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

Daniel Svonava

Superlinked

superlinked.com makes it easier to turn data into vector embeddings

Co-Founder & CEO

creable.io

creable.com is the world's biggest influencer database (300M creators)

Advisory Board Member

Tater Games

tatergames.com is a data-driven game studio

Board Member

Primeflow

primeflow.com built tools for super-connectors and their network-driven business

Founder & CTO

Google

2016 - 2018
Tech-lead for YouTube Reservation Ads forecasting (Can a reservation be booked or not?) and pricing (How much should we charge for it?).

2015
Tech-lead for YouTube Video Ads traffic prediction and cross-Google Ads reach prediction, enabling ad buying in Adwords and DBM.
Focus on making Google Ads useful for Brand advertisers.

2013 - 2014
Within YouTube monetization I'm constantly switching between being an analyst who organizes and makes sense of data, builds prototype visualizations and spreads around the insights to help decision making and product design and being an engineer who uses these insights to build useful production systems.

Senior Software Engineer

47pixels

We were a software startup in the Story-telling space. Major products:
47posters - B2B SaaS for Print Service Providers launched in 7 countries
memoarc - B2C storytelling photogallery
dofsome - B2C minimalistic, high-quality photo hosting.
Reach out to me for a complete portfolio.

Exploratory large-scale information mining from unstructured data. Patent granted.

Software Engineering Intern

IBM Research

Joined the Information analytics group within the Mathematics and Computer Science department again to continue my work on matrix reordering and co-clustering remotely via IBM SK.

Research Internship

Joined the Information analytics group within the Mathematics and Computer Science department. Co-authored a research paper involving graph theory, clustering and visualization. Worked on a web-based marketing intelligence tool employing data-mining techniques and back-end infrastructure optimization.

"Great Minds" Research Internship

Slovak University of Technology

The course is called Development of effective algorithms and programs. I prepared problem statements and solutions for assignments and competitions. I also set problems for ProFIIT, national programming competition for High school students.

Teaching assistant

Vector Compute @ Superlinked | xYouTube

➡️ Building a compute framework for turning data into vector embeddings.
🤗 Connect your data infrastructure to your vector database with Superlinked.

Join our product preview:
📋 Try it out in a Python notebook with no dependencies.
↗️ Design the perfect vectors for your retrieval use-case.
🔧 Mix and match streaming, batch, structured and unstructured data.
🚀 Launch to production with your favorite Vector Database.

➡️ Let's sort out information retrieval across your organization!
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Previous:

Daniel is a Senior Software Engineer at Google, where he tech-leads, designs and builds forecasting & pricing systems for ads worth billions of dollars. These are distributed systems that run on thousands of machines, process terabytes of data per query and combine Machine Learning modeling with game-like simulation of tens of thousands ad campaigns - all happening in real-time to enable thousands of concurrent users of buying tools like AdWords and DBM.

Daniel wields a rare combination of deep technical skills, a user-centric drive and an ability to align multiple teams towards a common goal - launching and landing high quality cutting edge products.

Daniel co-founded a tech startup that brought machine vision to the consumer photo space 10 years ago, he was an invited speaker at an ML dev conference in Zurich and speaks at startup events regularly. He holds multiple patents with Google, is published in a peer-reviewed ML journal from his time at IBM Research and is a Google Code Jam on-site finalist.

On weekends he flies, climbs, skis and bikes in the Alps, see: http://instagram.com/mountain.supo

Building Production AI: Key Learnings from the Field. 🤔💡

After reviewing hundreds of AI engineering papers, case studies, and repos, a few core principles consistently stand out.

Here's the scoop:

1️⃣ Fine-Tuning is a Data Problem, Not a Code Problem.
Many engineers get lost in hyperparameters.

But the best practitioners obsess over data quality, preparation, and verification.

1) Start with Fundamentals.
▸ OpenAI’s classic guide on best practices for fine-tuning is still a must-read, covering data prep, common mistakes, and the core mechanics. https://lnkd.in/eXqpFRgW

2) Learn from the State-of-the-Art.
▸ The Llama 3 paper is a gold mine for post-training data, and it covers the crucial (and often overlooked) topic of data verification. https://lnkd.in/eH4C9ECF

The lesson is clear: high-quality, verified data is the biggest lever you have. 

2️⃣ Master Prompt Engineering Before Anything Else.
Before diving into complex architectures, mastering the art of instruction is non-negotiable.

1) Interactive Learning is Key.
▸ Anthropic’s Prompt Engineering Tutorial is a standout resource. 

Its Google Sheets-based exercises provide immediate, hands-on feedback that builds real intuition. https://lnkd.in/eDv28QQf

A few hours of structured, interactive prompting is more valuable than weeks of passive learning. 

3️⃣ Plan for Inference from Day One.
A great model that's too slow or expensive to run is a failed project. Understanding the tradeoffs of inference optimization is critical.

1) Know Your Tradeoffs.
▸ "Efficiently Scaling Transformer Inference" (Pope et al., 2022) is an amazing paper that provides a clear guideline for optimizing for latency vs. throughput. https://lnkd.in/eVyWJQ62

2) Understand Agentic Planners.
▸ The "Chameleon" paper is a favorite for understanding how LLMs use tools and their failure modes—a key insight for building robust agents. https://lnkd.in/e6C5Pjup

4️⃣ Study Real-World Case Studies.
Theory is essential, but learning from teams who have successfully deployed AI is invaluable.

These case studies provide battle-tested insights you won't find in a paper:

▸ LinkedIn: How they scaled their systems. https://lnkd.in/eBnJKnZa
▸ Pinterest: A deep dive into their Text-to-SQL implementation. https://lnkd.in/euBEmsxp
▸ Gmail: The classic Smart Compose case study from 2019. https://lnkd.in/eZ646V8F
▸ Grab: How they leverage AI in their services. https://lnkd.in/e4fUGgWg

What's missing? What else do you recommend? 👇

Build better AI Search with Superlinked | xYouTube

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