Paul Iusztin

Stealth AI Startup

Founding Al Engineer

Decoding ML

Content to help you design, code, and deploy production-grade AI, GenAI and information retrieval systems.

Founder

Remote Work

💼 Senior machine learning engineer, contractor, consultant, and freelancer with 𝟔+ 𝐲𝐞𝐚𝐫𝐬 𝐨𝐟 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 offering scalable and modular machine learning solutions for businesses worldwide.

➞ I can design, implement, train, evaluate, and operate end-to-end ML pipelines for your business. I can scale up your ML systems by leveraging the latest MLOps technologies.

➞ I have spent 7+ years writing code in Python, but I also enjoy using Node.js for web development and C/C++ for optimizing various applications.

➞ My go-to machine learning frameworks are PyTorch, Keras, and Sklearn. Also, my favorite MLOps tech stack is AWS, W&B/MLFlow, and DVC.

🔥The world of AI is constantly changing, new technologies are born almost daily, and therefore, I am always open to new challenges.

💬 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐦𝐞, and let's see how I can help your business.

Senior Machine Learning Engineer • Freelancer • Contractor

Metaphysic.ai

🟡 Computer vision & deep learning workflow engine: orchestrate & execute

The client, a leading GenAI platform, faced difficulties scaling and breaking down its complex ML jobs. Their manual processes and CLI-based tooling added significant friction in serving their clients. Thus, the client required a more intuitive and scalable tool that could be used across the company to train and serve its ML models.

→ 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝗲𝗱 the ML worker architecture to follow DDD principles, making it more 𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲, 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, and 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗯𝗹𝗲.

→ 𝗗𝗲𝗰𝗿𝗲𝗮𝘀𝗲𝗱 the 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 of the data curation DL models 𝗯𝘆 𝟴𝟮.𝟳%

→ 𝗘𝘅𝘁𝗲𝗻𝗱𝗲𝗱 their clustering algorithm to accurately run on 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 >𝟭𝟬𝗸 𝗶𝗺𝗮𝗴𝗲𝘀 and 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁 the number of 𝗰𝗹𝘂𝘀𝘁𝗲𝗿𝘀 and 𝗼𝘂𝘁𝗹𝗶𝗲𝗿𝘀.

Senior Machine Learning Engineer

⚫️ Scalable ML batch pipeline for continuous training & inference

The client, a leading GenAI platform, struggled with manual training and calling their ML models. As a tedious and unscalable process, they needed to automate their ML processes completely and integrate them with their current web platform.

→ 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗱 and 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 a 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 that orchestrates the ML steps into 3 fully automated batch pipelines: feature, training, and inference, 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗮 𝘁𝗲𝗱𝗶𝗼𝘂𝘀 𝟮-𝗵𝗼𝘂𝗿 𝗺𝗮𝗻𝘂𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀/𝘃𝗶𝗱𝗲𝗼.

→ 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 the 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 of the batch ML pipeline 𝗯𝘆 𝟲𝟮.𝟱% while preserving the same accuracy and quality.

→ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝗠𝗟𝗢𝗽𝘀 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 such as IaC, CI/CD, monitoring, experiment tracking, and model registries, making the system reproducible, testable and trackable.

→ 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 a scalable & cost-effective 𝗮𝘀𝘆𝗻𝗰 𝗯𝗮𝘁𝗰𝗵 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 on top of AWS ECS & SQS that scales in and out based on the number of jobs, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 their 𝗔𝗪𝗦 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝟱𝟮%.

⚫️ Big data ingestion & archiving pipeline

The client wanted to expand its platform features by securely ingesting, preprocessing, and storing TB of data into their platform.

→ 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 to the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of a 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 >𝟓 𝐓𝐁 of data in 𝐨𝐧𝐞 𝐠𝐨 in a reliable, fast, and secure way.

→ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 a scalable 𝗱𝗮𝘁𝗮 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 that supports multiple media types (images, videos, audio, 3D objects), archiving the original files on an affordable storage solution, 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗰𝗼𝘀𝘁𝘀 𝗯𝘆 𝘂𝗽 𝘁𝗼 𝟴𝟭%.

Towards Data Science

⚫️ ML and MLOps engineering blog

Helping machine learning engineers level up through hands-on practical articles and tutorials on designing and productionizing ML systems.

➞ Published 𝐓𝐡𝐞 𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝟕-𝐒𝐭𝐞𝐩𝐬 𝐌𝐋𝐎𝐩𝐬 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 with 90k+ views on Medium and 600+ stars on GitHub. An MLE & MLOps course that teaches you how to design, build, and deploy an end-to-end ML batch system: https://github.com/iusztinpaul/energy-forecasting

Machine Learning Engineer / Technical Writer

CoreAI

⚫️ Real-time social media recommender system using machine learning & Kafka

The client, a social media start-up from Israel, needed a real-time dashboard and recommender system on top of their Kafka infrastructure using modern ML and MLOps solutions.

➞ 𝐋𝐞𝐝 the architecture system design of a modular real-time social media recommender system built on top of the client's Kafka nervous system that leverages streaming and ML technologies such as Kafka Streams, Kotlin, PyTorch, Python, and Docker.

➞ Quickly 𝐩𝐥𝐚𝐧𝐧𝐞𝐝 and 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 a scalable recommender system based on heuristics, picked through A/B testing, using Kafka Streams and Kotlin. It served as a vital entry point and baseline for future algorithms.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a dashboard that computes KPIs in 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐟𝐨𝐫 𝟏𝟎𝟎𝐤+ 𝐮𝐬𝐞𝐫𝐬 on the client's Kafka infrastructure using ksqlDB, SQL, Elasticsearch, and Apache Superset.

Machine Learning Tech Lead

⚫️ coreControl - end-to-end MLOps infrastructure

The client, a machine learning service provider, required an end-to-end ML/MLOps infrastructure to scale their operations and save development resources across multiple AI projects.

➞ 𝐋𝐞𝐝 the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of coreControl by integrating the software with the client's products and collaborating with other ML engineers, developers, and graphic designers.

➞ 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 the 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐢𝐦𝐞 across the client's machine learning projects 𝐛𝐲 𝟑𝟎% using coreControl.

➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 the data backbone of coreControl that syncs the data between multiple MLOps tools and exposes them to a UI through a RESTful API using Python, FastAPI, PostgreSQL, Docker, and AWS.

➞ 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 a Python package that wraps various MLOps tools that standardizes the data scientists' work and centralizes all the ML artifacts to easily be analyzed in a single place using Python, DVC, and ClearML.

⚫️ Prototyping AI solutions for Demand Forecasting, Churn Prediction, and Customer Segmentation

The client, a machine learning start-up, wanted to expand their services portfolio by prototyping and gaining knowledge in various AI fields that leverage structured retail data.

➞ 𝐋𝐞𝐝 𝐚 𝐭𝐞𝐚𝐦 𝐨𝐟 𝟐 to develop an ML clustering solution that aggregates data from multiple sources and finds customer segments and insights for new potential clients.

➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a prototype for churn and demand forecasting used as a proof of concept to close deals with various clients.

➞ 𝐒𝐚𝐯𝐞𝐝 𝟒𝟎% of 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 by building a reusable Python ML framework that can quickly be tailored to custom problems based on structured data.

Politehnica University of Timisoara

⚫️ Teaching the foundations of 𝐚𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 laboratory.

➞ Supervised and unsupervised learning
➞ Linear regression, SVMs, Decision trees
➞ Clustering, Dimensionality reduction
➞ Dense neural networks
➞ Convolutional neural networks
➞ Recurrent neural networks
➞ Transformers

Teaching Assistant

Everseen

🟡 Autonomous supply chain system using computer vision & deep learning

The client, a well-known player in the retail sector, needed to expand its deep learning and computer vision solutions by researching and building autonomous systems for its operations in the retail industry.

➞ 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 a Mask-RCNN model for 2D object detection and segmentation on 100k proprietary retail images, with an 𝐦𝐀𝐏 𝐨𝐟 𝟖𝟓%.

➞ 𝐒𝐩𝐞𝐞𝐝 𝐮𝐩 the 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐭𝐢𝐦𝐞 of the Mask-RCNN model 𝐛𝐲 𝟓𝟎% by levering Docker, ONNX, and TensorRT to containerize and accelerate the models.

➞ 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 a scalable classifier using an image retrieval model that can dynamically handle new classes without additional training.

➞ 𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐝 the 𝐫𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐛𝐢𝐥𝐢𝐭𝐲 of the 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 by implementing MLOps solutions, such as DVC & MLFlow, that included data & model versioning, registries, and experiment tracking features.

Continental

🟡 Autonomous driving system using computer vision & deep learning

The client, a well-known player in the automotive sector, needed to create an autonomous driving car prototype that could accurately detect traffic participants by leveraging DL and CV solutions.

➞ 𝐋𝐞𝐝 the 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 of a prototype for real-time traffic participants detection by deploying a CenterNet model for 3D object detection on a test car using an Nvidia Drive AGX C++ platform.

➞ 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 the reproducibility of the experimentation process by implementing MLOps solutions (DVC) that version unstructured big data.

➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a replacement to the Hungarian algorithm that 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞𝐝 the 𝐬𝐩𝐞𝐞𝐝 by 𝟑𝟎%, making the system 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞.

➞ 𝐒𝐚𝐯𝐞𝐝 𝟐 𝐝𝐚𝐲𝐬, in 𝐞𝐯𝐞𝐫𝐲 𝐦𝐨𝐧𝐭𝐡, of 𝐫𝐞𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 work by automating the steps for the creation of the 𝐦𝐨𝐧𝐭𝐡𝐥𝐲 demo.

Mid-Level Machine Learning Researcher

⚫️ Autonomous driving research using computer vision & deep learning

The client, a well-known player in the automotive sector, needed to expand its research into the autonomous driving field using novel deep learning and computer vision methods.

➞ 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 in 3D object detection, depth estimation & completion, tracking, and sensor fusion algorithms using various sensors such as images, radar & LiDAR point clouds.

➞ 𝐑𝐞𝐝𝐮𝐜𝐞𝐝 𝐛𝐲 𝟒𝟎% the 𝐦𝐞𝐦𝐨𝐫𝐲 𝐮𝐬𝐚𝐠𝐞 of the 3D object detection code by introducing the Dynamic Voxelization algorithm into the preprocessing pipeline.

➞ 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 state-of-the-art models to generate LiDAR-like dense point clouds from sparse Radar point clouds on various autonomous driving datasets, such as KITTI, nuScenes, DDAD, and A2D2.

Junior Machine Learning Researcher

Elementum Technologies

⚫️ Dorel - An Uber for handymen

𝐃𝐨𝐫𝐞𝐥 is an app that connects handypersons with customers. It contains a review and portfolio system where any handyperson can build his profile. Based on that, a user can determine whether he fits the job. The app intends to bring, once again, trust in handypersons.

➞ 𝐏𝐢𝐭𝐜𝐡𝐞𝐝 the 𝐢𝐝𝐞𝐚 to various start-up incubators.

➞ 𝐌𝐚𝐧𝐚𝐠𝐞𝐝 the backend components of the software solution of Dorel using an Agile methodology.

➞ 𝐋𝐞𝐝 the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of the Django RESTful API server and the notifications microservice.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐝 the Redux layer of the React Native mobile app.

Co-Founder

🤖 Senior Machine Learning Engineer • MLOps • Contractor ~ Helping startups engineer production-ready ML/AI systems.

I am a senior AI engineer and contractor with 8+ years of experience. I design and implement modular, scalable, and production-ready ML systems for startups worldwide.

My central mission is to build an AI academy and data-intensive AI/ML products that serve the world.

Since training my first neural network in 2017,  I have 2 passions that fuel my mission:

→ Designing and implementing production AI/ML systems using software engineering and MLOps best practices.

→ Teaching people about the process.

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I currently develop vertical AI agent solutions for a US startup. In the past, I built AI, ML and MLOps products for Metaphysic, CoreAI, Everseen, and Continental.

I am the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade AI systems.

As a content creator, I co-authored the LLM Engineer’s Handbook, an Amazon bestseller and industry standard on LLM, RAG, and LLMOps solutions, which sold over 15k+ copies.

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If you want to learn how to build end-to-end production-ready AI systems with software engineering and MLOps best practices, subscribe to my Substack:

🔗 https://decodingml.substack.com

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💬 If you need AI solutions for your business, let’s discuss!

🌎 Only open to full remote positions as a contractor.

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Contact:

✉️ Email: p.b.iusztin@gmail.com

💻 Decoding ML: https://decodingml.substack.com

🟡 Scalable ML batch infrastructure for continuous training & inference

The client, a generative AI leader, struggled with manual training and calling their ML models. As a tedious and unscalable process, they needed to automate their ML processes completely and integrate them with their current web platform.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a modular 𝐏𝐲𝐭𝐡𝐨𝐧 𝐩𝐚𝐜𝐤𝐚𝐠𝐞 that composes the ML model required steps into 3 main batch pipelines that can easily be automated: feature, training, and inference, 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐢𝐧𝐠 a 𝐭𝐞𝐝𝐢𝐨𝐮𝐬 𝟐-𝐡𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐚𝐥 𝐩𝐫𝐨𝐜𝐞𝐬𝐬/𝐯𝐢𝐝𝐞𝐨.
➞ 𝐃𝐞𝐩𝐥𝐨𝐲𝐞𝐝 a scalable & cost-effective 𝐛𝐚𝐭𝐜𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 and 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 on top of AWS ECS & SQS that scales in and out based on the number of jobs, 𝐬𝐚𝐯𝐢𝐧𝐠 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬 𝐨𝐟 𝐝𝐨𝐥𝐥𝐚𝐫𝐬/𝐦𝐨𝐧𝐭𝐡.
➞ 𝐒𝐚𝐯𝐞𝐝 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐟 𝐟𝐮𝐭𝐮𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 by using good software practices to make the Python module & batch cloud infrastructure flexible and composable.
➞ 𝐁𝐮𝐢𝐥𝐭 a 𝐯𝐨𝐢𝐜𝐞 𝐬𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬 𝐏𝐨𝐂 in ~1 week required to 𝐜𝐥𝐨𝐬𝐞 the 𝐝𝐞𝐚𝐥 with one of the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐜𝐥𝐢𝐞𝐧𝐭𝐬.

⚫️ Big data ingestion & archiving pipeline

The client wanted to expand its platform features by securely and safely ingesting, preprocessing, and storing TB of data into their platform.

➞ 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 to the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of a 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 >𝟓 𝐓𝐁 of data in 𝐨𝐧𝐞 𝐠𝐨 in a reliable, fast, and secure way.
➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a scalable 𝐝𝐚𝐭𝐚 𝐩𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports multiple media types (images, videos, audio, 3D objects) and creates small preview files that are cheap to transfer, store and visualize. The 𝐨𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐟𝐢𝐥𝐞𝐬 are 𝐚𝐫𝐜𝐡𝐢𝐯𝐞𝐝 on an affordable storage solution 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐜𝐨𝐬𝐭𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟖𝟏%.

Everybody talks about shipping AI products, but nobody shows you how.

These 5 books are all you need (and yes... I've read them all):

𝟭/ 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗯𝘆 Chip Huyen 
🔗: https://lnkd.in/dwmcghyA

One of the best books I've read on designing ML systems.

It's beginner-friendly.

And it provides a strong intuition into what an end-to-end ML system looks like.

𝟮/ 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝗯𝘆 John Berryman 𝗮𝗻𝗱 Albert Ziegler 
🔗: https://lnkd.in/dGXBYAFU

If you're obsessed with seeing the big picture and thinking in systems (like me), you'll love this book.

It will show you how to engineer your prompts effectively.

You will also learn how to manipulate your prompt, make it flexible, scalable, and squeeze the most out of your models.

P.S. As John said, this book should be called 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀.

𝟯/ 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗯𝘆 Chip Huyen 
🔗: https://lnkd.in/dPKStJW3

This book has one of the best (and clearest) explanations of:

• AI vs. ML Engineering
• RAG (from classic text search to more complex, semantic-based systems)
• Building agentic systems
• LLMOps (Especially observability and user feedback)
  
𝟰/ 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 Louis-François Bouchard 𝗮𝗻𝗱 Louie Peters 
🔗: https://lnkd.in/d9v6HyZr (𝟭𝟱% 𝗼𝗳𝗳 𝘂𝘀𝗶𝗻𝗴 𝗮𝗳𝗳𝗶𝗹𝗶𝗮𝘁𝗲 𝗰𝗼𝗱𝗲 𝗣𝗮𝘂𝗹_𝟭𝟱)

This book will walk you through how to implement all the core algorithms required to build LLM applications.

You'll use LangChain and LlamaIndex to implement all kinds of RAG techniques (starting from the vanilla architecture).

It's great for people who are mid-to-entry-level engineers.

𝟱/ 𝗟𝗟𝗠𝘀 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 Chris Brousseau 𝗮𝗻𝗱 Matthew Sharp 
🔗: https://lnkd.in/d_xqpfUz

This book focuses entirely on shipping AI.

Specifically, it does a great job of explaining:

• How to optimize LLMs using different tools
• The data engineering involved in building an LLM app
• Training and serving LLMs at scale
• How to think about infrastructure when serving LLMs
• etc.
  
𝗕𝗢𝗡𝗨𝗦: 𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿'𝘀 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 𝗯𝘆 𝗺𝗲 𝗮𝗻𝗱 Maxime Labonne 
Link: https://lnkd.in/dRmBZXdR

I couldn't list a bunch of books without mentioning my baby.

This book merges my obsession with thinking about end-to-end systems with Maxime Labonne 's love for fine-tuning.

We walk you through all the steps to build an LLM & RAG application.

Oh… and we’re working on a 2nd edition due to popular demand.

P.S. How many of these books have you read?

Senior AI Engineer • Founder @ Decoding AI ~ Building an army of AI agents and teaching you about the process.

90% of AI engineers build LLM apps the same way.

They spin up a RAG system, tweak a few prompts, and then ask:

"Does it work?"

Or worse... "Should I switch to the new model that just dropped?"

But these are the wrong questions to be asking.

Why? Because you have no way to know.

It's impossible to answer these questions without an evaluation framework.

This is why Hugo Bowne-Anderson suggests we use Evaluation-Driven Development (EDD).

The concept isn't new (especially for those with a background in ML).

The idea is to shift the development mindset from launch first, evaluate later to evaluate first, launch better.

Here's how it works:

1. 𝗨𝘀𝗲 𝘀𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗱𝗮𝘁𝗮 to uncover failure modes before launch.
2. 𝗟𝗮𝗯𝗲𝗹 𝗯𝘆 𝗵𝗮𝗻𝗱 to build intuition about what “good” looks like.
3. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 - even a spreadsheet is enough.
4. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 it to track changes over time.
   
This loop will help you compare models, debug failures, and build confidence that your system works before it hits production.

You wouldn’t build a search engine or recommender system without robust evaluation.... 
So why build an LLM app without one?

Hugo Bowne-Anderson explains EDD beautifully in a guest post he wrote for Decoding AI Magazine.

It’s one of the most practical, no-fluff guides I’ve read on building reliable LLM systems.

Read it here → https://lnkd.in/dWpSyfcq

✅ 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.

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

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

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

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.

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