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

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.

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Alex Razvant

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