I will build and deploy deep learning models for production use
About this Gig
I will build, train, and deploy custom neural networks using machine learning and deep learning models using Python, TensorFlow, Pytorch, Sklearn etc. From data preprocessing to model optimization and production deployment, I deliver accurate, scalable, and well-documented ML solutions. Ideal for AI for science predictive analytics, automation, and AI-driven applications.
FAQ
What do you need to get started?
I need your dataset (CSV, Excel, etc.), a clear description of your problem, and your goals (e.g., classification, regression, prediction). If you don't have data yet, I can help source or simulate it.
Which tools and libraries do you use?
I use Python with libraries such as scikit-learn, XGBoost, pandas, NumPy, TensorFlow, Keras, and Matplotlib. I can also work with PMML for model portability.
Can you work with engineering or simulation data?
Yes! I specialize in using machine learning for engineering, simulation, and materials data (e.g., FEA/CFD results, materials properties, logs files etc.).
Will you provide the source code?
Yes. All packages include the source code. You’ll get clean, well-documented Python scripts or Jupyter notebooks.
Can you deploy the model to the cloud or as an API?
Yes, in the Premium package or as an extra. I can deploy models using Flask, FastAPI, or cloud platforms like Heroku or Render.

