Prateek Mohta

Choosing the Right Framework for AI Applications! When building AI applications, selecting the right framework can make all the difference. Here's a quick guide to help you choose based on your project requirements: 🔍 Need RAG & Data Processing? 👉 Go with LlamaIndex ✅ Best for: Knowledge graphs, document indexing, structured data access 🔄 Need Sequential Workflow? 👉 Opt for LangGraph ✅ Best for: Data processing pipelines, multi-step workflows, state management 🤝 Need Team Collaboration? 👉 Choose Crew AI ✅ Best for: Task delegation, role-based tasks, Human-AI collaboration 🧠 Need Autonomous Agents? 👉 Select Microsoft AutoGen ✅ Best for: Multi-agent chat, complex reasoning, autonomous interactions 🔗 Need Search & Pipeline Focus? 👉 Go for Haystack ✅ Best for: Search applications, QA systems, modular pipelines This decision-making chart simplifies the complexity of AI framework selection, ensuring your project is equipped for success. 💡 What frameworks are you using for your AI applications? Let's discuss in the comments!

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I have covered 2 research papers on RAG (Retrieval Augmented Generation) in my recent youtube videos. 🚀 1. Self RAG [published Oct 2023] ✅ 2. Corrective RAG (C-RAG) [published Oct 2024] ✅ In the videos, we implemented the above papers from scratch in LangChain and Python and also did a detailed Code walkthrough. If you're interested to know how to build them, feel free to give them a watch! 💯 These are a part of my "AI Engineering 101" playlist. ❤️ Link to the channel is in the comments. 🔗 #aiengineer #youtube #rag #programming #coding

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Introducing DynaRoute - intelligent model routing for efficient LLM usage At Vizuara, we (Pritam Kudale, Pranavodhayam P R, Raj, Rajat) have spent the last few months studying how teams use large language models in production. What we found was simple but eye-opening. Most teams rely on one large and expensive model for every kind of query, whether it is a quick factual question or a complex reasoning task. This works, but it wastes a lot of money and computing power. That is what led us to build DynaRoute (https://lnkd.in/dGSx2KRq), an intelligent routing layer that automatically analyzes your queries and sends them to the most suitable model based on complexity, cost, and performance. You do not have to change your application or rebuild your backend. You can continue using your familiar OpenAI-style API, just by switching the base URL and key. From that point, DynaRoute handles everything intelligently behind the scenes. In our internal benchmarks, DynaRoute delivered remarkable results. Real-world employee questions achieved up to 92 percent cost savings, while code-related queries saw around 74 percent savings. All of this happened while maintaining nearly the same quality and accuracy as top-tier models. For teams that are running customer support systems, coding assistants, agents, or RAG-based pipelines, DynaRoute can reduce your inference bills drastically while preserving the quality your users expect. You can think of it as a smart, adaptive router that continuously learns how to choose between models like GPT-4.1-nano, Gemini 2.5 Flash, and Claude Sonnet, depending on the query. We also provide API access so you can integrate DynaRoute directly into your own platform or agent framework. The API is OpenAI-compatible and can be configured with just a few lines of code. Here are our current plans: Free Tier: ₹0 per month, includes up to 50,000 tokens, OpenAI-compatible endpoint, and access to premium models. Plus Tier: ₹888.30 per month, includes up to 3 million tokens, unlimited requests, access to premium models, and priority support. Enterprise Tier: completely custom implementation for companies that need dedicated support, detailed analytics, and on-premise deployment. We also provide custom routing models for enterprise-level optimization. We are currently offering Enterprise Plan API access to selected partners who wish to integrate DynaRoute into their platforms. If you want to optimize the cost and performance of your LLM-based applications without compromising output quality, give DynaRoute a try. 👉 Learn more and get your API key at https://lnkd.in/gTrs9NWA

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From concept to implementation to diagnostics — the DiD series is now complete! 🎉 Over the past few weeks, I been sharing a blog series with Banani Mohapatra breaking down causal inference methods - from intuition to methodology. We started with the intuition and assumptions, then moved to a step-by-step implementation guide, followed by a real-world case study on free shipping, and finally wrapped up with diagnostics and robustness checks. If you're a data scientist working with product or growth teams, this is a foundational tool for your causal inference toolkit. Catch up on the full series here and subscribe to the newsletter: https://lnkd.in/ethamg6R If you are curious to learn more from Banani and me, sign up for our upcoming AB Testing workshop: https://lnkd.in/gpxBwvrM

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What’s common in the tech stack of Netflix, Reddit, and Airbnb? -> Yes , you guessed it right — Flask is one of the common framework used by all and other many tech companies and startup 🧠🔥 At Vizuara, I just recorded a new video lecture for our 'ML in Production' series, explaining Flask with a practical machine learning demonstration. This is Part 1 of our upcoming end-to-end ML pipeline project, where we’ll walk through the entire journey — from building models to deploying them as interactive web applications. 🎥 Watch the video here: https://lnkd.in/g6W7rAqk 📘 What we cover in this session: 🔹 What is Flask and why it’s popular for web + ML apps 🔹 What is difference between flask and Django 🔹 How to integrate a Machine Learning model with Flask ✅ Bonus: A full hands-on implementation using the Iris dataset 🗒️ Lecture Notes: https://lnkd.in/g4diPpNG ✍️ Read our detailed Substack article: https://lnkd.in/gg9_duQ7 We’re just getting started in developing full end to end ML Project — the upcoming parts will dive deeper into building a complete ML pipeline, including data preprocessing, model training, deployment, and monitoring! If you have any Suggestions or Feedback for us , Feel free to contact us. #Flask #MLDeployment #MachineLearning #Python #IrisDataset #ModularCoding #Vizuara #DataScience #MLOps #TechEducation #AI

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🚀 Excited to share insights from Machine Learning Systems by Prof. Vijay Janapa Reddi, a comprehensive guide that explores the principles, practices, and real-world impact of AI engineering. From the evolution of AI 🤖 to cloud, edge, and tiny ML 🌐📱, this work dives into training, deployment, efficiency, and responsible AI 🌱 while highlighting challenges and opportunities ahead. A must-read for engineers, researchers, and innovators shaping the future of intelligent systems. 🌍💡 #MachineLearning #AI #MLOps #ResponsibleAI #Innovation

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Machine learning is no longer just a tech buzzword—it's transforming how we approach economic analysis, policy design, and forecasting. For students of economics, here are some important ML models worth understanding: 1. Linear and Logistic Regression – Still the backbone for causal inference and classification problems in economics. 2. Decision Trees and Random Forests – Great for handling non-linear relationships, policy evaluation, and survey analysis. 3. Support Vector Machines (SVM) – Useful for classification tasks with clear margins, like credit risk scoring or customer segmentation. 4. K-Means Clustering – Helps in market segmentation, regional economic clustering, and unsupervised pattern discovery. 5. Principal Component Analysis (PCA) – A powerful technique for dimensionality reduction in large economic datasets. 6. Time Series Models with ML Enhancements – Combining ARIMA with ML models for better forecasting of inflation, GDP, or unemployment. 7. Gradient Boosting (XGBoost, LightGBM) – High-performing models often used in economic competitions and real-world policy simulations. 8. Neural Networks (ANNs and RNNs) – Used in high-frequency trading, sentiment analysis, and nowcasting economic indicators. 9. Natural Language Processing (NLP) Models – For analyzing economic news, speeches, financial reports, and social media sentiments. 10. Causal Inference Models with ML (e.g., Double ML, EconML)– Advanced tools that blend econometrics with ML to extract credible causal relationships. Whether you’re aiming for academia, data science roles, or policy research, understanding these models can give you a big edge. #MachineLearning #Economics #DataScience #QuantitativeResearch #Econometrics #PolicyTech #EconStudents

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Recently, I came across news about PhysicsWallah’s new AI model that beat GPT-4 on JEE math questions. Naturally, the internet split in two—some proudly calling it India’s moment, others waving the “it’s just fine-tuning” flag. Here’s the reality: This is a small language model, not a large one. And its job wasn’t to write novels or generate Python—it was built specifically to excel at JEE math. Which it did, remarkably. But here’s where many get it wrong. Just because a domain-specific model outperforms GPT-4 on one task doesn’t make it more “intelligent.” That’s like saying a calculator is better than a scientist because it computes faster. So I wrote this piece to help business leaders, investors, and AI enthusiasts avoid falling for misleading headlines like “We built a model better than GPT-4 at X.” It probably is. But only at X. The article breaks down: • The difference in training goals, data, and infrastructure • Why smaller models are cheaper, safer, and sometimes better—but not general • How to spot hype vs actual product strategy Give it a read below if you’ve ever caught yourself thinking “why not just build our own GPT?” https://lnkd.in/gT-_JWnZ #AI #LLM #SmallLanguageModels #GPT4 #EdTech #DeepTech #AIProduct #MachineLearning #DomainSpecificAI #VC #AIForBusiness #LinkedInAI #AIethics #AIgovernance #LLMvSLM

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