What are the best practices for scaling and deploying neural networks in production environments?

In scaling and deploying neural networks, choosing the right framework is crucial. TensorFlow offers comprehensive support for distributed training and diverse deployment platforms like mobile and cloud, making it versatile for large-scale applications. PyTorch shines with its dynamic nature, facilitating rapid experimentation and Python integration, ideal for research and development. ONNX serves as a bridge, enabling model interchange among different ecosystems, thus ensuring flexibility across tools and frameworks. Your choice should align with your project's specific needs, whether that's scalability, ease of integration, or support for various deployment platforms.

In Enterprise or Operative Platform, I would not choose any more to directly use TensorFlow or PyTorch, instead I would use high-level and engine agnostic framework such as Deep Java Library. The reason for this is simple, instead of having hard dependency to one framework and needing specialists to use it, I can use abstraction framework which can be used by Java developers working with the frontline code. The added benefit of enabling developers in operative platforms to embed neutral networks directly to their code is the simplification of work. Previously a separate data team would have developed a model, and developers in operative platform would have written code to deploy it. This duplication of effort can be now avoided.

Select the appropriate framework like TensorFlow or PyTorch based on project requirements and community support. For example, TensorFlow is preferred for its scalability and deployment options, while PyTorch offers flexibility and ease of use for research-focused projects. Choosing the right framework ensures efficient development and deployment of neural network models in production.

Optimizing models involves reducing complexity without sacrificing accuracy. For instance, in image classification, pruning unnecessary connections can speed up inference while maintaining high accuracy, enhancing model performance and efficiency in production environments.

Before starting to optimise the model, one should challenge the need for it, as there are also major downsides to consider. For example quantisation reduces memory and computational demands, however it might not reduce enough for it to work in older microcontrollers and GPUs. Thus it is vital to understand what exactly is the target hardware or system where the model will be deployed. Maybe it is better to upgrade requirements than downsize the model? That said, if one goes to optimisation path, one should at least consider computational cost of the process, diminishing returns, loss of generalisation, sensitivity to hyper-parameters, overfitting and increased complexity.

Using right tools and platforms is important, but so is to maintain knowhow and knowledge of the tools and solutions used. In my experience, in order for organisations to support a technology and a solution based on it for long term, around minimum 10 people is needed for this. If an enterprise just takes whatever tools and platforms to use, they end up expanding their technology liabilities very fast and can see their development speed go down with increased maintenance burden. Thus when an organisation is starting a project to use neural networks in their products and services, they should do roadmap on where they see things naturally progressing and try to use this to limit the number of tools and platforms used.