Welcome to the Mlflow-TensorFlow-Guide project, created and maintained by Kailash Parshad, an Ethical Hacker, Penetration Tester, and Cybersecurity Enthusiast.
- Python 3.10.11: Python 3.10.11
- Anaconda: Anaconda
- VScode: VSCODE
This project is a TensorFlow and MLflow demonstration, showcasing the process of building, training, and evaluating a machine learning model using TensorFlow. It provides a step-by-step guide on how to set up the environment, load data, preprocess it, build and compile a model, and evaluate its performance.
- GitHub Repository: Mlflow-TensorFlow-Guide
- LinkedIn: Kailash Parshad's LinkedIn Profile
To get started with this project, follow these steps:
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Create a Conda Environment:
- Create a Conda environment to isolate the project dependencies.
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Activate the Environment:
- Activate the newly created Conda environment.
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Install Jupyter Notebook:
- Install Jupyter Notebook to run the provided Jupyter Notebook.
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Install Dependencies:
- Install project dependencies using pip.
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Tensorflow Model Building
- Follow the Guide pdf
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Recording Tensorflow model with MLflow
- Follow the Guide pdf
The project is structured as follows:
- Notebook: The main notebook for running the code is available here.
In the notebook, the following key steps are demonstrated:
- Import necessary libraries, including TensorFlow, MLflow, and more.
- Load and preprocess fashion MNIST dataset.
- Build a neural network model using TensorFlow.
- Compile the model with loss and optimizer.
- Train the model with training and validation data.
The model's performance is evaluated on the test dataset, and accuracy metrics are reported.
You can use this project as a guide to working with TensorFlow and MLflow for building and evaluating machine learning models.
- TensorFlow: Official TensorFlow Website
- MLflow: Official MLflow Website
Thank you for exploring the Mlflow-TensorFlow-Guide project. If you have any questions or suggestions, feel free to reach out to Kailash Parshad.