I'm exploring options to integrate deep learning into our business and came across Techstack's Deep Learning Development services. Could you explain what a typical development workflow looks like when working with them? Specifically, I'm interested in how data is handled, how models are designed and trained, and what happens during deployment. Also, how does Techstack support ongoing optimization after a solution has launched?

Techstack’s deep learning development process tech-stack.com/services/deep-learning-development is designed to guide clients from idea to full implementation with a strong focus on business value. It begins with a discovery phase where they assess your business objectives, existing data, and infrastructure. This allows them to define a strategy tailored to your goals.

Once the scope is clear, the team focuses on data preparation. They evaluate the quality of your datasets, perform cleaning and preprocessing, and engineer features that best support the model’s goals. This step is crucial, as the success of any deep learning system relies heavily on well-structured input data.

In the model development stage, Techstack designs and trains custom neural networks based on your specific use case. The training process includes frequent validation to ensure the model generalizes well and performs reliably.