I'm currently immersed in the realm of Machine Learning Engineering, utilizing Python and various frameworks like SciketLearn, and TensorFlow. I'm eager to collaborate on projects focused on deploying machine learning models in production environments. Currently, I'm seeking guidance on optimizing model performance and implementing advanced machine learning algorithms. I'm delving into the intricacies of designing algorithms to preprocess and clean large datasets efficiently. Feel free to reach out to me for discussions on machine learning model architectures, deployment strategies, or any aspect of AI development. On a lighter note, I'm an avid gamer and have a knack for creating visually appealing graphics.
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Sure, here's a categorized list of tools commonly used by ML engineers:
Programming Languages:
- Python
Machine Learning Frameworks:
- TensorFlow
- Scikit-learn
Development Environment:
- Jupyter Notebook
Data Manipulation and Analysis:
- Pandas
- NumPy
Containerization and Orchestration:
- Docker
Version Control:
- Git
Cloud Platforms:
- AWS
- Azure
- Google Cloud Platform
Big Data Processing:
- Apache Spark
Visualization:
- Matplotlib
- Seaborn
Web Frameworks:
- Flask
- FastAPI
Machine Learning Lifecycle Management:
- MLflow