Amr Mashlah, Artificial Intelligence (AI) Developer in London, United Kingdom
Amr Mashlah

Artificial Intelligence (AI) Developer in London, United Kingdom

Member since January 2, 2019
Amr builds machine learning services from prototype to production. His diverse ML experience helps him build rapid prototypes and find new creative uses for ML models from different domains. Amr enjoys building interactive visualization tools to validate and communicate results.
Amr is now available for hire

Portfolio

  • PatternedAI
    Artificial Intelligence (AI)
  • Eezylife Inc.
    Jupyter Notebook, SciPy, Data Visualization, Matplotlib, Scikit-learn...
  • MachineMedicine
    Jupyter Notebook, Data Visualization, Matplotlib, Git, MongoDB, SQLAlchemy...

Experience

Location

London, United Kingdom

Availability

Full-time

Preferred Environment

Jupyter, Git, NumPy, Python, Pandas, Jupyter Notebook, SQL

The most amazing...

...thing I've developed is behavioral clustering with LDA using few labeled data as seeds to influence learned clusters.

Employment

  • Founder

    2022 - PRESENT
    PatternedAI
    • Developed a customized Stable Diffusion model and served it using serverless GPUs.
    • Fine-tuned a custom image generation model and optimized it for different use cases.
    • Handled autoscaling computes necessary to serve a large volume of users and cut costs with a low number of users.
    Technologies: Artificial Intelligence (AI)
  • Senior Machine Learning Engineer

    2020 - PRESENT
    Eezylife Inc.
    • Built and maintained recommendation engines for restaurants, events, movies, and music.
    • Extracted key information that helps users relate to their recommendations.
    • Developed an interactive interpretation tool for debugging and validation.
    • Hired and managed a team of data scientists and mentored interns.
    Technologies: Jupyter Notebook, SciPy, Data Visualization, Matplotlib, Scikit-learn, Convolutional Neural Networks, Git, NLTK, SQL, SQLAlchemy, Machine Learning, Artificial Intelligence (AI), Keras, Jupyter, NumPy, Pandas, TensorFlow, PostgreSQL, Python, Topic Modeling, Computer Vision
  • Data Scientist

    2018 - 2019
    MachineMedicine
    • Used pose estimates from video recording to assess motor skills objectively for Parkinson's patients.
    • Built the analytics pipeline using Python and ingested it in a Flask web application.
    • Created plots to visualize and validate the several steps in the analytics pipeline and the activity detection algorithm.
    Technologies: Jupyter Notebook, Data Visualization, Matplotlib, Git, MongoDB, SQLAlchemy, Machine Learning, Artificial Intelligence (AI), Keras, Jupyter, NumPy, Pandas, SciPy, Scikit-learn, Python
  • Data Scientist

    2016 - 2018
    IntentHQ
    • Researched new approaches to data enrichment techniques, including behavioral clustering, audience expansion, and modeling user preferences.
    • Enhanced data quality control by creating a web interface for topic disambiguation, automating the repetitive analysis, and reports.
    • Labeled unlabeled data using probabilistic methods.
    • Devised the evaluation metrics for model performance.
    Technologies: Jupyter Notebook, Data Visualization, Matplotlib, Git, NLTK, SQL, Machine Learning, Artificial Intelligence (AI), Keras, TensorFlow, Jupyter, NumPy, Pandas, SciPy, Scikit-learn, Python

Experience

  • Semantic Search
    https://github.com/amrakm/semantic_search

    An interactive web app to perform a semantic search in a large number of documents.

    A script to embed a list of documents and upload them to a vector database. These embeddings were matched against search queries and served in a Streamlit web app.

  • ML Framework
    https://github.com/amrakm/ML_Framework

    A generic ML experiment framework to be used as a starting point and a baseline.

    Works on tabular datasets, handles numerical and categorical data automatically, and extracts embedding from text fields using BERT model.

  • DQN_Navigator
    https://github.com/amrakm/DQN_Navigator

    Using deep reinforcement learning (DQN) to navigate a 3D Unity ML environment. This is an exercise to train an RL agent using DQN to navigate a large, square world. This is a customized version of Unity ML agents.

Skills

  • Languages

    Python, Scala, SQL
  • Libraries/APIs

    Pandas, Keras, NumPy, Scikit-learn, Matplotlib, SQLAlchemy, TensorFlow, SciPy, PyTorch, TensorFlow Deep Learning Library (TFLearn), NLTK, Beautiful Soup
  • Tools

    Amazon SageMaker, Git, Jupyter
  • Paradigms

    Data Science
  • Platforms

    Jupyter Notebook, Amazon Web Services (AWS)
  • Storage

    Databases, PostgreSQL, MySQL, MongoDB
  • Other

    Data Analytics, Machine Learning, Data Cleaning, Data Handling, Machine Language, Convolutional Neural Networks, Natural Language Processing (NLP), Artificial Intelligence (AI), Data Reporting, Recommendation Systems, Data Visualization, Dashboards, APIs, Deep Learning, Topic Modeling, Generative Pre-trained Transformer 3 (GPT-3), Sentiment Analysis, Clips, OpenAI, Diffusion Models, Stable Diffusion, Algorithms, Deep Reinforcement Learning, Computer Vision, Streamlit, Vector Data, Vector Databases, Semantics, Scraping, Reinforcement Learning, BERT
  • Frameworks

    Selenium

Education

  • Master of Science Degree in Artificial Intelligence
    2015 - 2016
    University of Edinburgh - Edinburgh, United Kingdom
  • Bachelor of Engineering Degree in Mechatronics Engineering
    2007 - 2013
    University of Aleppo - Aleppo, Syria

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