Building Robust Production-Ready Deep Learning Vision Models in Minutes

Thanks to faster compute, better storage and easy to use software, deep learning based solutions are definitely seeing the light of the day coming out from the proof-of-concept tunnel into the real-world! We are seeing widespread adoption of deep learning models across diverse domains in the industry including healthcare, finance, retail, tech, logistics, food-tech, agriculture amongst many others! Considering the fact that deep learning models are resource hungry and often compute-heavy, we need to pause for a moment and think about model inference and serving times, when consumed by end-users. Training and performing model inference on static batches of data while prototyping is necessary. However this methodology and code artifacts don't make the cut when we want our model to be consumed in the form of a web service or API.