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Lecture Notes: How to build ML Models using the tooling of TensorFlow, PyTorch, Scikit-Learn

Machine learning (ML) is a powerful technique that allows us to train algorithms to make predictions or decisions based on patterns in data. However, building ML models can be a complex and time-consuming process, which is why a variety of tools and frameworks have been developed to help developers streamline the process. Three popular tools for building ML models are TensorFlow, PyTorch, and Scikit-Learn. In this lecture, we will explore how these tools work, their strengths and weaknesses, and how they can be integrated into a typical ML model development workflow.
First, let's start with TensorFlow. TensorFlow is an open-source machine learning framework that was developed by Google. It is primarily used for building neural networks, but it can also be used for other machine learning tasks. TensorFlow is designed to be highly flexible and can run on a variety of platforms, including CPUs, GPUs, and even mobile devices. It also has a large and active community, which means that there are many resources available for learning and support.

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Here's an example of using TensorFlow to build a real estate prediction engine assuming current data is available in a file called realestate.csv. This example uses a fully-connected neural network to predict housing prices
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Using Tensorflow and Keras to predict house prices using a .csv file of house data
This walkthrough shows the process of using deep learning to predict house prices using Tensorflow, Keras and a .csv file of sales data.
junwin.github.io
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Both TensorFlow and Keras can be used to predict house prices using deep learning models. Keras is a high-level neural networks API that can run on top of TensorFlow, while TensorFlow is a low-level machine learning library that can be used to build and train deep learning models.
Keras provides a simpler and more user-friendly interface for building deep learning models, while TensorFlow provides more flexibility and control over the model architecture and training process.
Keras is recommended for beginners or those who want to quickly prototype and experiment with deep learning models, while TensorFlow is recommended for advanced users or those who want to build more complex and customized models
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One of the main strengths of TensorFlow is its scalability. It can be used to build models for a wide range of applications, from small-scale projects to large-scale production systems. TensorFlow also has a high degree of flexibility, allowing developers to customize their models and optimize them for their specific use case.
However, one potential weakness of TensorFlow is that it can be quite complex to use, especially for beginners. The learning curve can be steep, and the documentation can be challenging to navigate. Additionally, while TensorFlow is highly flexible, this flexibility can also make it more challenging to use for certain tasks.
Next, let's consider PyTorch. PyTorch is an open-source machine learning library that was developed by Facebook. Like TensorFlow, it is primarily used for building neural networks, but it can also be used for other machine learning tasks. PyTorch is known for being highly user-friendly and having a more Pythonic API than TensorFlow, which can make it easier for developers to learn and use.
One of the main strengths of PyTorch is its ease of use. It has a simple and intuitive API, which can make it easier for developers to get started with building ML models. Additionally, PyTorch has excellent support for dynamic computation, which can make it easier to build models that are highly flexible and adaptable to changing data.
However, one potential weakness of PyTorch is that it may not be as scalable as TensorFlow, especially for large-scale production systems. While PyTorch has made significant strides in recent years in terms of scalability and performance, TensorFlow still has the edge in terms of support for large-scale distributed systems.
Finally, let's consider Scikit-Learn. Scikit-Learn is an open-source machine learning library that is primarily used for building traditional machine learning models (i.e., not neural networks). Scikit-Learn is designed to be highly user-friendly and has a simple and intuitive API that makes it easy for developers to get started with building ML models.
One of the main strengths of Scikit-Learn is its ease of use. It has a simple and consistent API that makes it easy to build and evaluate models. Additionally, Scikit-Learn has excellent support for data preprocessing and feature engineering, which can make it easier to get data into a format that is suitable for training ML models.
However, one potential weakness of Scikit-Learn is that it may not be as flexible or powerful as TensorFlow or PyTorch, especially for tasks that require building complex models or handling large amounts of data. Additionally, Scikit-Learn is not designed to handle unstructured data (such as images or text), which means that it may not be suitable for certain types of machine learning tasks.

Now that we have discussed how TensorFlow, PyTorch, and Scikit-Learn work, and their strengths and weaknesses, let's consider how these tools can be integrated into a typical ML model development workflow.
One common workflow for developing ML models is the CI/CD (continuous integration and continuous deployment) process. This process involves several steps, including data preparation, model development and training, model testing and evaluation, and deployment to production.
In the data preparation step, developers use tools like Pandas or NumPy to clean and preprocess data, and Scikit-Learn to perform feature engineering. Once the data is ready, developers use TensorFlow or PyTorch to build and train their models. During the model development stage, developers can use tools like TensorBoard (for TensorFlow) or PyTorch Lightning (for PyTorch) to monitor and visualize their models' performance.
Once the model is trained, developers can use Scikit-Learn or other evaluation tools to test and evaluate the model's performance on test data. This step is crucial for ensuring that the model is accurate and reliable before it is deployed to production.
Finally, once the model is ready for deployment, developers can use tools like TensorFlow Serving or PyTorch Serve to deploy their models to production systems. These tools provide a simple and scalable way to deploy ML models in a production environment.
Overall, integrating these tools into a CI/CD workflow can help developers streamline the ML model development process and ensure that their models are accurate, reliable, and scalable. Some best practices for using these tools effectively include keeping the workflow modular and using version control to track changes to the model code over time. Additionally, developers should be sure to use appropriate testing and evaluation metrics to ensure that their models are performing as expected.
In conclusion, TensorFlow, PyTorch, and Scikit-Learn are powerful tools for building ML models, each with their strengths and weaknesses. By integrating these tools into a CI/CD workflow, developers can streamline the ML model development process and ensure that their models are accurate, reliable, and scalable.
Here's an example of using TensorFlow to build a real estate prediction engine assuming current data is available in a file called realestate.csv. This example uses a fully-connected neural network to predict housing prices1.
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