a superclass for Agentic AI, and hence they are used interchangeably. I hope this overview of AI Agents and Agentic AI is helpful. ----------- Share this with your network if you found this insightful ♻️ Follow me (Aishwarya Srinivasan) for more AI tid-bits, and hit 🔔 to get notified for my posts 💙

All You Need to Learn About AI Agents 🤖 I've recently checked out the comprehensive e-book "Mastering AI Agents" by Pratik Bhavsar at Galileo🔭 , an excellent resource exploring the practical applications and deep technical insights of AI agents powered by Large Language Models (LLMs). → 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 AI agents use LLMs to autonomously execute complex tasks, make decisions, and dynamically interact with external tools and data. Unlike simpler automated systems, agents actively adapt their actions based on real-time context.  → 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗪𝗵𝗲𝗿𝗲 𝗧𝗵𝗲𝘆 𝗘𝘅𝗰𝗲𝗹 ↳ Fixed Automation Agents: Handle repetitive, predictable tasks (e.g., invoice processing). ↳ LLM-Enhanced Agents: Ideal for flexible, high-volume tasks like content moderation or customer support classification. ↳ ReAct Agents: Excel at strategic multi-step tasks requiring iterative decision-making (e.g., project management). ↳ ReAct + RAG Agents: Necessary for high-stakes environments needing accuracy and real-time information (medical/legal contexts). ↳ Tool-Enhanced Agents: Efficient for complex workflows involving multiple APIs and integrations (e.g., automated coding, data analysis). ↳ Memory-Enhanced Agents: Excellent for personalized, adaptive interactions (CRM, personal assistance). →𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 ↳ LangGraph: Ideal for detailed, complex workflows with graph-based state management. ↳ AutoGen: Great for conversational and intuitive task management. ↳ CrewAI: Optimal for structured, collaborative, multi-agent roles and interactions. →𝗖𝗼𝗺𝗺𝗼𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 & 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 ↳ Infinite Loops: Define clear termination criteria and monitoring. ↳ Cost Efficiency: Optimize model usage strategically, employing smaller LLMs when possible. ↳ Context Accuracy: Implement rigorous evaluations and continuous real-world scenario testing. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗚𝗮𝗹𝗶𝗹𝗲𝗼 𝗔𝗜 𝗳𝗼𝗿 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗔𝗴𝗲𝗻𝘁 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Galileo AI is a powerful platform that enhances AI agent reliability through: ↳ Advanced evaluation and monitoring tools. ↳ Proprietary metrics for precise performance assessment. ↳ Effective optimization strategies to maintain operational efficiency and minimize costs. Integrating Galileo🔭 ensures your agents remain robust, continuously optimized, and consistently aligned with industry best practices. What have you been building with AI Agents? Share your thoughts below 👇 📕 Download the full e-book here: https://shorturl.at/hmIuH ------- If you found it insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI news and educational content to help you stay up-to-date in the AI space!

This morning, I sat in awe during Jensen Huang's keynote at NVIDIA GTC! Here’s what resonated with me: 👉 Agentic AI is Real: Imagine machines that don’t just respond but actually think, reason, and make decisions on their own. It’s not a scene from a sci-fi movie—it’s the next chapter in AI. 👉The Birth of AI Factories: Nvidia is moving beyond traditional data centers. Instead, they’re creating AI factories that generate “tokens”—the essential building blocks for truly intelligent systems. 👉 Game-Changing Blackwell Chips: With a performance boost that’s 40 times over the previous generation, these chips are set to power a new era of efficiency and capability. 👉 Robotics with a Human Touch: Beyond digital breakthroughs, Nvidia is paving the way for robots that can see, understand, and act in our physical world, addressing real labor challenges. 👉 The Dynamo Operating System: Think of it as the command center for these AI factories—automating complex processes so that businesses can harness the full potential of AI. 👉 Announced Fireworks AI X NVIDIA partnership: Fireworks AI now seamlessly integrates with NVIDIA NIM microservices, powered by NVIDIA AI Enterprise. What struck me most is how these innovations are set to change everything—from healthcare to robotics to enterprise solutions. We’re moving from an era where AI simply retrieves data to one where it generates insights, solves intricate problems, and becomes a true partner in innovation. I’m excited about what lies ahead and curious to hear your thoughts. #GTC2025 #Nvidia #AgenticAI #Blackwell #TechInnovation #FutureOfWork

5 GitHub Repositories Every AI Engineer Should Bookmark 🚀 If you're serious about GenAI, these GitHub repos will save you countless hours and make you a better engineer 👇 1. Awesome Generative AI ↳A collection of resources for building, training, and deploying generative AI models. 🔗 https://lnkd.in/d6n9rtkh 2. SmolAgents (Hugging Face) ↳A lightweight framework for building autonomous AI agents. 🔗 https://lnkd.in/dTwRdQMq 3. LangChain ↳The go-to framework for building LLM-powered applications. 🔗 https://lnkd.in/deNjUUkB 4. CrewAI ↳A cutting-edge framework for multi-agent collaboration using AI. 🔗 https://lnkd.in/dHBCPmkX 5. Awesome-LLMOps ↳ A must-have collection of tools for scaling, deploying, and managing LLM applications. 🔗 https://lnkd.in/dvAg3Znd You can use the example code in each of these repos to get started with your projects! Which one is your favorite? P.S. Save this post so you don’t lose these gems. 🔥 ------------ Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources!

Met with Thomas Wolf, co-founder Hugging Face yesterday and recorded a super insightful podcast! We spoke about how he started Hugging Face, how they built such a vibrant AI developer community, how future of LLMs and SLMs are going to look like, his take and inclination on AI + robotics, and many more interesting stuff that you don't want to miss. Stay tuned here, and I will be sharing our interview soon 😄 Thomas is deeply technical and so entrepreneurial at the same time, a very rare combination. It was such a pleasure meeting with his and talking about all things AI.

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!

It's time for one of my fav marketing conferences. As a 9-5 content creator, I cannot emphasize enough how much AI creative tools have helped me be more productive and create content that would have otherwise been way beyond my skills. Last year, it was an absolute blast to be in-person at Adobe Summit, and listen to all the amazing usecases built on Adobe suite, my favorite being the Coca Cola AI marketing pipeline! This year, I am even more excited to partner with Adobe Express, as a Virtual Insider, and get you all some exclusive insights from Adobe Summit. Adobe Summit is happening from 18th-20th March, in Las Vegas. If you are anything like me, and want to dive into how AI is transforming creative space and how you can use a quick and easy tool like Adobe Express to elevate your content, this is the place to be! Register here: https://lnkd.in/gXNfkZnR Adobe #AdobeExpressPartner #AdobeSummit

NVIDIA GTC 2025 just wrapped up, and I’m still buzzing from the incredible energy and announcements that dropped this year. Here are the standout moments that really caught my eye: 1️⃣ Blackwell Ultra AI Chips (end of 2025) ↳ 50% performance boost = faster training, quicker iteration ↳ Big implications for vision + language foundation models 2️⃣ Vera Rubin Series (late 2026) ↳ Focused on compute efficiency & scalability ↳ Could help democratize AI by lowering training costs 3️⃣ NVIDIA Dynamo (open-source inference) ↳ Optimizes resource use + model serving ↳ Real-world deployments could get more stable & cost-effective ↳ Fireworks AI is an official inference partner 4️⃣ Newton Physics Engine ↳ Partnered with DeepMind + Disney Research ↳ Better simulation = safer, more adaptive robotics 5️⃣ Isaac GR00T N1 (open-source humanoid robot) ↳ Foundational model for robotics developers ↳ Sparks innovation in real-world robotic systems 6️⃣ Quantum Lab (Harvard + MIT) ↳ Blending quantum + AI research ↳ Potential to redefine compute boundaries in AI 7️⃣ General Motors Collaboration ↳ Integrating AI into next-gen autonomous vehicles ↳ Safer, more intelligent AVs on the horizon 8️⃣ Fireworks AI x NVIDIA NIM ↳ Scalable, distributed AI inference ↳ Helps get models from lab → production faster I’m especially excited about Dynamo and GR00T N1 for what they could unlock in open-source research. Which of these do you think is most pivotal for the future of AI? PS: Finally, I did manage to get a pic with Jensen 😄 #GTC2025 #NVIDIA ----------- Reshare this ♻️ if it was helpful for your network! Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational content!

If you want to get started in learning about LLMs, here is a easy-to-follow roadmap, I would recommend 👇 📍 Step 1: Fundamentals of Machine Learning & Deep Learning ↳ Machine Learning Course: https://lnkd.in/d_2iakPf ↳ Intro to Deep Learning: https://lnkd.in/dNE69D5j 📍 Step 2: Understanding Transformers & Attention Mechanism ↳ Transformers from Scratch: https://lnkd.in/dnu9WGqC ↳ HuggingFace NLP Course: https://lnkd.in/dMPWewTd 📍Step 3: LLM Pre-training, Fine-tuning & RAG ↳ LLM University: https://cohere.com/llmu ↳ Pre-training LLM Course: https://lnkd.in/dC9CkCSA ↳ Fine-tuning: https://lnkd.in/d2CnYHEJ 📍 Step 4: Applications of LLMs & AI Agents ↳ AI Agents Course: https://lnkd.in/d4K4CUbS ↳ Advanced Large Language Model Agents Course: https://lnkd.in/diBAcg_s ↳ AI Agents Mastery: https://lnkd.in/daTH2_3u ↳ Multi AI Agent Systems with crewAI: https://lnkd.in/d8iu6pap 📕 Top Book Recommendations: ↳ Natural Language Processing with Transformers: https://amzn.to/4ibJAjP ↳ Deep Learning book: https://lnkd.in/dq9pXXtq ↳ AI Engineering by Chip Huyen: https://amzn.to/3FoO9ZA ↳ Building LLMs for Production by Louis-François Bouchard https://amzn.to/3Fan1Ob ↳ LLM Engineer's Handbook by Maxime Labonne: https://amzn.to/3Ffz6By 📚 Key Research Papers: 📄 Attention Is All You Need: https://lnkd.in/dzGaatsJ 📄 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: https://lnkd.in/da9CReM3 📄 GPT-3: Language Models are Few-Shot Learners: https://lnkd.in/dHVGucdG 📄 Retrieval-Augmented Generation for Knowledge-Intensive NLP: https://lnkd.in/d-Ajg2_w 📄 Scaling Transformers to 1 Trillion Parameters: https://lnkd.in/d2-JZ-js 📄 DeepSeek R1 paper: https://lnkd.in/dEcicM2Z

When I moved to the U.S. as an international student to pursue my Master’s in Data Science at Columbia University, I knew it would be an expensive and intense journey. But by the time I graduated, I wasn’t just debt-free—I had actually earned money during my program. How? I strategically combined research assistantships, internships, and scholarships throughout my Master’s. From the very beginning, I sought out research assistant positions, and by my second semester, I secured one that fully covered my tuition and provided a generous stipend. During my summer break, I balanced two internships—one at Columbia and another at IBM —while continuing to work on impactful projects and research. By graduation, I wasn’t just financially ahead, but I had also built a portfolio of high-impact work that propelled my career. Here’s my advice for anyone looking to do the same: 1️⃣ Be proactive about research assistantships: Most professors don’t advertise openings. Reach out directly, express interest in their work, and show how your skills can contribute to their projects. 2️⃣ Ask about scholarships, always: Even at private universities, scholarships and tuition waivers exist. Make it a point to ask professors or program coordinators and negotiate whenever possible. 3️⃣ Never skip negotiations: Whether it’s a stipend or internship salary, don’t settle for the first offer. Many positions are negotiable, and advocating for yourself can significantly increase your earnings. 4️⃣ Choose long-term value over short-term gains: Focus on projects, internships, and assistantships that align with your career goals. While jobs like working in a library or cafeteria might provide instant money, they don’t contribute to long-term success. To my fellow immigrants and international students: I know how overwhelming it can feel to chase your dreams in a new country, often with limited resources and endless challenges. But trust me, every opportunity is out there waiting—you just have to go after it. Be resourceful, stay persistent, and don’t be afraid to ask for help or put yourself out there. Your Master’s program isn’t just about earning a degree; it’s about building a foundation for your future, creating opportunities, and proving to yourself just how far you can go. You’ve got this—let’s make it count! What strategies or lessons have shaped your journey? I’d love to hear your story. 👇👇👇 Share this with your network ♻️ Follow me (Aishwarya Srinivasan) for AI insights, news, and educational resources.

When I started my master's in the U.S., I faced unexpected challenges—not due to academic pressure or adjusting to a new country, but because the U.S. academic system felt completely alien compared to India. In India, education often relies on structured lectures, memorization, and final exams. In the U.S., it was all about continuous assessments, group projects, active class participation, practical discussions, and most importantly being industry-ready. Adapting to this overnight was overwhelming. I often wished I'd done my undergrad in the U.S., but financially it wasn't possible. Doing a master's after in the US, after completing my undergrad in India meant spending two extra years in school and significantly more financial investment. This is why I love the hybrid model offered by Futurense Technologies US Pathway, where I'm now an Advisor with Raghav Gupta helping structure curriculum and university choices. 📍Students start their first semester with one IIT-IIM in India (IIT Jodhpur, IIM Indore, IIM Udaipur), easing into the U.S. academic style, cutting costs, earning a globally valued certification, and even waiving GMAT/ GRE exams. Plus, there's the added advantage of scholarships (up to 65%). 🏁The U.S Universities that are partnering with the Futurense program are Rutgers University, University at Buffalo, Rochester Institute of Technology , Drexel University, Case Western Reserve University, and many more! I genuinely wish something like this existed when I was applying—it would've saved me a lot of stress, and added two more years in my career. If you're thinking about a master's in the U.S., check out Futurense's US Pathway. It's the best of both worlds! https://lnkd.in/d46TVYKb Have you faced similar transitions? I'd love to hear your experiences! 👇 #MSinUS #StudyAbroad #IIT #IIM #Scholarships #FutureReady

10 Portfolio Projects for Aspiring Data Scientists & AI Engineers If you're looking to stand out, work on these 👇 1️⃣ Stock Price Prediction with GenAI Insights Use generative AI to predict stock prices, integrating market sentiment and real-time economic indicators. ↳Tools: OpenAI API, LangChain, LlamaIndex, Prophet, Darts, Hugging Face, PyTorch Lightning, Pandas, Plotly, yFinance 2️⃣ Multimodal Sentiment Analysis Analyze sentiment from diverse data types (text, audio, video) sourced from social media platforms. ↳Tools: OpenAI GPT-4 Vision, Hugging Face Transformers, CLIP, Ollama, PyTorch Lightning, TensorFlow, OpenCV, Gradio, Pillow 3️⃣ Advanced Recommendation Engine Develop a recommendation system incorporating deep learning and user-generated content analytics. ↳Tools: OpenAI API, LangChain, LlamaIndex, Pinecone, Chroma, Surprise, LightFM, TensorFlow Recommenders, Redis, Airflow 4️⃣ AI-driven Customer Segmentation Automate customer segmentation for personalized marketing in e-commerce. ↳ Tools: scikit-learn, PyCaret, PyTorch Lightning, OpenAI API, SHAP, Plotly, Tableau, Pandas 5️⃣ Real-time Fraud Detection with AI Build systems capable of detecting fraudulent activities instantly using transaction data. ↳ Tools: OpenAI API, LangChain, PyTorch Lightning, scikit-learn, XGBoost, Apache Kafka, TensorFlow, Grafana, Prometheus 6️⃣ Predictive Healthcare Analytics Forecast patient health outcomes and potential risks using historical medical data. ↳ Tools: OpenAI API, Hugging Face (ClinicalBERT), LangChain, LlamaIndex, PyTorch Lightning, scikit-learn, Pandas, FHIR API, Streamlit 7️⃣ Real-time Autonomous Image Recognition Create image recognition systems for diagnostics, security, or autonomous navigation. ↳ Tools: OpenAI GPT-4 Vision, Hugging Face ViT/DETR/YOLO, Ollama, PyTorch Lightning, TensorRT, OpenCV, Kafka, NVIDIA Jetson 8️⃣ GenAI-powered Smart Retail Experience Enhance retail customer experience through personalized interactions and inventory management. ↳ Tools: OpenAI API, LangChain, LlamaIndex, Ollama, Pinecone, Chroma, scikit-learn, FastAPI, React.js, Streamlit 9️⃣ GenAI Customer Support Assistant Develop generative AI-powered systems to automate and enhance customer support services, build RAG for fetching internal data. ↳ Tools: OpenAI API, LangChain, LlamaIndex, Ollama, Rasa, Pinecone, Chroma, FastAPI, React.js 🔟 Predictive Maintenance Systems Predict equipment failures and optimize maintenance schedules. ↳ Tools: OpenAI API, LangChain, LlamaIndex, scikit-learn, XGBoost, PyTorch Lightning, Apache Kafka, AWS IoT, Grafana, MLflow ⛩️ Check out the document below for hints for the projects 👇 If you were to pick ONE project from this list, which one would you choose? P.S. Repost this ♻️ so more aspiring Data Scientists & AI Engineers can see it! ---------- Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources 🔔

This is my biggest advice to new graduates 👇 Welcome to the real world—it sucks, you’re gonna love it! (The AI Edition 😂) 1️⃣ Refine the Four Cs: Communication, Collaboration, Curiosity, and Coding ↳ Communication: Even the most powerful AI models lose value if stakeholders can’t clearly understand their impact. Focus on making complex ideas accessible and straightforward. ↳ Collaboration: AI is deeply interdisciplinary—solutions emerge from teamwork across healthcare, finance, climate tech, ethics, and more. Build partnerships and speak your team’s language early on. ↳ Curiosity: In AI, today’s innovation quickly becomes tomorrow’s baseline. Keep exploring new technologies, models, and methodologies. Continuous learning is your superpower. ↳ Coding: Even with the rise of low-code and no-code tools, strong technical foundations set you apart. Deep coding knowledge remains critical for building innovative solutions. 2️⃣ Get Hands-On with Agentic AI Autonomous AI agents—intelligent assistants capable of completing tasks independently—are mainstream in 2025. Understand their strengths and limitations. Be ready to step in, troubleshoot, and optimize, treating your AI agents like valuable team members. 3️⃣ Master Multimodal AI AI isn’t limited to just text or code anymore. Today’s leading models effortlessly blend text, speech, images, and video. Develop your skills with multimodal tools such as GPT-Vision, Google’s Gemini, or DeepMind’s latest offerings to stay versatile and relevant. 4️⃣ Prioritize Ethics and Compliance Ethics and regulation (like Europe’s AI Act, US AI Bill, Copyright Protection Act) aren’t optional—they’re fundamental. Be proactive, ensuring your AI solutions are transparent, fair, and accountable. Anticipate ethical implications early to build responsible, trusted technologies. 5️⃣ Become Familiar with Cutting-Edge Infrastructure AI hardware advancements, like NVIDIA ’s Vera Rubin and BlackWell Ultra are pushing the memory and inference speeds. Learning about modern infrastructure—chips, data centers, cloud operations—will enhance your ability to scale and deliver reliable solutions, making you indispensable. 6️⃣ Align AI Innovations with Business Strategy Organizations in 2025 increasingly seek measurable returns from AI. Focus on solving real-world business problems and clearly communicate your project’s impact—whether it’s reducing costs, boosting efficiency, or driving revenue growth. 7️⃣ Cultivate a Growth Mindset AI moves quickly, and your greatest advantage is your willingness to learn and adapt. Be open to exploring new ideas, actively seek opportunities to expand your skillset, and don’t shy away from challenges—they’re your best opportunities for growth. Share this guide with fellow professionals navigating their own AI journeys ♻️ Follow me (Aishwarya Srinivasan) for more actionable AI insights to thrive in 2025 and beyond!