Machine Learning Model Tuning

What does an 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 look like? MLOps practices are there to improve Machine Learning Product development velocity, the biggest bottlenecks happen when Experimentation Environments and other infrastructure elements are integrated poorly. Let’s look into the properties that an effective Experimentation Environment should have. As a MLOps engineer you should strive to provide these to your users and as a Data Scientist, you should know what you should be demanding for. 𝟭: Access to the raw data. While handling raw data is the responsibility of Data Engineering function, Data Scientists need the ability to explore and analyze available raw data and decide which of it needs to be moved upstream the Data Value Chain (2.1). 𝟮: Access to the curated data. Curated data might be available in the Data Warehouse but not exposed via a Feature Store. Such Data should not be exposed for model training in production environments. Data Scientists need the ability to explore curated data and see what needs to be pushed downstream (3.1). 𝟯: Data used for training of Machine Learning models should be sourced from a Feature Store if the ML Training pipeline is ready to be moved to the production stage. 𝟰: Data Scientists should be able to easily spin up different types of compute clusters - might it be Spark, Dask or any other technology - to allow effective Raw and Curated Data exploration. 𝟱: Data Scientists should be able to spin up a production like remote Machine Learning Training pipeline in development environment ad-hoc from the Notebook, this increases speed of iteration significantly. 𝟲: There should be an automated setup in place that would perform the testing and promotion to a higher env when a specific set of Pull Requests are created. E.g. a PR from feature/* to release/* branch could trigger a CI/CD process to test and deploy the ML Pipeline to a pre-prod environment. 𝟳: Notebooks and any additional boilerplate code for CI/CD should be part of your Git integration. Make it crystal clear where a certain type of code should live - a popular way to do this is providing repository templates with clear documentation. 𝟴: Experiment/Model Tracking System should be exposed to both local and remote pipelines. 𝟗: Notebooks have to be running in the same environment that your production code will run in. Incompatible dependencies should not cause problems when porting applications to production. It can be achieved by running Notebooks in containers. Did I miss something? 👇 #GenAI #LLM #LLMOps #MachineLearning

If you’re an AI engineer trying to optimize your LLMs for inference, here’s a quick guide for you 👇 Efficient inference isn’t just about faster hardware, it’s a multi-layered design problem. From how you compress prompts to how your memory is managed across GPUs, everything impacts latency, throughput, and cost. Here’s a structured taxonomy of inference-time optimizations for LLMs: 1. Data-Level Optimization Reduce redundant tokens and unnecessary output computation. → Input Compression:  - Prompt Pruning, remove irrelevant history or system tokens  - Prompt Summarization, use model-generated summaries as input  - Soft Prompt Compression, encode static context using embeddings  - RAG, replace long prompts with retrieved documents plus compact queries → Output Organization:  - Pre-structure output to reduce decoding time and minimize sampling steps 2. Model-Level Optimization (a) Efficient Structure Design → Efficient FFN Design, use gated or sparsely-activated FFNs (e.g., SwiGLU) → Efficient Attention, FlashAttention, linear attention, or sliding window for long context → Transformer Alternates, e.g., Mamba, Reformer for memory-efficient decoding → Multi/Group-Query Attention, share keys/values across heads to reduce KV cache size → Low-Complexity Attention, replace full softmax with approximations (e.g., Linformer) (b) Model Compression → Quantization:  - Post-Training, no retraining needed  - Quantization-Aware Training, better accuracy, especially <8-bit → Sparsification:  - Weight Pruning, Sparse Attention → Structure Optimization:  - Neural Architecture Search, Structure Factorization → Knowledge Distillation:  - White-box, student learns internal states  - Black-box, student mimics output logits → Dynamic Inference, adaptive early exits or skipping blocks based on input complexity 3. System-Level Optimization (a) Inference Engine → Graph & Operator Optimization, use ONNX, TensorRT, BetterTransformer for op fusion → Speculative Decoding, use a smaller model to draft tokens, validate with full model → Memory Management, KV cache reuse, paging strategies (e.g., PagedAttention in vLLM) (b) Serving System → Batching, group requests with similar lengths for throughput gains → Scheduling, token-level preemption (e.g., TGI, vLLM schedulers) → Distributed Systems, use tensor, pipeline, or model parallelism to scale across GPUs My Two Cents 🫰 → Always benchmark end-to-end latency, not just token decode speed → For production, 8-bit or 4-bit quantized models with MQA and PagedAttention give the best price/performance → If using long context (>64k), consider sliding attention plus RAG, not full dense memory → Use speculative decoding and batching for chat applications with high concurrency → LLM inference is a systems problem. Optimizing it requires thinking holistically, from tokens to tensors to threads. Image inspo: A Survey on Efficient Inference for Large Language Models ---- Follow me (Aishwarya Srinivasan) for more AI insights!

As Agentic AI continues to revolutionize our field, the secret lies in adopting a 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗮𝗻𝗱 𝗲𝘅𝘁𝗲𝗻𝗱𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 that scales with your ideas. I'm excited to share a framework to keep your AI projects organized, agile, and ready for rapid innovation. 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: - 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗖𝗼𝗱𝗲 𝗕𝗮𝘀𝗲: Break your project into distinct, manageable modules for data processing, feature engineering, and modeling. This promotes reusability and simplifies testing, so you can quickly adapt to new challenges. - 𝗘𝘅𝘁𝗲𝗻𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Seamlessly add new features, experiments, or data sources. The structure is built to grow with your project, ensuring you’re always prepared for the next big breakthrough. - 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆: Maintain clear folders for Jupyter notebooks, documentation, and version-controlled configuration files, keeping your team in sync and your project transparent. - 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use dedicated configuration files to switch environments or adjust settings effortlessly without disrupting your core code. - 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴: Organize your experiments with dedicated folders that record configurations, results, and models, making it easier to iterate and refine your approach. Embracing this modular and extendable approach is key to unlocking the full potential of Agentic AI, paving the way for innovative solutions and rapid advancements. Curious to learn more? 𝗥𝗲𝗮𝗱 𝗼𝗻 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 about how structured design is powering the next generation of AI breakthroughs.

AI performance is won between the specs. A GPU may advertise enormous compute, but real-world AI speed depends on how effectively the entire system moves data, uses memory, serves tokens, and distributes work across hardware. The hardware layer begins with CUDA cores for parallel computation, Tensor Cores for matrix operations, and HBM for feeding data to the GPU. In many inference workloads, memory bandwidth—not peak compute—becomes the actual constraint. As systems scale, communication matters just as much. ↳ NVLink connects GPUs directly ↳ InfiniBand links GPU servers into high-speed clusters ↳ Data movement determines whether expensive compute stays productive Measurement reveals what specification sheets cannot. FLOPS describe theoretical performance. MFU shows how much of that compute a training run actually uses. Compute-bound versus memory-bound analysis identifies whether the workload is waiting for calculations or data. For inference, users experience two metrics immediately: ↳ Time to First Token ↳ Tokens per Second Precision techniques such as mixed precision, quantization, FP8, and FP4 help models use less memory and achieve higher throughput. Then come the techniques that make inference more efficient: ↳ KV Cache avoids recomputing previous tokens ↳ Continuous batching keeps GPUs productive ↳ Flash Attention reduces slow memory operations ↳ PagedAttention improves KV-cache allocation ↳ Speculative decoding accelerates generation ↳ Disaggregated serving scales prefill and decode separately At larger scale, data, tensor, pipeline, and expert parallelism distribute models and workloads across multiple GPUs. The real lesson is simple: AI compute is not one GPU, one benchmark, or one performance number. It is a complete system of compute, memory, networking, precision, inference, and parallelism. Understanding these 25 terms helps teams make smarter decisions about speed, scalability, infrastructure, and cost.

My Service Design team at LinkedIn has been actively wokring to experiment with and learn about the new AI tools. So far we've learned that: 🔹 We've got to try the tool to know the tool 🔹 We should document everything - prompts used, tools tried, use cases applied, everything 🔹 We should spend more time writing better prompts to get better results 🔹 We've got to set aside time for learning, for experimentation, for reflection It's now been over a month of intensive experimentation and testing of new AI tools for myself and my team. Some people have been experimenting far longer than that. This post is not for you (though if you have words of wisdom to share in the comments, please do!). If you haven't started trying out the new AI Tools, or you're early in your journey, read on. I wanted to share an early version of a framework for thinking about how to get a handle on the emerging AI Tools landscape: 1️⃣ Learning about new tools - prompt the LLMs, listen, read and watch There are so many new tools to potentially test out. Whatever the use case, I always start by checking with Claude about which tools it would recommend and why. But I also want serendipity and so I am listening to and watching podcasts - Dive Club, Lenny's, How I AI, Beyond the Prompt are my go-tos for now - and reading sources like Lenny's Newsletter, and the Listed AI newsletter to learn about new tools and new use cases. I'm sure there are many other great sources. ❓ What are you go-to sources for learning about AI? 2️⃣ Documenting and evaluating everything that we do to help with tool selection Not only do you have to sift through a growing mountain of new tools, but you also have to match tools with use cases. With that in mind, my team and I keep a running list of problems or opportunities we want to test out AI tools on. In addition, I'm documenting the steps in our workflow, what tools we use today, and where we might integrate new tools to save us time, or improve the quality of our work. This makes it easier to match potential tools with use cases and parts of our workflow. 💡 Clearly documenting what you do and where you might apply new AI tools makes it far easier to move past the paradox of choice with all the new tools, and select a subset to try out. 3️⃣ Set aside time to experiment with the tools - learning the tools does require an investment of time. We meet regularly as a team to experiment and test out new tools. The possibilities of productivity improvements, and quality improvements are real enough that it makes sense for us to devote time to this. I believe it will pay off significantly in the long term. ⏲️ You aren't getting more time in your week, and I'm guessing you were already busy, so you have to make a conscious choice to repurpose some existing time for testing out the new tools. ❓How are you approaching the new AI tools? #AI #Vibecoding #LIPostingDayJune

After optimizing costs for many AI systems, I've developed a systematic approach that consistently delivers cost reductions of 60-80%. Here's my playbook, in order of least to most effort: Step 1: Optimizing Inference Throughput Start here for the biggest wins with least effort. Enabling caching (LiteLLM (YC W23), Zilliz) and strategic batch processing can reduce costs by a lot with very little effort. I have seen teams cut costs by half simply by implementing caching and batching requests that don't require real-time results. Step 2: Maximizing Token Efficiency This can give you an additional 50% cost savings. Prompt engineering, automated compression (ScaleDown), and structured outputs can cut token usage without sacrificing quality. Small changes in how you craft prompts can lead to massive savings at scale. Step 3: Model Orchestration Use routers and cascades to send prompts to the cheapest and most effective model for that prompt (OpenRouter, Martian). Why use GPT-4 for simple classification when GPT-3.5 will do? Smart routing ensures you're not overpaying for intelligence you don't need. Step 4: Self-Hosting I only suggest self-hosting for teams at scale because of the complexities involved. This requires more technical investment upfront but pays dividends for high-volume applications. The key is tackling these layers systematically. Most teams jump straight to self-hosting or model switching, but the real savings come from optimizing throughput and token efficiency first. What's your experience with AI cost optimization?

In a recent roundtable with fellow CXOs, a recurring theme emerged: the staggering costs associated with artificial intelligence (AI) implementation. While AI promises transformative benefits, many organizations find themselves grappling with unexpectedly high Total Cost of Ownership (TCO). Businesses are seeking innovative ways to optimize AI spending without compromising performance. Two pain points stood out in our discussion: module customization and production-readiness costs. AI isn't just about implementation; it's about sustainable integration. The real challenge lies in making AI cost-effective throughout its lifecycle. The real value of AI is not in the model, but in the data and infrastructure that supports it. As AI becomes increasingly essential for competitive advantage, how can businesses optimize costs to make it more accessible? Strategies for AI Cost Optimization 1.Efficient Customization - Leverage low-code/no-code platforms can reduce development time - Utilize pre-trained models and transfer learning to cut down on customization needs 2. Streamlined Production Deployment - Implement MLOps practices for faster time-to-market for AI projects - Adopt containerization and orchestration tools to improve resource utilization 3. Cloud Cost Management -Use spot instances and auto-scaling to reduce cloud costs for non-critical workloads. - Leverage reserved instances For predictable, long-term usage. These savings can reach good dollars compared to on-demand pricing. 4.Hardware Optimization - Implement edge computing to reduce data transfer costs - Invest in specialized AI chips that can offer better performance per watt compared to general-purpose processors. 5.Software Efficiency - Right LLMS for all queries rather than single big LLM is being tried by many - Apply model compression techniques such as Pruning and quantization that can reduce model size without significant accuracy loss. - Adopt efficient training algorithms Techniques like mixed precision training to speed up the process -By streamlining repetitive tasks, organizations can reallocate resources to more strategic initiatives 6.Data Optimization - Focus on data quality since it can reduce training iterations - Utilize synthetic data to supplement expensive real-world data, potentially cutting data acquisition costs. In conclusion, embracing AI-driven strategies for cost optimization is not just a trend; it is a necessity for organizations looking to thrive in today's competitive landscape. By leveraging AI, businesses can not only optimize their costs but also enhance their operational efficiency, paving the way for sustainable growth. What other AI cost optimization strategies have you found effective? Share your insights below! #MachineLearning #DataScience #CostEfficiency #Business #Technology #Innovation #ganitinc #AIOptimization #CostEfficiency #EnterpriseAI #TechInnovation #AITCO

Supervised fine-tuning teaches AI new skills. Reinforcement learning fine-tuning makes it better at applying them. But which one should you use? Both methods improve models, but they work very differently. Let’s break it down: 1️⃣ Supervised Fine-Tuning (SFT) → Learning New Capabilities • How it works: Train a pre-trained model on a labeled dataset (input → expected output). • Goal: Teach the model something it didn’t previously know. • Data Needed: Lots of high-quality, labeled examples (hundreds to thousands). 🛠 Example: Want GPT to translate Python to a proprietary language? Fine-tune it with thousands of Python-language X function pairs. Pros: ✅ Efficient/powerful if you have data. ✅ Predictable behavior—model learns to imitate examples. Cons: ❌ Needs extensive labeled data. ❌ Struggles outside its training distribution. ❌ Can be complicated to get it right and needs experimenting. 2️⃣ Reinforcement Learning Fine-Tuning (RL Fine-Tuning) → Refining Behavior • How it works: The model generates outputs, which are scored via a reward function. The training process optimizes for higher rewards rather than exact matches. • Goal: Improve a model’s decision-making, style, or "adherence to human preferences". • Data Needed: A way to measure success (human feedback, programmatic rewards, or policy optimization). 🛠 Example: Your AI already knows Python & Rust but sometimes produces inefficient lengthy translations. RL fine-tuning rewards better translations, improving quality without needing thousands of new labeled examples. Pros: ✅ Cheaper: Requires fewer direct labels (but needs a good reward function). ✅ Can optimize for things like engagement, correctness, or efficiency. Cons: ❌ Computationally expensive—training involves exploration. ❌ Risk of unintended behaviors (model exploits reward hacks). Real-World Example: OpenAI’s ChatGPT 1️⃣ First, SFT trained the model on instruction-following data (telling it what “good” answers look like). Teaching the model to "follow instructions" 2️⃣ Then, RLHF (a form of RL fine-tuning) helped it align with human preferences, making responses more natural, useful, and safe. Key Takeaway: • SFT = Teach new knowledge. (Needs lots of labeled data.) • RL Fine-Tuning = Optimize decisions. (Needs a good way to score outputs.) 🚀 Best Practice: Use SFT for knowledge, RL for alignment & improvement. p.s. With advancements from providers like OpenAI, implementing these fine-tuning methods is becoming more accessible and cost-effective, reducing previous barriers related to cost and experimentation. So keep an eye on those!

Don’t let your AI project die in a notebook. You don’t need more features. You need structure. This is the folder setup that actually ships from day one. 📁 𝗧𝗵𝗲 𝗳𝗼𝗹𝗱𝗲𝗿 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 Forget monolithic scripts. You need this: /config 🔹YAML files for models, prompts, logs 🔹Config lives outside the code, always /src 🔹Modular logic: llm/, utils/, handlers/ 🔹Clean, testable, scalable from day one /data 🔹Cached outputs, embeddings, prompt responses 🔹Cut latency + save on API costs instantly /notebooks 🔹For testing, analysis, and iteration 🔹Never pollute your main codebase again 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘀𝗼𝗹𝘃𝗲𝘀 ▪️Prompt versioning is built in ▪️Rate limiting and caching come standard ▪️Error handling is modular ▪️Experiments stay reproducible ▪️Deployment is one Dockerfile away 𝗕𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗯𝗮𝗸𝗲𝗱 𝗶𝗻 1. Prompts are versioned by default ▪️Stored in prompt_templates.yaml + templates⋅py ▪️Track, test, roll back 2. Rate limiting is pre-integrated ▪️rate_limiter⋅py stops API overloads and surprise bills 3. Caching is plug-and-play ▪️Duplicate calls get stored in /data/cache ▪️Cut costs by 70% on day one 4. Each module does one thing only ▪️Models in llm/, logs in utils/, errors in handlers/ ▪️No sprawl 5. Notebooks are safely isolated ▪️Run tests and explorations in prompt_testing.ipynb ▪️Nothing leaks into production logic ⚙️ Clone the github template below - in first comment This structure ships faster, costs less and scales without rewrites. ------------ ⚡I’m Nina. I build with AI and share how it’s done weekly. #aiagents #softwaredevelopment #MCP #genai #promptengineering

I made this mistake in Data Science for 2 years... Maybe you're making it too 👀 I thought becoming a Data Scientist was all about building models. So I focused on: ✓ Machine Learning Algorithms ✓ Model Training ✓ Hyperparameter Tuning ✓ Accuracy Scores And ignored everything else. My models worked. But my projects were impossible to scale, maintain, or hand over to another team member. Files were scattered everywhere. Data pipelines were mixed with notebooks. Models, reports, and code lived in random folders. Then I realized something: The difference between a beginner and a professional Data Scientist isn't just building models. It's project organization and reproducibility. A well-structured project saves hundreds of hours when projects grow. Here's the Data Science project structure I wish someone had shown me earlier 👇 Phase 1: Data Organization 📂 data/ ├── raw ├── processed ├── external Keep your raw data untouched. Always create processed versions for modeling. This makes experiments reproducible. Phase 2: Exploration & Experimentation 📂 notebooks/ ✓ EDA ✓ Feature Engineering ✓ Modeling ✓ Evaluation Separate notebooks by purpose. Don't keep everything in one giant notebook 😅 Phase 3: Source Code 📂 src/ ├── data ├── features ├── models ├── visualization ├── utils Move reusable logic from notebooks into Python modules. This is where projects become production-ready. Phase 4: Model Management 📂 models/ ├── saved_models ├── artifacts ├── logs Store: ✓ Trained Models ✓ Feature Encoders ✓ Experiment Artifacts ✓ Training Logs Never lose your best model again. Phase 5: Reporting 📂 reports/ ├── figures ├── reports.html ├── summary.md A project isn't finished until stakeholders understand the results. Visualizations and reports matter as much as the model. Phase 6: Configuration & Testing 📂 config/ ├── config.yaml 📂 tests/ ├── test_data.py ├── test_model.py Learn: ✓ Configuration Management ✓ Unit Testing ✓ Integration Testing ✓ Validation Pipelines This is where Data Science becomes Engineering. Phase 7: Industry Ready Projects Add: ✓ Docker ✓ Git ✓ CI/CD ✓ Experiment Tracking ✓ MLflow ✓ FastAPI ✓ Monitoring ✓ Model Deployment Most portfolio projects stop before this stage. That's why the gap between a project and a production system is huge. 💡 Resources I'd Recommend: • Python Documentation • Pandas Documentation • scikit-learn-Learn Documentation • Kaggle Learn • StatQuest • FastAI • w3schools.com • MLflow Docs • GeeksforGeeks The lesson that took me the longest to learn: A messy project structure doesn't hurt on Day 1. It destroys productivity on Day 100. Save this post for your next Data Science project 📌 🔁 Repost it to help someone build better projects. What project organization mistake took you the longest to realize? 👇 #DataScience #MachineLearning #MLOps #AI #Python #DataAnalytics #DataScientist #MLEngineer #ProjectStructure #SoftwareEngineering #TechCareer