Andrei Apostol
Verified Expert in Engineering
Artificial Intelligence Developer
Iași, Iași County, Romania
Toptal member since September 1, 2022
Andrei studied computer science in his hometown in Romania and completed his master's degree in AI at the University of Amsterdam. He has accumulated practical experience in AI over years of training, developing data processing pipelines, and deployment. He is an engineer always looking forward to new challenges. Andrei also has academic experience through publishing two papers on neural pruning and quantization, which were well received by the academic community.
Portfolio
Experience
- Machine Learning - 4 years
- Computer Vision - 3 years
- Python 3 - 3 years
- PyTorch - 3 years
- Generative Pre-trained Transformers (GPT) - 2 years
- Docker - 2 years
- Object Detection - 2 years
- Neural Network Pruning - 2 years
Work Experience
Machine Learning Engineer
Omnimodular
- Built a custom and flexible BERT-like architecture for multi-class document classification and trained on data from various clients, obtaining 90-94% average accuracy.
- Combined the traditional NLP augmentation model with the GPT-3 large language model to do data augmentation for clients, reducing the error rate by 50%.
- Used Hugging Face datasets based on Apache Arrow to handle large volumes of data that normally would not fit in memory and implemented an efficient and replicable data processing pipeline with batching and multiprocessing.
- Held regular client meetings, giving high-level overviews of our technical solution and explaining our core metrics.
Machine Learning Engineer
Braincreators
- Trained a YOLOv5 object detection network for waste management and recycling, obtaining mean-average-precision scores of over 95% on over 40 classes of objects with a speed of over 200 frames per second on a conventional GPU.
- Built an app around the object detection network using FastAPI to expose the endpoints and Streamlit for the UI, converting the network to the ONNX format after training for faster inference time.
- Created a module for out-of-distribution detection using the CLIP pre-trained model, obtaining over 97% accuracy in the in-/out-of-distribution classification while allowing the class taxonomy to be changed without re-training.
- Held regular meetings with project stakeholders, led demos and presentations for the client to provide estimates for the next milestones, and conducted sprint planning sessions, keeping track of progress.
- Published the paper "Highlights of AI Research in Europe" in a special edition of the European Journal of AI, demonstrating that pruning and quantization can bring greater acceleration when used without sacrificing accuracy.
- Implemented object tracking using the SORT algorithm with re-identification to follow the trajectories of objects over time. Obtained over 85% MOTA (multi-object tracking accuracy) and 80% IDF1.
Machine Learning Engineer
Mantis NLP
- Designed a BERT-based architecture for medical research document tagging. Implemented an efficient and scalable training pipeline, able to process 15M+ documents in a matter of hours. Achieved a state-of-the-art micro-F1 score of 70%.
- Created a user-facing application that allows for easy creation, deployment, and removal of machine learning models in production using AWS Sagemaker. Included monitoring, alerts, and a complete test suite to ensure quality and reliability.
- Used Langchain with ChatGPT and FAISS. Created a personal assistant that could answer a user's questions based on their collection of notes. Performed prompt engineering to obtain better-quality responses.
- Held close contact with key clients and stakeholders to ensure we were aligned on requirements and created the highest quality deliverables at all stages of the project.
Machine Learning Research Internship
BrainCreators
- Researched and built expertise in neural network pruning techniques as part of my master's dissertation.
- Developed a novel pruning algorithm that obtains state-of-the-art results for high sparsity scenarios and other properties such as the ability to prune during training, computational tractability, and hyperparameter invariance.
- Received the Best Paper award for writing a scientific paper around said algorithm and publishing it at the BeneLearn 2020 conference held in Belgium, Netherlands, and Luxembourg.
Data Scientist
FeedbackFruits
- Built a time series forecasting model with the SARIMA method, achieving a low mean square error for all predictions within the confidence bound.
- Created an encoder/decoder gated recurrent unit network for document part classification, obtaining over 90% accuracy.
- Deployed the trained models to production by exposing the core functionality via RESTful APIs and monitored the performance in production.
Research Scientist Intern
Amazon.com
- Analyzed customer behavior on the platform and developed a random forest model with a high ROC-AUC score.
- Handled large volumes of data using Apache Spark and created data processing pipelines to filter and prepare data using the Python and Scala APIs and Spark SQL.
- Conducted A/B testing and integrated the resulting model into several Amazon sites.
Project History
FlipOut | Uncovering Redundant Weights via Sign Flipping
https://github.com/AndreiXYZ/flipoutIt can remove over 98% of the connections in common networks with little to no impact on accuracy, allowing for large speed gains. Compared to baselines from literature, this method can prune during training, is insensitive to the selection of hyperparameters, and allows for selecting the sparsity level directly.
I wrote a paper around this method and published it in BeneLearn 2020, obtaining the Best Paper award.
Education
Master's Degree in Artificial Intelligence
University of Amsterdam - Amsterdam, The Netherlands
Bachelor's Degree in Computer Science
Alexandru Ioan Cuza University - Iași, Romania
Certifications
Model Parallelism: Building and Deploying Large Neural Networks
NVIDIA
IELTS Academic Certificate (Native Level)
British Council
Skills
Libraries/APIs
PyTorch, Matplotlib, OpenCV, Scikit-learn, Pandas, PyTorch Lightning, OpenAI API, TensorFlow, NumPy, DeepSpeed
Tools
TensorBoard, You Only Look Once (YOLO), Slack, GitHub, ChatGPT, Git, Docker Compose, Open Neural Network Exchange (ONNX), Spark SQL, ARIMA, Amazon SageMaker
Languages
Python 3, Python, Bash, SQL
Frameworks
Flask, Streamlit, Apache Spark
Platforms
Visual Studio Code (VS Code), Rocket.Chat, Linux, Docker, Zeppelin, AWS IoT, Amazon Web Services (AWS)
Paradigms
Object-oriented Programming (OOP), Agile, REST
Other
Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Long Short-term Memory (LSTM), Deep Neural Networks (DNNs), Neural Network Pruning, Quantization, FastAPI, Object Detection, Transformers, BERT, Classification, English, Google Cloud/Suite, Artificial Intelligence (AI), Proof of Concept (POC), Minimum Viable Product (MVP), Natural Language Understanding (NLU), Contract, Information Extraction, Generative Pre-trained Transformers (GPT), OpenAI GPT-3 API, Data Visualization, Statistics, Software Engineering, Data Science, Scientific Data Analysis, Random Forests, APIs, Scientific Computing, Research, NVIDIA Triton, Detection Engineering, Hugging Face, DVC, Machine Learning Operations (MLOps), Language Models, Algorithms, Data Analytics, Large Language Models (LLMs), NVIDIA TensorRT, Image Processing, Generative Adversarial Networks (GANs), Generative Artificial Intelligence (GenAI), Optical Character Recognition (OCR), PDF, Chatbots, Information Retrieval, Cluster Computing, Time Series, 3D, OpenAI GPT-4 API, LangChain, FAISS, Generative Pre-trained Transformer 3 (GPT-3), Prompt Engineering
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