I'm Karim Osman, a passionate Machine Learning Engineer, Data Engineer, Data Scientist, and DevOps Specialist. I leverage the power of AI and data-driven solutions to tackle complex real-world challenges. My expertise encompasses the entire machine learning lifecycle—from model development to deployment—while ensuring robust data engineering and cloud infrastructure support.

• Current Focus: Developing state-of-the-art deep learning models for Natural Language Processing (NLP) and computer vision applications.
• Continuous Learning: Engaging with advanced topics such as reinforcement learning and the latest advancements in AI technologies.
• Collaborative Spirit: Open to partnerships on projects that intersect machine learning, AI, and data engineering.

• Frameworks & Libraries:
  • Machine Learning: scikit-learn, XGBoost, LightGBM, CatBoost
  • Deep Learning: TensorFlow, Keras, PyTorch, MXNet, Caffe
  • Data Science: Pandas, NumPy, SciPy, Statsmodels
  • Deep Learning Models:
    • NLP: BERT, GPT-3, LSTM, RNN, CNN
    • Computer Vision: U-Net, ResNet, VGG16, EfficientNet, YOLO
    • Generative Models: GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders)
  • Data Visualization: Matplotlib, Seaborn, Plotly, Bokeh

• Big Data Tools: Hadoop, Spark, Hive, Flink
• Cloud Platforms: AWS (SageMaker, EC2, Lambda), GCP (AI Platform, BigQuery), Azure
• MLOps & Deployment: Docker, Kubernetes, CI/CD, MLflow, Airflow, Kubeflow
• Databases: PostgreSQL, MySQL, MongoDB, Redis, Cassandra

• NLP with Transformers:
  Developed advanced text classification models utilizing BERT, fine-tuning them on custom datasets for sentiment analysis and topic classification.

• Time Series Forecasting:
  Designed robust forecasting models employing LSTM, ARIMA, and Prophet to predict stock prices, with comprehensive visualization and accuracy metrics.

• End-to-End Machine Learning Pipeline on AWS:
  Engineered a scalable machine learning pipeline for customer churn prediction, utilizing AWS services, Docker, and CI/CD methodologies for efficient deployment.

• Real-Time Data Pipeline with Apache Kafka & Spark:
  Architected a high-performance ETL pipeline for processing streaming log data using Apache Kafka and Spark, featuring real-time analytics dashboards.

I am eager to engage in meaningful conversations about innovative projects, ideas, and opportunities. Feel free to connect with me through the following platforms:

I love exploring the intersection of technology and art. When I'm not coding, you might find me experimenting with creative projects or participating in hackathons! 🚀