TOOLS AND TECHNOLOGIES
The following list encapsulates a comprehensive array of tools and technologies that I have extensively engaged with across both professional and personal projects. This collection includes, but is not limited to, various frameworks and platforms that are pivotal in the field of artificial intelligence and data science. My experience spans a wide spectrum of applications, ensuring a robust understanding and practical knowledge of each technology. These tools have been integral in the development and execution of my projects, underpinning my capabilities in delivering complex solutions effectively.
Programming Languages
Python – Essential for its libraries like TensorFlow and PyTorch.
R – For statistical analysis in AI contexts.
Scala – Often used with Apache Spark for big data processing.
Data Handling and Processing
NumPy & Pandas – Fundamental for data manipulation in Python.
Apache Kafka – For handling real-time data streams in AI applications.
Framework
TensorFlow & Keras – For deep learning and neural networks.
Scikit-Learn – For traditional machine learning algorithms.
PyTorch & FastAI – Ideal for dynamic neural networks and rapid prototyping.
Hugging Face Transformers – Crucial for implementing pre-trained LLMs and fine-tuning.
Development and Deployment Tools
FastAPI – A modern web framework for building APIs with Python 3.7+, known for its high performance and ease of use, supporting asynchronous request handling
Kubernetes – Orchestration of containerized applications, essential for scaling LLM deployments.
Docker – Containerization of applications.
MLflow – Lifecycle management from experimentation to production.
Apache Airflow – Orchestrates complex computational workflows and data processing pipelines, ensuring automation and management of the AI lifecycle.
Kubeflow – – Streamlines deployment of machine learning workflows on Kubernetes, facilitating both development and production pipelines.
Cloud Platforms and Infrastructure Management
AWS, Azure, Google Cloud – For hosting, scaling, and managing AI applications.
Version Control and CI/CD
Github – Source code management.
GitHub Actions, Jenkins – For CI/CD, automating testing and deployment pipelines.
Visualization and Reporting
Tableau, Power BI – For data visualization and business intelligence insights.
Matplotlib/Seaborn – Python libraries for creating detailed visualizations.