What is the best way to choose a machine learning framework for your software engineering project?

The most important step in any project, be it related to machine learning or not, is to identify and understand the problem to be solved. Using any ml model or framework that you "like" without understanding the problem would be like slicing fruits using a sword, because why not. If you are working on a problem that emphasizes on explainability of the model, use one that can help you with that. Linear models can be a great choice, using deep layer neural networks to predict temperature in your project is like using a chainsaw for slicing.

as a rule of thumb, identifying your problem and how you would like to solve. that way, you can then pick a model that will work with your data and solve the problem thereof.

This is good but I suggest even before diving into ML jargon "what kind of machine learning task..." you start with the outcome...you indicate that below but I would start there. Maybe even reference "Start With Why" by Simon Sinek.

The subsequent step involves exploring and comparing the features and capabilities of various machine learning frameworks. Numerous frameworks exist, each with its unique strengths and weaknesses. Prominent ones include TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost. Utilizing online resources like documentation, tutorials, blogs, and forums can provide deeper insights into each framework and their practical applications. Looking at examples and case studies of similar projects that employ these frameworks can also be beneficial.

The third step involves assessing the performance and usability of the potential frameworks. Performance pertains to the speed and efficiency of the framework in processing your data and executing your machine learning models. Usability relates to the ease and intuitiveness of using and customizing the framework. The performance and usability of the frameworks can be tested by conducting experiments on your data or a sample dataset. Additionally, the code quality, documentation, and community support of the frameworks can be examined to determine their maintenance and updates.

The fourth step involves contemplating the compatibility and scalability of the potential frameworks. Compatibility pertains to the framework’s ability to integrate seamlessly with your existing software engineering tools and platforms, such as programming languages, libraries, databases, and cloud services. Scalability refers to the framework’s capacity to manage large and complex data and models, as well as distributed and parallel computing. The compatibility and scalability of the frameworks can be evaluated by examining their requirements, dependencies, and features. Additionally, feedback and reviews from other users who have utilized the frameworks in similar scenarios can be beneficial.

The ultimate step is to select the framework that best suits your project, based on your assessment and comparison of the frameworks. Each project has unique requirements and constraints, so there isn’t a universal solution for machine learning frameworks. However, by adhering to the outlined steps and criteria, you can make a more informed decision that will assist you in accomplishing your machine learning objectives.