Everseen

✅ Actively involved in all stages of ML project lifecycle.

𝐄𝐯𝐞𝐫𝐬𝐞𝐞𝐧 is an AI company that empowers end-to-end visibility across the entire supply chain for the biggest retail clients in the world, aiming to  reduce shrink and increase perpetual inventory accuracy using it’s VisualAI platform.

☑ Worked on a theft-prevention system that was showcased at 𝐍𝐞𝐰𝐘𝐨𝐫𝐤 𝐍𝐑𝐅2023, and at numerous demo-sessions that 𝐚𝐭𝐭𝐫𝐚𝐜𝐭𝐞𝐝 3 𝐧𝐞𝐰 𝐜𝐥𝐢𝐞𝐧𝐭𝐬 into wanting the product for their stores. 

☑ 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 project/tooling 𝐫𝐚𝐦𝐩-𝐮𝐩 phase for new members within the team, successfully reducing the period from 1 𝐦𝐨𝐧𝐭𝐡 𝐭𝐨 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 1 𝐰𝐞𝐞𝐤.

☑ Handled 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 engineering and 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 to 5 𝐩𝐢𝐥𝐨𝐭 𝐬𝐭𝐨𝐫𝐞𝐬.

☑ Improved the 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐬𝐩𝐞𝐞𝐝 within the team by 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐌𝐋𝐎𝐩𝐬 practices such as Structured Model Experimenting, Resources Monitoring, Model Registry, CI/CD and Jira Integrations.

☑ 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐞𝐝 and established a 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 which enabled us to 𝐫𝐞𝐚𝐜𝐡 > 70% 𝐜𝐨𝐯𝐞𝐫𝐚𝐠𝐞 both on code,tools documentation and custom models tailored for various clients.

☑ Worked on a live camera feed processing pipeline that ran 3 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐮𝐥𝐞𝐬 while achieving 24-25 FPS on a 4K video stream with high QPS rate.

☑ Implemented a 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐞𝐝𝐠𝐞 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 that saved time with hardware requirements and optimisations using Prometheus, Grafana, Kibana and ES.

☑ Actively 𝐩𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐞 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 of the current project, providing support to QA, Development and Deployment processes.

☑ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 the multi-gpu 𝐦𝐨𝐝𝐞𝐥 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 and integration with high-throughput Model Serving technologies like Nvidia Triton Server.

Senior Machine Learning Engineer

Neural Bits

✅ Writing the Neural Bits Newsletter to help AI/ML Engineers upskill in their career.
☑ Making AI accessible to a larger general audience by explaining concepts in simple terms.
☑ Sharing insights and practical advice on AI/ML Systems.

Author

Decoding ML

Sharing insights and actionable advice towards building production-ready ML systems.

Co-Founder

HandsOnLLMs

✅ Contributed to a free hands-on course on end-to-end LLMops.

Worked alongside 𝐏𝐚𝐮 𝐋𝐚𝐛𝐚𝐫𝐭𝐚 𝐁𝐚𝐣𝐨 𝐚𝐧𝐝 𝐏𝐚𝐮𝐥 𝐈𝐮𝐬𝐳𝐭𝐢𝐧 into developing this end-to-end course to help students 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐋𝐋𝐌 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 while following the 𝐛𝐞𝐬𝐭 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 both in software development, model deployment and MLOps integrations.

☑ Implemented the EDA stage on LLM fine-tuning dataset.
☑ Implemented the LangChain logic and modules that connected the QDrant vector database to inference pipeline and model registry.
☑ Worked on the training/fine-tuning pipeline and prompt monitoring using CometML.
☑ Provided support and approved Pull Requests.
☑ Implemented and documented best practices on Prompt Templates and LLM workflow.

Check the course here: http://bit.ly/3Rxjyvp

Co Author

IVFuture

✅ Worked on on a suite of tools including Generative AI, LLM’s and Vision Models.

𝐈𝐕𝐅𝐮𝐭𝐮𝐫𝐞’𝐬 mission is to transform common businesses, communities and people’s needs into intelligent and innovative software solutions, to simplify life through scalable technology.

☑ Implemented a 𝐝𝐞𝐦𝐨 for virtual try-on fashion tool called 𝐓𝐀𝐈𝐒, which was presented to investors and attracted interest from various fashion providers across Romania.

☑ Implemented a CV solution, that was able to accurately showcase over 25 𝐛𝐨𝐝𝐲 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 3𝐃 using 2D cameras, with an 𝐞𝐫𝐫𝐨𝐫-𝐫𝐚𝐭𝐞 𝐨𝐟 5-10% while using cheap hardware, <700$ 𝐩𝐞𝐫 𝐬𝐞𝐭𝐮𝐩, considering industry available setups 𝐞𝐱𝐜𝐞𝐞𝐝 10.000$.

☑ Developed and integrated an 𝐋𝐋𝐌 13𝐁 𝐜𝐡𝐚𝐭 𝐚𝐠𝐞𝐧𝐭, fine-tuned on specific descriptions that was used by the team to automatically 𝐥𝐚𝐛𝐞𝐥 𝐡𝐮𝐧𝐝𝐫𝐞𝐝𝐬 𝐨𝐟 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬.

☑ Fine-tuned 𝐒𝐭𝐚𝐛𝐥𝐞 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧1.5 and tailored it to specific product generation use-case.

☑ Worked on a solution for 𝐓𝐓𝐒 𝐚𝐧𝐝 𝐒𝐓𝐓 and exposed it as an API, which was able to accurately 𝐜𝐥𝐨𝐧𝐞 𝐯𝐨𝐢𝐜𝐞𝐬 𝐟𝐫𝐨𝐦 𝐚 𝐬𝐡𝐨𝐫𝐭 5𝐬𝐞𝐜 𝐚𝐮𝐝𝐢𝐨 𝐜𝐥𝐢𝐩 and synthesize text-to-speech and speech-to-text.

☑ Worked on a 𝐑𝐀𝐆 𝐚𝐠𝐞𝐧𝐭, that was able to ingest document context and provide assistant-like answers on user queries.

Machine Learning Consultant - Generative AI

Odeen Software

✅ Worked on Flowstate, a Computer Vision solution developed for 𝐔𝐑𝐁𝐍𝐒𝐔𝐑𝐅 𝐌𝐞𝐥𝐛𝐨𝐮𝐫𝐧𝐞, 𝐀𝐮𝐬𝐭𝐫𝐚𝐥𝐢𝐚, that generates 𝐬𝐨𝐜𝐢𝐚𝐥-𝐦𝐞𝐝𝐢𝐚 𝐫𝐞𝐞𝐥𝐬 𝐚𝐧𝐝 𝐬𝐡𝐨𝐫𝐭𝐬 for park attendees.

𝐎𝐝𝐞𝐞𝐧 is a company that provides IT services, staff augmentation and SASS solutions on various technology stacks, including Machine Learning, Web Development and more.

☑ Implemented a 𝐦𝐮𝐥𝐭𝐢-𝐜𝐚𝐦𝐞𝐫𝐚 𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠 feature using SOTA Centroid ReID to track and re-identify person detections in multiple camera views.

☑ Integrated 𝐅𝐀𝐈𝐒𝐒 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 to store track embeddings and perform ReID searching for similar tracks in a batch fashion.

☑ Used 𝐃𝐞𝐞𝐩𝐅𝐚𝐜𝐞 to detect and group track detections with face detections, and human pose prediction to filter accurate poses to consider when generating reels.

☑ Worked on the video streaming workflow, offline batch tracking and edge integration with Viso.AI.

Machine Learning Consultant

Upwork

💎 Top Rated • 💯 Job Success Rate • ⭐⭐⭐⭐⭐ Reviews

✅ Developed plug-and-play ML/CV modules.

☑ Client had over 50𝐤 𝐫𝐞𝐜𝐞𝐢𝐩𝐭𝐬 from various stores, and needed to automate the process of analyzing these receipts. I’ve implemented a 𝐌𝐋 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐬𝐚𝐯𝐢𝐧𝐠 𝐮𝐩 𝐭𝐨 80% of the time used to detect, decode and organise these receipts by their QR code metadata.

☑ Client wanted a 𝐄𝐝𝐮𝐓𝐞𝐜𝐡 𝐰𝐞𝐛 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 that allows parents to create quizz tests for their children based on an uploaded document (.pdf, .docx). I’ve implemented a full-stack web application with included ML components which loaded a document, applied ETL, generated quizz test and construct the UI view.

☑ Client wanted a CV plugin that would detect various icons from a phone screen, classify them and automatically create app testing workflows. Using template matching, pyramid-detection and super-resolution GAN’s, I’ve managed to 𝐨𝐩𝐭𝐢𝐦𝐢𝐬𝐞 𝐚𝐧𝐝 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐜𝐥𝐨𝐬𝐞 𝐭𝐨 100% on a few use-cases, and around 30-40% 𝐨𝐧 𝐭𝐡𝐞 𝐫𝐞𝐬𝐭 of the use-cases required.

☑ 𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐞𝐝 on various Computer Vision topics and project ideas.

Top Rated Machine Learning Engineer

✅ Developed and deployed CV/ML modules into existing API's.

☑ Client wanted a CV solution that 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 𝐬𝐩𝐨𝐫𝐭𝐬 𝐠𝐚𝐦𝐞𝐬 𝐚𝐧𝐝 𝐬𝐞𝐧𝐝𝐬 𝐧𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐡𝐞𝐧𝐞𝐯𝐞𝐫 𝐭𝐡𝐞 𝐬𝐜𝐨𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐬. whenever the score changes. I’ve applied smart-template matching, targeted motion detection and OCR to extract the team names, scores and send a push message whenever the score changed.

☑ Client wanted a CV solution that scans documents for hand signatures, identifies faulty ones and groups them in a specific structure. Solution was based on deep learning and OCR post-processing, deployed as a docker ML service which helped the client to 𝐬𝐩𝐞𝐞𝐝-𝐮𝐩 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐛𝐲 𝐚 3𝐱 𝐟𝐚𝐜𝐭𝐨𝐫, relying far less on Humans-in-the-Loop activity.

☑ Client wanted a project concept report on how to apply custom texture from an image onto a 3D model. I’ve researched and planned a workflow that included Object Detection, Instance Segmentation and UV mapping.

Machine Learning Engineer

Elektrobit

✅ Developed multiple Computer Vision solutions and deployed them on edge devices.

𝐄𝐥𝐞𝐤𝐭𝐫𝐨𝐛𝐢𝐭 is key supplier of embedded software solutions for the automotive industry.

☑ Worked on a 𝐣𝐨𝐢𝐧𝐭-𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐰𝐢𝐭𝐡 𝐁𝐌𝐖 𝐌𝐮𝐧𝐢𝐜𝐡 𝐟𝐨𝐫 𝐄𝐏𝐈 (European Processor Initiative 2020) and deployed 𝐚 𝐜𝐚𝐫 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐩𝐚𝐭𝐡-𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 on an Nvidia Jetson AGX board, placed in a real 𝐁𝐌𝐖 𝐱5 𝐭𝐞𝐬𝐭 𝐜𝐚𝐫, which was able to detect and map the environment around the test vehicle using video feed from 2xIntelCameras and 1xLidarSensor.

☑ 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥𝐥𝐲 𝐩𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐚 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐩𝐚𝐩𝐞𝐫 titled GFPNet, under the supervision of the Head of AI @Elektrobit Romania, at the 𝐈𝐄𝐄𝐄 𝐑𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐞𝐭𝐭𝐞𝐫𝐬 2020, within the Machine Learning and Robotics field that studied the shape reconstruction of occluded 3D objects.

☑ Generated synthetic data using 𝐂𝐚𝐫𝐥𝐚𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐨𝐫 (𝐔𝐄4) to train deep neural networks for specific autonomous driving scenarios, improving the accuracy of certain prediction.

☑ Worked on development and optimisation of a vehicle direction and position classifier neural network, integrated directly on the Renesas 𝐑-𝐂𝐚𝐫-𝐇3𝐄 𝐜𝐡𝐢𝐩 (𝐒𝐨𝐂), further deployed to one of the clients test car.

☑ Worked on a 𝐦𝐨𝐝𝐞𝐥 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 𝐚𝐧𝐝 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 for edge devices, that allowed a model to be ported in Python/C++ with no further intervention, 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐭𝐡𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐛𝐲 𝐨𝐯𝐞𝐫 50%.

✅ Researched computer vision topics, mainly targeted towards autonomous driving scenarios.

☑ Researched 𝐇𝐲𝐝𝐫𝐚𝐍𝐞𝐭𝐬 for 𝐚𝐮𝐭𝐨𝐦𝐨𝐭𝐢𝐯𝐞 𝐯𝐢𝐬𝐢𝐨𝐧 𝐭𝐚𝐬𝐤𝐬, using ResNet (50, 100) encoder backbones and multiple prediction heads (classification, 3D bounding-box, object detection and orientation), the research 𝐫𝐞𝐩𝐨𝐫𝐭 𝐛𝐞𝐢𝐧𝐠 𝐟𝐮𝐫𝐭𝐡𝐞𝐫 𝐮𝐬𝐞𝐝 to develop, train and deploy a model on a small 𝐟𝐨𝐮𝐫-𝐰𝐡𝐞𝐞𝐥𝐞𝐝 𝐫𝐨𝐛𝐨𝐭.

☑ Researched 𝐏𝐨𝐢𝐧𝐭𝐂𝐥𝐨𝐮𝐝 𝐬𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐨𝐦 𝐃𝐞𝐩𝐭𝐡 using a X-Box KinectSensor, feature that was further tested in a stack of models 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐨𝐧 𝐚 𝐟𝐥𝐲𝐢𝐧𝐠 𝐝𝐫𝐨𝐧𝐞 that followed QR markers on the floor.

☑ Worked on a model-development dashboard that 𝐚𝐥𝐥𝐨𝐰𝐞𝐝 𝐮𝐬𝐞𝐫𝐬 𝐭𝐨 𝐮𝐩𝐥𝐨𝐚𝐝 𝐝𝐚𝐭𝐚, 𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥, 𝐬𝐜𝐡𝐞𝐝𝐮𝐥𝐞 𝐚 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐣𝐨𝐛, inspect metrics and download artifacts on specific targets (.pt, .onnx, .trt, .gzip).

Junior Machine Learning Engineer

Universitatea Transilvania din Brașov

✅ Teaching 𝐢𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 programming concepts and intro to 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠.

☑ Java OOP, Python, Git
☑ Data Structures and Algorithms, Graphs
☑ Artificial Neural Networks
☑ Supervised and unsupervised learning
☑ House Price Prediction
☑ Activation Functions
☑ Hand Digits Recognition using Computer Vision and Deep Learning

Teaching Assistant

Siemens

✅ Platform to manage/schedule an industrial process workflow.

𝐒𝐢𝐞𝐦𝐞𝐧𝐬 𝐂𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 is a strategic partner to Siemens operative units. It provides important services along the entire value chain – from research and development to production and quality assurance, as well as optimized business processes.

☑ 𝐏𝐥𝐚𝐧𝐧𝐞𝐝 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 for an app that would allow users to inspect and manage stock for a potato-chips factory.

☑ Implemented the 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐥𝐚𝐲𝐞𝐫 for admin, observer, user with different functionalities for each category.

☑ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥-𝐬𝐭𝐚𝐜𝐤 𝐥𝐨𝐠𝐢𝐜, communications between components and frontend dashboard.

☑ 𝐖𝐨𝐧 3’rd place amongst over 10 projects presented.

Student Internship

Hard work always pays off, be consistent!

I'm a Senior Machine Learning Engineer with a keen interest in developing AI/ML solutions for real world problems and helping others get started on their Machine Learning Journey.

Let's connect @:
📘 https://medium.com/@alexandrurazvant
✉️ alexandrurazvant@gmail.com
🌐 https://www.neuraleaps.com

Alex Razvant

Want to learn about Agents, MCP, Multimodal AI, and LLMOps?

Miguel Otero and I have released an open-source course where we build an Multimodal Agent that can understand videos, step by step.

The project idea is simple: 𝐘𝐨𝐮 𝐮𝐩𝐥𝐨𝐚𝐝 𝐚 𝐯𝐢𝐝𝐞𝐨, 𝐚𝐧𝐝 𝐭𝐡𝐞𝐧 𝐜𝐡𝐚𝐭 𝐰𝐢𝐭𝐡 𝐢𝐭..

However, there are many complex challenges, underlying this simple idea.

Some of them are:
- How to build and debug MCP Servers in Python
- Understanding Multimodal Ingestion (video, audio, text, images)
- Designing efficient FastAPI APIs, Background Tasks
- Tool Calling / Router Agents from scratch, in Python
- Understanding Vision Language Models (VQA), Audio Transcription Models (STT)
- How to connect APIs (FastAPI) with UIs (React JS)

Throughout the course,  we explain each module with an general audience pace in mind.

We aimed for it to be easy to follow, easy to understand.

However, we still kept it technical enough.

You don't have to register or be an AI Expert to take this course. It's free.

The course lessons split:
1/ Course Overview
🔗 https://lnkd.in/dm73KT_i

2/ Understand Multimodal Pipelines
🔗  https://lnkd.in/dmn_RBfm

3/ Advanced FastAPI and Pydantic (extra)
🔗  https://lnkd.in/drpKtPFD

4/ Understand the MCP Protocol (extra)
🔗  https://lnkd.in/dJzNp8mH

5/ Build an MCP Server for Multimodal Ingestion
🔗  https://lnkd.in/d33Rsm4e

6/ Building a Tool Calling Agent from Scratch
🔗  https://lnkd.in/dDz6-zZU

7/ Building the full-stack (API, Agent, UI) Application
🔗 https://lnkd.in/dJezq4ry 

8/ Kubrick: The Multimodal Agent Round-up
🔗 https://lnkd.in/drZWiBdT 

Find the GitHub Repository here (give us a ⭐ ):
🔗 https://lnkd.in/dudc7BZS


Tech Stack:
- FastAPI, React JS, FastMCP, MCP Inspector, Docker Compose
- LLMOps with Opik (by @Comet)
- Multimodal Processing with @Pixeltable
- LLMs, VLMs and STT (Speech To Text) AI Models.

What you'll learn as a result:
- Build an MCP Server
- Build an API in Python with FastAPI
- Add LLM/Agent observability
- Process video/audio multimodal data.
- Connect multiple components into an AI System

Have fun learning & building!
✌️

Senior AI Engineer | Author @ NeuralBits | Helping engineers understand, build, and master AI.

CNNs remember where. ViTs remember what  

In a majority of Computer Vision models, the feature extractors (i.e, backbone networks) were built either as CNN encoders, ResNets, or Pyramid Networks.

Regardless of how large the Backbone is, CNNs are not that good at learning full-image context, due to how Receptive Fields work.

```
In deep learning, a receptive field is the specific area of the input data that influences the calculation of a single output feature or neuron in a CNN layer. 
```

1/ CNNs have inductive biases baked into their design
2/ Local receptive fields, hard to scale to High-Res images.
3/ Hierarchical feature extraction
4/ This can be improved, but at the cost of the model's depth

In the top section of the attached image, the `blue-square trace represents a receptive field from High-Level features onto Low-Level features`.

The red ❌  signs represent the feature regions that are not covered by the Receptive Field.

Thus, with CNNs, a larger image context is left out.

---

ViTs, on the other hand, see an image as a sequence of patches and mix them globally through self-attention. 

When feeding an image to a transformer, we first split it into patches of NxN pixels.
Each patch receives a positional encoding vector.
The resulting embedding vector is passed through the Transformer layers.

That makes Transformers:
1/ Permutation-invariant, positional embeddings are required.
2/ Focus on learning global + local level features 
3/ Adversarial Robustness (due to global-context, can spot tinkered features)
3/ Attention Mechanism over patches is computed in Many-to-Many order.
4/ Better for tasks that require image-wide context.

To summarize:
CNNs are limited in capturing global context due to their Receptive Fields.
ViTs compute relationships with and between each patch in the image.

CNNs are better for locality-focused tasks, e.g, Object Detection.
ViTs are better for context-focused tasks, e.,g VQA.

♻️ Reshare and let others learn this too.
Follow me to learn more about AI Engineering and production-ready AI Systems!

"What should I know about NVIDIA AI Stack?"

Here's my answer, tailored for those starting out in AI regarding NVIDIA's ecosystem.

𝗧𝗵𝗲 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
NVIDIA doesn't just build the GPUs that power a large chunk of AI Infrastructure.

They've built a full end-to-end ecosystem.
> 𝙃𝙖𝙧𝙙𝙬𝙖𝙧𝙚: All the architectures and iterations (Turing, Ada, Hopper, Blackwell, etc.)
> 𝙎𝙤𝙛𝙩𝙬𝙖𝙧𝙚: CUDA, cuDNN, TensorRT, Triton, NeMo, Dynamo, and more.

This stack powers everything from training LLMs to deploying models at the edge, across various fields (Robotics, Vision, Language, Speech, Agentic and Embodied AI).

But for a beginner, these are too many, each with its place in the end-to-end AI workflow.
It's easy to get lost.

So, here's my advice, split into 3 categories:
1/ General Important Details
2/ Engineering Advice
3/ Software Stack

𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗲𝘁𝗮𝗶𝗹𝘀
First, I recommend that you learn at a very high level the GPU components.

This will get you started in understanding the hardware black box and differentiating between various GPU/DSA architectures.

Learn about:
1. GPU Cores
What are they? What's the difference between a CUDA Core and a Tensor Core?

2. Memory Hierarchy
Differentiate between VRAM, SRAM, and Registers. This will open doors to understanding FlashAttention, which is a key optimization in LLMs.

3. Precision & Data Types
The majority of LLMs are trained in FP32 or Mixed (FP32/FP16). For Inference, FP16/BF16,  FP4. This will also help you understand the quantization data types.

𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗔𝗱𝘃𝗶𝗰𝗲
These will help you understand how to work more efficiently with NVIDIA's hardware and software stack.

1. Learn to read a GPU Cheatsheet (details about a GPU's performance)
2. Learn to use the Framework Compatibility Matrix (mandatory for SWEs, AIEs)
3. Learn to use NVIDIA's pre-built containers (built in robust, isolated environments)

𝗧𝗵𝗲 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗦𝘁𝗮𝗰𝗸
We have a lot on this layer, but for starters, just learn what each of these does:

1. 𝘊𝘰𝘳𝘦 > CUDA & cuDNN, the backbone for deep learning, both training and inference.
2. 𝘋𝘢𝘵𝘢 𝘗𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 > DeepStream for real-time video pipelines, cuDF for Dataframes on GPU.
3. 𝘐𝘯𝘧𝘦𝘳𝘦𝘯𝘤𝘦 > TensorRT/LLM for optimizing models, Triton, and Dynamo for deployment.
4. 𝘎𝘦𝘯𝘈𝘐 > Nemotron Models, NeMo Framework to create, customize, and deploy GenAI models.
5. 𝘌𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 > NIM for batteries-included GenAI deployments. Riva for Speech.
6. 𝘌𝘥𝘨𝘦 𝘈𝘐 > Jetson Xavier, DGX Spark, Orin platforms.
7. 𝘚𝘤𝘢𝘭𝘪𝘯𝘨 > K8S GPU Operator and KAI Scheduler for GPU resource management at scale.

---
There are a few more, but this is a solid start.

In the end, if you spend a few hours reading through these, you'll get a good overview of what's out there.

♻️ Share this post, and research it later.

Ashish Patel 🇮🇳

Sr.Data Scientist & AI Consultant

Oracle

▪️ Led the AI strategy, architecture, and design for Artificial Intelligence, Machine Learning, and Data Science products, services, and solutions, aligning them with Oracle Cloud Applications, OCI, and Oracle Enterprise.
▪️ Provided thought leadership and technology oversight for AIML Development teams, directly influencing business and technology decisions.
▪️ Spearheaded the development and deployment of AI Agents for scalable, resilient enterprise applications, driving AI-powered automation across business functions.
▪️ Architected and implemented MCP server enterprise architecture, ensuring seamless integration with cloud-native environments and maximizing the scalability and performance of enterprise applications.
▪️ Collaborated with senior executives across multiple business lines to translate business requirements into technology solutions, aligning them with the overall AI strategy and next-gen tech stack.
▪️ Directed the development of machine learning models, integrating them with full-stack software ecosystems, and contributed to the strategic design of data engineering.
▪️ Worked with product managers and development teams to define project scopes and software requirements, ensuring the timely delivery of projects meeting quality standards.
▪️ Authored and maintained technical documentation, including architecture diagrams, design specifications, and system diagrams.
▪️ Led the adoption of cutting-edge technologies and frameworks (e.g., PyTorch, TensorFlow, Scikit-learn), driving innovation and best practices within the team.
▪️ Mentored junior developers, fostering skill development and encouraging a culture of creativity and ownership within the AIML domain.
▪️ Stayed at the forefront of industry trends, ensuring the team remained competitive by recommending new technologies and platforms.

Senior Principal Architect - AIML & Data Science

▪️Design and/or delivery of solutions, assets, and content at sustainable AI.
▪️Apply methodology and governance around project development.
▪️Document and articulate clear understanding of customer/partner success criteria.
▪️Apply critical thinking, technical knowledge, customer/partner insights to solve problems with industry methodologies, and frameworks.
▪️Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology.
▪️Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimization, and reliability.
▪️Define, lead, and execute architecture design workshops and white-boarding sessions to build implementation plans.
▪️Develop deep relationships with key customers through solution integration to drive long-term IBM and AWS adoption.
▪️Team well with our key IBM & Red Hat business units, and strategic partners to support our partnership.
▪️Consult as a subject matter expert on our IBM/AWS technology and services portfolio.
▪️Author use cases, reference architectures, blog posts, and other field and external content.
▪️Work in sandbox environments to demonstrate IBM Hybrid Cloud Capabilities with AWS.
▪️Led the design and automation of tasks in Oracle HCM using Generative AI and LLMs, streamlining HR processes and enhancing efficiency.
▪️Mentored junior and senior scientists, guiding their development and ensuring successful project execution.
▪️Developed production code and championed best coding and engineering practices in AI and machine learning projects.
▪️Identified and mitigated risks in AI project execution, ensuring successful business and technical outcomes.
▪️Collaborated with product managers and technical leaders to ensure timely and successful delivery of AI models and services.

Sr. AWS AI/ML Solution Architect

Cygnet Infotech

• Interact with customer and come up with use-case’s on ML/AI and use the same in
solution integration on existing business. 
• Selecting features, building, and optimizing classifiers using machine learning techniques.
• Develop and test Computer vision algorithms to edge devices and advanced hardware systems created.
• Develop and evaluate state-of-the-art computer vision algorithms for real-time edge devices
and other hardware devices.
• Perform technical performance benchmarking and analyses to support engineering decisions
• Data mining using state-of-the-art methods 
• Extending company’s data with third party sources of information when needed 
• Enhancing data collection procedures to include information that is relevant for building
analytic systems 
• Processing, cleansing, and verifying the integrity of data used for analysis.
• Participate in design reviews. 
• Doing ad-hoc analysis and presenting results in a clear manner 
• Creating automated anomaly detection systems and constant tracking of its performance 
• Research and devise innovative statistical models for data analysis 
• Work as the lead data strategist, identifying and integrating new datasets that can be 
leveraged through our product capabilities and work closely with the engineering team to
strategize and execute the development of data products. 
• Collaborate with product design and engineering to develop an understanding of needs
and Quality. 
• Analyse data for trends and patterns and Interpret data with a clear objective in mind. 
• Provide deep learning and machine learning base solutions to Solve varies Real world
problem.

Projects : Semantic Segmentation, Image classification,  3d Sementic Segmentation, Few-shot Image Classification, Object detection, Image Quality Assessment, Pose Estimation, Key-point Detection,  Image Augmentation, Super Resolution

Senior Data Scientist

SYMBIOSIS INTERNATIONAL UNIVERSITY

- Teaching Computer vision to M.tech Students.

Topics I teach: 

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗜𝗺𝗮𝗴𝗶𝗻𝗴
- Image Acquisition, Sampling, Quantization, Difference in Monochrome and Multichrome imaging, the concept of color spaces, point processing techniques, mask processing methods, image filtering, shape in images, edge detection, gradient operators- Roberts, Sobel, Prewitt, Canny, Slope magnitude method, morphological image processing, erosion, dilation, opening, closing, hit-n-miss transform, thinning, Top Hat transformation, Bottom hat transformation

𝗜𝗺𝗮𝗴𝗲 𝗥𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗥𝗲𝗴𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
 - Shape Descriptors-contour based, region-based, Boundary based; Thresholding based segmentation, Watershed based Segmentation, Gray level Co-occurrence Matrix-energy, entropy, maximum probability, contrast, correlation; wavelets, wavelet Pyramids, Image matching, 
- Hough Transform, RANSAC, Large-scale retrieval: Spatial Verification, TF-IDF, Query Expansion, feature encoding, Crowdsourcing, and Human Computation, Corners - Harris and Hessian Affine, Orientation Histogram, SIFT, SURF, HOG, GLOH, Scale-Space Analysis- Image Pyramids and Gaussian derivative filters, Gabor Filters and DWT.

Industry Associate Professor

Packt

TensorFlow Developer Certificate Guide : 

https://www.amazon.in/TensorFlow-Developer-Certificate-Comprehensive-Guide-ebook/dp/B0BVMQ1TZS

Technical Book Reviewer

Apress

Book Author

Manning Publications Co.

Prompt Softech

•	Collaborated with business teams to gather and analyze requirements, ensuring 100% alignment with organizational objectives and delivering solutions that met strategic goals.
•	Enhanced training datasets using advanced image augmentation techniques, improving model robustness and achieving a 15% increase in classification accuracy.
•	Leveraged Transfer Learning methodologies, repurposing pre-trained models to deliver 25% faster development cycles while achieving notable accuracy improvements across multiple tasks.
•	Implemented state-of-the-art deep learning models for image classification, extracting meaningful insights from complex datasets and enabling 98% accuracy in production systems.
•	Validated and fine-tuned models on extensive datasets containing thousands of images, ensuring 99% reliability and seamless deployment in real-world applications.

Softweb Solutions Inc.

•	Data mining using state-of-the-art methods. 
•	Hands-on experience in selecting features, building, and optimizing classifiers using machine-learning techniques. 
•	Enhancing data collection procedures to include information that is relevant for building analytic systems 
•	Processing, cleansing, and verifying the integrity of data used for analysis.
•	Doing ad-hoc analysis and clearly presenting results.
•	Creating anomaly detection systems and constant tracking of its performance.
•	Having strong knowledge in Statistics, Feature Engineering, Model Selection, Model Evaluation, and Feature Scaling to build accurate machine learning.
•	Experience in tuning and optimize ML/DL model for performance improvement.
•	Hands-on experience working with live projects on different machine learning techniques.
•	Having strong problem-solving capabilities.
•	Working with most demand industrial technology Framework like pandas, numpy, matplotlib, nltk(NLP), Keras(Deep Learning, tensor flow scikit learn(Machine learning), open cv

Projects: Image Classifications, Facial Expression Recognition, Recommendation system, Predictive Modeling, Regression Analysis

Solaris Publication

●	Research raw data and disseminate easily understood insights to provide organizational direction.
●	Produced a company guidelines and best practices document based on consumer insight analysis.
●	Performed regular web scraping to synthesize data.

Project: Image Classification, Seasonality Trends Analysis, Recommendation System

Jr. Data Scientist

🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

I have over 12+ years, Author, Data Scientist and Researcher with 8+ Years of Experience of Data Science technology and Research experience in wide functions including predictive modelling, data preprocessing, feature engineering, machine learning and deep learning. Currently, I work as Sr.Aws AI ML Solution Architect(Chief Data Scientist) at IBM India Pvt Ltd. Helps to Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with the foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology. Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimisation, and reliability.

If you catch me outside of work, I'm an adventurous traveller and avid cricket lover!

𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗶𝗲𝘀: Data science, Machine learning, Deep learning, Audio Processing, Python, Computer Vision, SQL, Keras, TensorFlow, Librosa, Pyaudio, Mlflow, tube flow, mlops, DVC, MLOps on AWS, Kubeflow on AWS, 

Enterprise LLMs Services: Amazon Bedrock, Amazon Titan, IBM WatsonX.ai, Azure OpenAI, NvidiaNemo, Google PaLM

LLMs Knowledge: GPT-4, Claude, ChatGPT, OpenChat, UltraLM, OpenCoderPlus, WizardLM, Nous Hermes, Vicuna, Baize-v2, Guanaco, Minotaur, Davinci003, Falcon, Alpaca Farm PPO Sim (GPT-4), Pythia, Cohere, Alpaca Farm PPO Human, Cohere Chat, Alpaca, Falcon 7B Instruct, Davinci001.

MLOps on AWS Services: Amazon SageMaker, AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS Batch, AWS Glue, AWS Data Pipeline, Amazon Elastic Container Service, AWS Farget, AWS Lambda, AWS EKS(Elastic Kubernetes Service), AWS ECS(Elastic Container Service), AWS APP Runner, AWS Cloud Formation, AWS ECR(Elastic Container Registry), AWS CloudWatch, AWS Auto Scaling, AWS CloudTrail, AWS EC2, AWS EBS(Elastic Block Store), AWS S3, AWS Route53

I also like meeting new people over a cup of coffee. Feel free to reach out at ashishpatel.ce.2011@gmail.com 

I am a passionate blogger, Researcher like to write about Machine learning and deep learning related Articles. you can visit my blog 

𝗟𝗶𝗻𝗸: https://medium.com/ml-research-lab.
book: https://www.amazon.in/B-V-Vishwas-ebook/dp/B08GLG46PQ/
Orcid Profile: https://orcid.org/0000-0002-5385-3897
Google Scholar : https://scholar.google.com/citations?hl=en&authuser=1&user=MfHiCbkAAAAJ

P.S. If you're passionate about creating tech-focused products to help people reach their potential, I am happy to connect at https://www.linkedin.com/in/ashishpatel2604

Sr. AWS AI/ML Solution Architect(Chief Data Scientist)

▪️Design and/or delivery of solutions, assets, and content at sustainable AI
▪️Apply methodology and governance around project development.
▪️Document and articulate clear understanding of customer/partner success criteria.
▪️Apply critical thinking, technical knowledge, customer/partner insights to solve problems with industry methodologies, and frameworks.
▪️Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology.
▪️Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimization, and reliability.
▪️Define, lead, and execute architecture design workshops and white-boarding sessions to build implementation plans.
▪️Develop deep relationships with key customers through solution integration to drive long-term IBM and AWS adoption.
▪️Team well with our key IBM & Red Hat business units, and strategic partners to support our partnership.
▪️Consult as a subject matter expert on our IBM/AWS technology and services portfolio.
▪️Author use cases, reference architectures, blog posts, and other field and external content.
▪️Work in sandbox environments to demonstrate IBM Hybrid Cloud Capabilities with AWS.

If you want to truly understand LLMs…
try calculating them by hand first.

That’s the magic of My friend Prof. Tom Yeh  Deep Learning Math Workbook.

It forces you to slow down.
Do the math.
See the mechanics.
Understand what’s happening behind every token prediction.

Here’s what each chapter teaches you — and why it matters for LLMs:

1. Dot Product

When you calculate it manually, you see how “similarity” is measured.
In LLMs → this becomes attention scores.

2. Matrix Multiplication

Doing it by hand shows how information combines and transforms.
In LLMs → this powers every forward pass through every layer.

3. Linear Layer

Manually computing W·X + b shows how inputs turn into features.
In LLMs → this is how Q/K/V projections and MLPs work.

4. Activation

Calculating ReLU, Sigmoid, Tanh makes you feel the effect of non-linearity.
In LLMs → this gives the model its reasoning ability (GELU, gates, etc.).

5. Artificial Neuron

Working through a single neuron reveals the logic inside every unit.
In LLMs → millions of these form the model’s internal understanding.

6. Batch

Manually applying operations across multiple examples shows training efficiency.
In LLMs → batching is the secret to stable, scalable learning.

7. Connection

Seeing residuals and wiring by hand makes network flow click.
In LLMs → this prevents memory loss and exploding gradients.

8. Hidden Layer

When you compute hidden transformations, you see abstraction forming.
In LLMs → this is where meaning and representation live.

9. Deep

Stacking operations manually shows how depth compounds understanding.
In LLMs → deeper stacks = richer reasoning and context retention.

10. Wide

Calculating wide layers reveals how models store more information.
In LLMs → width boosts knowledge capacity and expressiveness.

11. Softmax

Manually normalizing logits shows how models “choose” the next token.
In LLMs → this becomes the probability distribution we decode from.

12. Gradient

Computing gradients by hand shows how learning actually happens.
In LLMs → gradients tune billions of weights during training.

When you calculate these steps manually, you stop memorizing formulas…
and start understanding the machine.

That’s the real gift of Prof. Tom Yeh’s workbook — it makes deep learning visible, tangible, and uncomplicated.

♻️ Repost this if you want more people to build real intuition — not just run code.

P.S. Which chapter should I break down next with a simple example?

No alternative text description for this image

Everyone is yelling about “AGI” right now, but the people closest to the frontier are still giving wide, uncertain timelines.

Ilya just said 5–10 years on the Dwarkesh Patel podcast.
Demis Hassabis has said similar numbers.
Meanwhile, expert surveys still place the median expectation closer to the 2040s–2050s, and a good chunk of researchers think it could be much later — or never.

there is no consensus, just speculation.

That’s why this meme fits the current moment: companies barking at each other about AGI while no one has crossed the threshold.

The real progress isn’t in “AGI claims.”
It’s in the unglamorous fundamentals:

• reliability under real workloads
• reasoning that actually generalizes
• efficient multimodal integration
• scalable data + compute strategies
• products that deliver value today, not in hypothetical AGI futures

The noise is about AGI timelines.
The work is about making current AI systems genuinely useful.

Last week didn’t just feel big.I had the honour of inaugurating the upGrad Ahmedabad Learning Centre…

…and as an Amdavadi, this moment hit differently.
(It felt like the city is finally stepping into the future.)

But the real spark?

The conversations with the students.

The excitement.
The curiosity.
The obsession with AI.

It was absolutely electric.

And the questions they asked?

Not basic. Not surface-level.
But sharp, forward-thinking, “this generation is built different” kind of questions:

• “How do we build a career in AI if we’re starting from scratch?”
• “What skills in AI will matter the most in the next 3–5 years?”
• “Will AI replace jobs — or create even bigger opportunities?”
• “How do we stay relevant when AI tools evolve so fast?”
• “What does the industry actually expect from an AI fresher?”

These weren’t just questions.
They were signals.

Signals that Gujarat isn’t just catching up —
Gujarat is ready to lead.

And that’s exactly why this centre matters so much.

Here’s what made the day unforgettable:

1️⃣ Meeting Shubham Singh  & Ravneet Arora  
Two leaders who are fearlessly pushing to bring future-ready learning to Gujarat.

2️⃣ Feeling the energy in the room with 
Students. Working professionals. 
Tech enthusiasts.
Everyone driven by one thing — growth.

3️⃣ What this centre unlocks
• Data Science & AI/ML
• Full Stack Development
• Data Analytics
• Digital Marketing

These aren’t just courses.
They’re career accelerators.
Gateways to opportunities that didn’t exist a decade ago.

#Gujarat’s learners are ambitious.
They’re curious.
They’re hungry.

All they needed was a launchpad.

And now — they have one.

If you’re in #Ahmedabad and want to build a future-proof tech or AI career, this is your moment to step in.

♻️ Reshare this to inspire someone who needs to see it.

I learned something wild listening to an O’Reilly podcast: we’re only 5% of the way to real LLMOps maturity.

The guest was Abi Aryan ☯︎  of LLMOps.
Here are the biggest lessons I walked away with👇

⸻

1️⃣ LLMOps isn’t MLOps 2.0

It’s a different world.

• From deterministic → nondeterministic
• From “deploy the model” → manage distributed inference
• From accuracy → latency + throughput + memory + cost

And honestly?
Software engineers seem to transition faster than MLOps engineers —
because LLMOps feels more like distributed systems than classic ML.

⸻

2️⃣ Agent systems = distributed systems (not magic)

Modern setups usually have:

• A planner
• Specialized workers
• A reassembly layer

But this creates real problems:

• GPU load balancing
• Consensus when an agent fails
• Errors multiplying across the chain

Also… most “agents” today are just fancy if-this-then-that workflows.

⸻

3️⃣ Observability needs a new mindset

It’s not about watching dashboards.
It’s about anomaly detection.

• What changed across 50+ signals?
• What’s unusual before an error appears?
• What’s drifting?

It’s more like monitoring fraud detection than monitoring ML.

⸻

4️⃣ FinOps is the silent pain point

We’re maybe 5% of the way to good tooling.

Real cost buckets:

1️⃣ Data engineering
2️⃣ Compute-bound work
3️⃣ Communication-bound bottlenecks (GPU↔GPU)

Most teams overspend because they:

• Use reasoning models unnecessarily
• Overfill KV cache
• Optimize compute when the real issue is memory

⸻

5️⃣ RL has a fatal flaw

LLM RL environments are static…
but agents need dynamic learning.

Once a model “finishes” the environment — it plateaus.
It’s the same freshness problem we see in RAG.

Curriculum learning exists in research,
but no practical implementation yet.

Huge gap.

⸻

6️⃣ The future (and how I’m thinking about my own skills)

Areas that feel massively underserved:

• System-level profiling
• Dynamic RL environments
• Data engineering (big practical wins)

And the skill order that makes sense:

• Learn distributed systems
• Learn profiling (memory, compute, comms)
• Then go deeper into model internals

LLMs aren’t “another ML model.”
They’re Software 3.0 — end-to-end systems where the model is the product.

If this breakdown helped, consider resharing ♻️

P.S. What part of LLMOps do you think is most underserved right now?

Catastrophic Forgetting’s 𝗦𝗰𝗮𝗹𝗲 𝗟𝗮𝘄: Why Bigger LLMs Forget Faster 🚨

Ever fine-tuned a model and watched it forget everything? 🤯 The bigger the model, the faster it forgets. 🧠💨
A few months ago, we fine-tuned a 70B-parameter LLaMA model. It didn’t just forget the new task—it lost the skills and safety mechanisms we worked so hard to build. 😱

Why? That’s when I discovered 𝗖𝗮𝘁𝗮𝘀𝘁𝗿𝗼𝗽𝗵𝗶𝗰 𝗙𝗼𝗿𝗴𝗲𝘁𝘁𝗶𝗻𝗴 and 𝘁𝗵𝗲 𝗦𝗰𝗮𝗹𝗲 𝗟𝗮𝘄.

𝗪𝗵𝗮𝘁’𝘀 𝗛𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴? 🤔
Fine-tuning large models often causes catastrophic forgetting. Larger models (70B+) forget faster than smaller ones (7B).

Why?
 • High-Dimensional Loss Landscape 🌄: Bigger models have more catastrophic basins. Even small shifts can push them into these basins, causing knowledge loss.
 • Ripple Effects of Fine-Tuning 🌊: Tuning one layer can trigger a chain reaction, destroying unrelated skills.

𝗧𝗵𝗲 𝗦𝗰𝗮𝗹𝗲 𝗟𝗮𝘄 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 📊
A 13B model lost double the capabilities of a 7B on unrelated tasks after fine-tuning.
Bigger models = Bigger forgetfulness risks ⚠️

𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲 (𝗪𝗵𝗲𝗻 𝗶𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱)

 • 1989: "Catastrophic Forgetting" coined by McCloskey & Cohen.
 • 2016: French et al. confirm neural networks suffer from catastrophic forgetting.
 • 2017: Kirkpatrick et al. propose Elastic Weight Consolidation (EWC) to mitigate forgetting.
 • 2023: Luo et al. find larger LLMs (1B-7B) forget more severely.
 • 2023: Lermen et al. show fine-tuning impacts LLM adaptability.
 • 2024: Kalajdzievski et al. identify an inverse relationship between fine-tuning and forgetting with LoRA.
 • 2025: Google Research introduces Nested Learning to address catastrophic forgetting in AI.

𝗧𝗵𝗲 𝗟𝗼𝘀𝘀 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲: 𝗔 𝗛𝗶𝗱𝗱𝗲𝗻 𝗧𝗿𝗮𝗽 🕳️
Imagine a model’s knowledge as a wide canyon. Fine-tune in the wrong direction, and it’s like walking it off a cliff. Even a small update can push it into a "catastrophic basin"—performance collapses. 💣

𝗗𝗼𝗲𝘀 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿-𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗧𝘂𝗻𝗶𝗻𝗴 𝗛𝗲𝗹𝗽? 🚫
LoRA freezes most parameters, but even minimal updates can lead to massive skill loss. Fine-tuning GPT-3.5 on just 10 adversarial examples caused an 88.8% increase in harmful responses. 💀

𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻? 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆 🔄
Forget keeping weights close—preserve the geometry of the loss landscape.
 Methods like Sharpness-Aware Minimization (SAM) and Functionally Invariant Paths (FIP) reshape the landscape for stability. 🧭

𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 🔑

 • Bigger models = Bigger forgetfulness risks ⚠️
 • Small updates can lead to catastrophic forgetting 💥
 • Flat, wide loss landscapes are your best defense 🌍

The real challenge is optimizing the loss landscape geometry to prevent disastrous forgetting. 🌟

♻ Repost if you’re building or fine-tuning LLMs—your team needs to know! 🚀
P.S. Ever destroyed a model with just one fine-tune? Share your story in the comments! 🗣️👇

The Turing Test isn’t broken—it’s just irrelevant now.

AI can sound human without understanding a thing.

Mimicry is easy; reasoning isn’t.

Old benchmarks can’t measure modern capabilities.

We need tests that track reliability and real problem-solving.

Impressing humans for a few minutes isn’t intelligence.

This meme captures the gap perfectly.

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