Dawid is available for hire
Hire DawidDawid Smoleń
Verified Expert in Engineering
Machine Learning Engineer and Software Developer
Location
Kraków, Poland
Toptal Member Since
September 15, 2021
Dawid has delivered more than 30 successful projects in the data science and machine learning areas. He worked with both classical and deep learning solutions in a few industries. He is focused on creating systems regarding MLOps best practices and design patterns. Having experience with many cloud providers, he is able to automate the whole ML process from data gathering to automated deployments and continuous training.
Portfolio
Freelance
Python, Scikit-learn, Docker, Amazon Web Services (AWS)...
Sinch
Kubernetes, Google Kubernetes Engine (GKE), Docker, Helm, GitOps...
Grape Up
Azure, Convolutional Neural Networks (CNN), Deep Learning, Dataiku, Python...
Experience
Availability
Part-time
Preferred Environment
Ubuntu, Azure, Amazon Web Services (AWS), Google Cloud, Python, Scikit-learn, PyTorch, TensorFlow
The most amazing...
...things I've created are reliable systems not only in terms of data modeling methodology but also in implementing the best MLOps design patterns.
Work Experience
ML Consultant | MLOps Engineer
2018 - PRESENT
Freelance
- Acted as a data science trainer for two training companies and conducted training for around seven teams from various enterprises.
- Deployed modeling services to Kubernetes clusters, Amazon EKS and Google Kubernetes Engine (GKE).
- Introduced tracking servers to the existing projects to improve the observability of a model and understanding of a problem.
- Developed an end-to-end solution from data investigation to a deployed model that monitors daily statistics and business metrics regarding user experience in eCommerce.
- Consulted an ECG-related company from Latin America. Helped with the design and implementation of crucial Holter analysis steps.
- Prepared NFT market analysis tools based on machine learning traits valuation.
- Prepared deduplication service for real-estate website scraper.
Technologies: Python, Scikit-learn, Docker, Amazon Web Services (AWS), Digital Signal Processing, ECG, Training, Data Science, Jupyter Notebook, Artificial Intelligence (AI), Machine Learning, Regression Modeling, Classification Algorithms, Kubeflow, Kubernetes, CI/CD Pipelines, Machine Learning Operations (MLOps), Data Scraping, Data Engineering, Front-end, Data Analysis, Non-fungible Tokens (NFT)
MLOps Engineer
2022 - 2023
Sinch
- Managed thousands of models in production. Maintained them and also significantly optimized the costs and speed.
- Added a lot of observability tools on many levels.
- Worked with the hottest tech, including GitOps and event-based architecture.
- MIgrated massive projects between popular cloud providers.
Technologies: Kubernetes, Google Kubernetes Engine (GKE), Docker, Helm, GitOps, Large-scale Projects, Artificial Intelligence (AI), Data Engineering, Python
Machine Learning Engineer
2020 - 2021
Grape Up
- Developed an end-to-end deep learning automotive project together with full automation (CI, CD, and CT) and infrastructure.
- Created POCs and demos in machine learning and data science areas together with simple UI demos and API first approach.
- Contributed to the company's entry into the AI market, working on papers, blog posts, offers, and creating POCs.
- Worked on machine learning best practices using modern tools and solutions.
Technologies: Azure, Convolutional Neural Networks (CNN), Deep Learning, Dataiku, Python, Digital Signal Processing, DevOps, Machine Learning Operations (MLOps), REST APIs, React, Deep Neural Networks, Metaflow, Data Science, Jupyter Notebook, Artificial Intelligence (AI), Predictive Modeling, Machine Learning, Amazon Web Services (AWS), Data Scraping
Deep Learning Engineer
2017 - 2018
Lekta
- Created a library for users' intent classification that employs industry best practices to make predictions millions of times a month in a real-time, demanding environment.
- Developed a novel speech recognition system based on state-of-the-art papers that beat the current market in some areas in terms of accuracy or performance.
- Researched numerous topics in the areas of speech recognition, voice-based gender recognition, intent classification, sentence representation, and text representation.
- Developed machine learning algorithms for both voice bots and chatbots.
Technologies: C++, Audio Processing, Digital Signal Processing, Python, PyTorch, TensorFlow, Deep Learning, Speech Recognition, GPT, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Scikit-learn, Data Science, Jupyter Notebook, Chatbots, Artificial Intelligence (AI), Predictive Modeling, Amazon Web Services (AWS), Machine Learning, Data Scraping, Data Engineering
Machine Learning Engineer
2016 - 2017
Aspel SA
- Created a brand new QRS detector tested on many benchmarks and real-world monitoring tests.
- Developed clustering algorithms that can efficiently cluster long Holter monitor tests, focusing on user experience.
- Developed embedded resampling algorithms for ECG devices.
- Contributed to QRS morphology classifiers that highly improved the work of doctors and met AMA standards.
- Helped develop user experience-related algorithms that simplify the work of the doctors and technicians.
Technologies: C++, Python, MATLAB, Scikit-learn, SciPy, Artificial Intelligence (AI), Predictive Modeling, Classification Algorithms, Regression Modeling, Data Analysis
NLP Engineer
2015 - 2016
WitKom – Virtual Translator of Sign Communication
- Developed the first Polish to Polish Sign Language translation system on the language level.
- Built the first Polish Sign Language to Polish translation system on the language level using Seq2Seq models.
- Created huge artificial datasets for sign languages based on heuristics, rules, and DL technology.
Technologies: Python, Natural Language Toolkit (NLTK), Deep Learning, Sequence Models, TensorFlow, Data Analysis
Experience
Gomrade — Play Go Against AI on a Real, Physical Board
https://github.com/smolendawid/GomradeThis repository allows you to play Go with strong AI on a real board. Gomrade analyses the board state from an image using a computer camera and answers the AI moves using a synthesized voice. The example video of Gomrade in action is under development.
Speech Representation and Exploration Notebook
https://www.kaggle.com/davids1992/speech-representation-and-data-explorationThis is one of the top 15 Kaggle notebooks ever, with more than 100,000 views. I introduced a few basic concepts about speech representation and performed data analysis looking for the most interesting examples from the dataset.
The Simplest Python Cache for Data Scientists
https://github.com/smolendawid/cachaThe simplest Python cache for data scientists.
Contrary to many other tools, cacha boasts the following features:
• It is used at the function call, not the definition. Many packages implement the @cache decorator that has to be used before the definition of a function that is not easy enough to use.
• It stores the cache on disk, which means you can use the cache between runs. This is convenient in data science work.
Contrary to many other tools, cacha boasts the following features:
• It is used at the function call, not the definition. Many packages implement the @cache decorator that has to be used before the definition of a function that is not easy enough to use.
• It stores the cache on disk, which means you can use the cache between runs. This is convenient in data science work.
Drifting – The Most Flexible Drift Detection Server
https://github.com/sign-ai/driftingThe most flexible Drift Detection framework for everyone! Python-first, API-first, user-friendly, and open-source!
PYTHON-FIRST
Communicate with the Drift Detection server using a super simple Python client. No additional management needed!
EASY INTEGRATIONS
Using drifting is simple thanks to standardized, ML server-based integrations like Kafka, OpenAPI, and gRPC.
FLEXIBLE
One server for managing many models, projects, versions, and features without any further tools.
STATE-OF-THE-ART
An open-source project built upon the top-tier libraries—Alibi Detect, ML server, and more!
PYTHON-FIRST
Communicate with the Drift Detection server using a super simple Python client. No additional management needed!
EASY INTEGRATIONS
Using drifting is simple thanks to standardized, ML server-based integrations like Kafka, OpenAPI, and gRPC.
FLEXIBLE
One server for managing many models, projects, versions, and features without any further tools.
STATE-OF-THE-ART
An open-source project built upon the top-tier libraries—Alibi Detect, ML server, and more!
Education
2019 - 2021
PhD in Electrical and Electronics Engineering
AGH University of Science and Technology - Cracow, Poland
2011 - 2016
Master's Degree in Acoustical Engineering
AGH University of Science and Technology - Cracow, Poland
Certifications
AUGUST 2021 - PRESENT
ML Practitioner
Dataiku
AUGUST 2021 - PRESENT
Core Designer
Dataiku
SEPTEMBER 2017 - PRESENT
Machine Learning
Coursera
SEPTEMBER 2017 - PRESENT
Neural Networks and Deep Learning
Coursera
SEPTEMBER 2017 - PRESENT
Structuring Machine Learning Projects
Coursera
SEPTEMBER 2017 - PRESENT
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Coursera
Skills
Languages
Python, C++
Libraries/APIs
Scikit-learn, PyTorch, TensorFlow, REST APIs, Node.js, React, OpenCV, Keras, SciPy, Natural Language Toolkit (NLTK)
Paradigms
Data Science, Continuous Integration (CI), DevOps
Other
Machine Learning Automation, Audio Processing, Natural Language Processing (NLP), Deep Neural Networks, Deep Learning, Machine Learning, Sequence Models, Machine Learning Operations (MLOps), ECG, Artificial Intelligence (AI), CI/CD Pipelines, GPT, Generative Pre-trained Transformers (GPT), Data Scraping, Data Engineering, Data Analysis, Acoustics, Digital Signal Processing, Speech Recognition, Convolutional Neural Networks (CNN), Training, Chatbots, Predictive Modeling, Regression Modeling, Classification Algorithms, GitOps, Large-scale Projects, Front-end, Non-fungible Tokens (NFT), Acoustical Engineering
Tools
Google Kubernetes Engine (GKE), MATLAB, Helm
Platforms
Azure, Kubeflow, Jupyter Notebook, Ubuntu, Dataiku, Docker, Amazon Web Services (AWS), Kubernetes
Storage
Google Cloud
Frameworks
Metaflow
Collaboration That Works
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