Machine Learning Algorithms

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

If you’re building with LLMs, these are 10 toolkits I highly recommend getting familiar with 👇 Whether you’re an engineer, researcher, PM, or infra lead, these tools are shaping how GenAI systems get built, debugged, fine-tuned, and scaled today. They form the core of production-grade AI, across RAG, agents, multimodal, evaluation, and more. → AI-Native IDEs (Cursor, JetBrains Junie, Copilot X) Modern IDEs now embed LLMs to accelerate coding, testing, and debugging. They go beyond autocomplete, understanding repo structure, generating unit tests, and optimizing workflows. → Multi-Agent Frameworks (CrewAI, AutoGen, LangGraph) Useful when one model isn’t enough. These frameworks let you build role-based agents (e.g. planner, retriever, coder) that collaborate and coordinate across complex tasks. → Inference Engines (Fireworks AI, vLLM, TGI) Designed for high-throughput, low-latency LLM serving. They handle open models, fine-tuned variants, and multimodal inputs, essential for scaling to production. → Data Frameworks for RAG (LlamaIndex, Haystack, RAGflow) Builds the bridge between your data and the LLM. These frameworks handle parsing, chunking, retrieval, and indexing to ground model outputs in enterprise knowledge. → Vector Databases (Pinecone, Weaviate, Qdrant, Chroma) Backbone of semantic search. They store embeddings and power retrieval in RAG, recommendations, and memory systems using fast nearest-neighbor algorithms. → Evaluation & Benchmarking (Fireworks AI Eval Protocol, Ragas, TruLens) Lets you test for accuracy, hallucinations, regressions, and preference alignment. Core to validating model behavior across prompts, versions, or fine-tuning runs. → Memory Systems (MEM-0, LangChain Memory, Milvus Hybrid) Enables agents to retain past interactions. Useful for building persistent assistants, session-aware tools, and long-term personalized workflows. → Agent Observability (LangSmith, HoneyHive, Arize AI Phoenix) Debugging LLM chains is non-trivial. These tools surface traces, logs, and step-by-step reasoning so you can inspect and iterate with confidence. → Fine-Tuning & Reward Stacks (PEFT, LoRA, Fireworks AI RLHF/RLVR) Supports adapting base models efficiently or aligning behavior using reward models. Great for domain tuning, personalization, and safety alignment. → Multimodal Toolkits (CLIP, BLIP-2, Florence-2, GPT-4o APIs) Text is just one modality. These toolkits let you build agents that understand images, audio, and video, enabling richer input/output capabilities. If you're deep in AI infra or systems, print this out, build a test project around each, and experiment with how they fit together. You’ll learn more in a weekend with these tools than from hours of reading docs. What’s one tool you’d add to this list? 👇 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI infrastructure insights, and subscribe to my newsletter for deeper technical breakdowns: 🔗 https://lnkd.in/dpBNr6Jg

LLMOps is about running LLMs like real products with feedback loops, monitoring, and continuous improvement baked in 💯 This visual breaks it down into 14 steps that make LLMs production-ready and future-proof. 🔹 Steps 1-2: Collect Data + Clean & Organize Where does any good model start? With data. You begin by collecting diverse, relevant sources: chats, documents, logs, anything your model needs to learn from. Then comes the cleanup. Remove noise, standardize formats, and structure it so the model doesn’t get confused by junk. 🔹 Steps 3-4: Add Metadata + Version Your Dataset Now that your data is clean, give it context. Metadata tells you the source, intent, and type of each data point: this is key for traceability. Once that’s done, store everything in a versioned repository. Why? Because every future change needs a reference point. No versioning = no reproducibility. 🔹 Steps 5-6: Select Base Model + Fine-Tune Here’s where the model work begins. You choose a base model like GPT, Claude, or an open-source LLM depending on your task and compute budget. Then, you fine-tune it on your versioned dataset to adapt it to your specific domain, whether that’s law, health, support, or finance. 🔹 Steps 7-8: Validate Output + Register the Model Fine-tuning done? Cool, and now test it thoroughly. Run edge cases, evaluate with test prompts, and check if it aligns with expectations. Once it passes, register the model so it’s tracked, documented, and ready for deployment. This becomes your source of truth. 🔹 Steps 9-10: Deploy API + Monitor Usage The model is ready! You expose it via an API for apps or users to interact with. Then you monitor everything: requests, latency, failure cases, prompt patterns. This is where real-world insights start pouring in. 🔹 Steps 11-12: Collect Feedback + Store in User DB You gather feedback from users: explicit complaints, implicit behavior, corrections, and even prompt rephrasing. All of that goes into a structured user database. Why? Because this becomes the compass for your next update. 🔹 Steps 13-14: Decide on Updates + Monitor Continuously Here’s the big question: Is your model still doing well? Based on usage and feedback, you decide: continue as is or loop back and improve. And even if things seem fine, you never stop monitoring. Model performance can drift fast. 📚 Research and Curation Effort: 4 hours If you've found it helpful, please like and repost it to uplift your network ♻️ Follow me, Bhavishya Pandit, to stay ahead in Generative AI! ❤️ #llm #opensource #rag #meta #google #ibm #openai #gpt4 #ml #machinelearning #ai #artificialintelligence #datascience #python #genai #generativeai #huggingface #openai #linkedin #computervision

For consumer-facing platforms, delivering relevant and personalized recommendations isn’t just about convenience—it’s key to enhancing the traveler experience. In a recent blog post, Expedia Group's Data Science team shared how they’ve refined their property search ranking algorithm to better match user intent and provide more meaningful results. Expedia’s recommendation system is traditionally designed for destination searches, where travelers enter a location and filter to find suitable lodging. In this case, the algorithm ranks properties based on their overall relevance. However, another common scenario is property searches, where users arrive on the platform looking for a specific hotel—often through external channels like search engines. If that property is unavailable, simply displaying top-ranked hotels in the area isn’t the best solution. Instead, the system needs to recommend accommodations that closely match the traveler’s original intent. To tackle this, the Data Science team enhanced their machine learning models by incorporating property similarity into the ranking process. They improved data preprocessing by focusing on past property searches that led to bookings, ensuring the model learns from real traveler behavior. Additionally, they introduced new similarity-based features that compare properties based on key factors like location, amenities, and brand affiliation. These improvements allow the system to suggest highly relevant alternatives when a traveler’s first choice isn’t available, making recommendations feel more intuitive and personalized. While broad recommendation systems lay the foundation for personalization, adapting them to specific user behaviors can greatly improve satisfaction. Expedia’s approach highlights the power of fine-tuning machine learning models to better address evolving business needs. #MachineLearning #DataScience #Algorithm #Recommendation #Customization #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFZSXpMQ

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻����𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

In a Generative Adversarial Network, a piece of technology that has shown for the first time that a machine can be made capable of generating images indistinguishable from man-made ones, a generator network tries to produce realistic data while a discriminator tries to distinguish generated samples from real ones. For this to work, you need a way to measure how close the generated distribution is to the real one — and the standard measures turn out to have a serious flaw: when the two distributions don't overlap much, which is almost always true early in training, they go flat and stop providing useful gradient signals. This paper works through exactly why that happens, shows that the Wasserstein distance — which measures how much "effort" it would take to reshape one distribution into the other — stays well-behaved even in that situation, and builds a practical GAN training algorithm around it that is more stable, less sensitive to architectural choices, and for the first time produces a loss number that actually correlates with output quality. A great example of scientific research where an existing science invented earlier is applied to a new science invented much later, and it all fits. Read online with an AI tutor: https://lnkd.in/e9Sa3Teq Download the PDF: https://lnkd.in/esRKdeCa

Leveraging GANs for Scalable Muon Decay Simulations—Exploration of ML in Particle Physics Data Generation I am delighted to present a significant advancement in my pursuit of integrating particle physics with machine learning: the development of a Generative Adversarial Network (GAN) designed explicitly for simulating datasets related to muon decay observations. Project Overview Muon decay, a fundamental process in particle physics, requires extensive datasets for accurate modeling and analysis. Traditional simulation methods like GEANT4 are computationally intensive, often demanding high-performance GPUs for large-scale simulations. My GAN-based model addresses this limitation by generating synthetic datasets efficiently, even on modest hardware. Key Highlights Efficient Dataset Generation: The GAN's generator produces synthetic muon decay data, replicating real-world decay patterns with remarkable accuracy. Scalability: The model can simulate extremely large datasets, empowering researchers to work with a variety of scales without being constrained by hardware. Minimal Hardware Requirements: Dataset generation requires no high-power GPUs, making it accessible to a broader audience in the scientific community. Development Process The project involved extensive training using real muon decay data, with the GAN trained over 300,000 iterations across several weeks. The result is a robust generator capable of producing datasets indistinguishable from actual experimental data. Github: https://lnkd.in/gTAVEQqP Looking Ahead I’m in the process of compiling a detailed explanation of the GAN’s architecture, training pipeline, and optimization strategies. This includes the technical aspects of handling particle physics data, tuning hyperparameters for complex systems, and achieving the convergence needed for high-fidelity simulations. Stay tuned for a comprehensive breakdown of the entire project, including the code, methodologies, and insights gained along the way. I look forward to sharing this work and exploring its potential applications in advancing data-driven particle physics research. At last but now the least special thanks to Dr. Apoorva Bhatt and Dr. Paawan Sharma for their cooperation. Let me know your thoughts or suggestions!

𝐓𝐡𝐞 𝐪𝐮𝐢𝐞𝐭 𝐬𝐞𝐜𝐫𝐞𝐭 𝐛𝐞𝐡𝐢𝐧𝐝 𝐡𝐢𝐠𝐡-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐋𝐋𝐌𝐬 𝐢𝐬 𝐧𝐨𝐭 𝐦𝐚𝐬𝐬𝐢𝐯𝐞 𝐦𝐨𝐝𝐞𝐥𝐬. 𝐈𝐭 𝐢𝐬 𝐰𝐞𝐥𝐥-𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐡𝐞𝐦  Many teams still focus on pushing bigger models, more GPUs, and expensive infrastructure. But the teams building the most stable, cost-efficient LLMs do something different: they invest heavily in clean data pipelines, efficient processing, and rigorous evaluation. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐀𝐈 𝐭𝐞𝐚𝐦𝐬 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞𝐢𝐫 𝐋𝐋𝐌 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐬𝐭𝐚𝐜𝐤 𝟏. 𝐇𝐢𝐠𝐡-𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐚𝐭𝐚 𝐜𝐮𝐫𝐚𝐭𝐢𝐨𝐧 A reliable model starts with reliable data. Cleaning, filtering, and standardizing datasets reduce hallucinations and make every downstream task more stable and predictable. 𝟐. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 Large-scale training involves billions of tokens. Modular data pipelines with efficient batching, token standardization, and GPU streaming reduce bottlenecks and maximize throughput. 𝟑. 𝐒𝐭𝐚𝐛𝐥𝐞 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐬𝐞𝐭𝐮𝐩 Training is not just about speed, it is about control. Techniques like mixed precision, adaptive scheduling, and checkpointing keep long runs from collapsing or wasting resources. 𝟒. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐦𝐨𝐝𝐞𝐥 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 Strong architecture ensures that the model remains fast, efficient, and flexible. It is the foundation that makes future fine-tuning and adaptation easier. 𝟓. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐭𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 This is where the real differentiation happens. Fine-tuning, alignment, and compression allow you to hit target accuracy without overpaying for compute. 𝟔. 𝐑𝐢𝐠𝐨𝐫𝐨𝐮𝐬 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 This is the part many skip. Clear benchmarks, drift monitoring, and feedback loops ensure your model stays safe, useful, and reliable long after training ends. Building great LLMs is not about brute force. It is about smart engineering choices early in the pipeline. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐚𝐫𝐞𝐚𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐭𝐞𝐚𝐦𝐬 𝐬𝐡𝐨𝐮𝐥𝐝 𝐢𝐧𝐯𝐞𝐬𝐭 𝐦𝐨𝐫𝐞 𝐭𝐢𝐦𝐞 𝐢𝐧? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more #LLM #AIEngineering #MLOps

Exciting breakthrough in making RAG systems more efficient and domain-specific! ServiceNow researchers have developed a novel approach to multi-task retriever fine-tuning that significantly improves retrieval performance across diverse enterprise use cases. >> Key Innovations Multi-Task Architecture The researchers fine-tuned mGTE-base (305M parameters) on multiple retrieval tasks simultaneously, including step retrieval, table retrieval, and field retrieval. Their solution achieved impressive recall scores of 0.90 for both step and table retrieval tasks, substantially outperforming baseline models like BM25 and other embedding models. Technical Implementation The system leverages instruction fine-tuning with a contrastive loss objective and employs sophisticated negative sampling strategies. The training process includes 5,000 steps with a batch size of 32, using gradient checkpointing and the Adafactor optimizer for memory efficiency. Cross-Domain Capabilities What's particularly impressive is the model's ability to generalize across different domains - from IT to HR and finance. The researchers validated this by testing on 10 different out-of-domain splits, demonstrating robust performance even in previously unseen contexts. Multilingual Support The fine-tuned model maintains strong multilingual capabilities, showing impressive results across German, Spanish, French, Japanese, and Hebrew translations of the development dataset. This work represents a significant step forward in making RAG systems more practical and efficient for enterprise applications. The researchers' approach not only improves retrieval accuracy but also addresses crucial real-world concerns like scalability and deployment costs.

Transfer Learning vs. Fine-tuning vs. Multitask Learning vs. Federated Learning, explained visually👇 . . Most ML models are trained independently without any interaction with other models. But real-world ML uses many powerful learning techniques that rely on model interactions. The following animation summarizes four such well-adopted and must-know training methodologies: 1) Transfer learning Useful when: - The task of interest has less data. - But a related task has abundant data. This is how it works: - Train a neural network model (base model) on the related task. - Replace the last few layers on the base model with new layers. - Train the network on the task of interest, but don’t update the weights of the unreplaced layers. Training on a related task first allows the model to capture the core patterns of the task of interest. Next, it can adapt the last few layers to capture task-specific behavior. 2) Fine-tuning Update the weights of some or all layers of the pre-trained model to adapt it to the new task. The idea may appear similar to transfer learning. But here, the whole pretrained model is typically adjusted to the new data. 3) Multi-task learning (MTL) A model is trained to perform multiple related tasks simultaneously. Architecture-wise, the model has: - A shared network - And task-specific branches The rationale is to share knowledge across tasks to improve generalization. In fact, we can also save computing power with MTL: - Imagine training 2 independent models on related tasks. - Now compare it to having a network with shared layers and then task-specific branches. Option 2 will typically result in: - Better generalization across all tasks. - Less memory to store model weights. - Less resource usage during training. 4) Federated learning This is a decentralized approach to ML. Here, the training data remains on the user's device. So in a way, it's like sending the model to data instead of data to model. To preserve privacy, only model updates are gathered from devices and sent to the server. The keyboard of our smartphone is a great example of this. It uses FL to learn typing patterns. This happens without transmitting sensitive keystrokes to a central server. Note: Here, the model is trained on small devices. Thus, it MUST be lightweight yet useful. Model compression techniques are prevalent in such cases. I have linked a detailed guide in the comments. -- If you want to learn AI/ML engineering, I have put together a free PDF (530+ pages) with 150+ core DS/ML lessons. Get here: https://lnkd.in/gi6xKmDc -- 👉 Over to you: What are some other ML training methodologies that I have missed here?