As the founder of Martin Data Solutions and a freelance data scientist, I’ve had the opportunity to work across a range of data-focused domains, including Regression, Classification, Natural Language Processing, Large Language Models, Retrieval-Augmented Generation, Neural Networks, Ensemble Methods, and Computer Vision. I’m passionate about building innovative solutions and skilled in implementing both ML and LLM systems. My experience includes hands-on teaching, developing business-focused applications, and technical writing.
My technical strengths include Python, data science, data engineering, analytics, AI, and MLOps, along with experience in REST API development and cloud platforms like GCP and AWS. I hold an AWS Machine Learning and AI certification, equipping me to deliver seamless, scalable, data-driven solutions that address practical challenges. Additionally, I have a very good command of tools like Docker, MLflow, and Streamlit for streamlined application development and user-friendly interfaces.
In each project I take on, I make sure to follow up-to-date practices in software engineering and DevOps, like CI/CD, code linting, formatting, model monitoring, experiment tracking, and robust error handling. I’m committed to delivering complete solutions, turning data insights into practical strategies that help businesses grow and make the most out of data science, machine learning, and AI.
– Acquired skills in data collection, preprocessing, EDA, A/B testing, ML, DL, NLP, RNN, CNN, and computer vision
– Led a deep learning computer vision classification project using TensorFlow, Docker, FastAPI, Streamlit, and GCP
Core Experience:
– Successfully developed 30+ end-to-end ML projects, encompassing data cleaning, analytics, modeling, and deployment on AWS and GCP, delivering impactful and scalable solutions
– Built interactive and scalable web applications using Streamlit and Gradio for diverse industry use cases
– Taught and mentored 50+ students in data science and analytics, fostering their professional growth through personalized learning approaches
– Designed course content for retrieval-augmented generation (RAG) applications tailored to enterprise requirements
– Authored high-impact technical blogs on platforms like Medium, Zilliz, Marvelous MLOps, and Artifex, covering topics like MLOps, vector databases, and LLMs, achieving significant engagement
Tech Stack:
– Data Science, Analytics & ML: Python, TensorFlow, Scikit-learn
– AI: Langchain, LlamaIndex, Hugging Face, Transformers, Vector Databases
– Domains: Regression, Classification, NLP, LLM, RAG, Computer Vision, Neural Networks, Ensemble Methods, Clustering, Dimensionality Reduction
– Data Engineering: dbt, Terraform, SQL, BigQuery, PySpark, Databricks
– MLOps: MLflow, Prefect, Comet, Docker, Kubernetes
– APIs: Flask, FastAPI
– Cloud Platforms: GCP, AWS
– Apps: Streamlit, Gradio
– Version Control: Git
Open-Source Contributions:
– Qdrant: Supported advanced vector database projects through technical writing and project development
Clients: Le Wagon, Towards AI, Zilliz, Artifex, Apprentus (private students), Qdrant, Upwork (private clients)
Volunteering: DeepLearning.AI (course tester), GenAI Zurich 2024 (event volunteer)
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