Artificial Intelligence (AI) and machine learning (ML) technologies extend the capabilities of software applications that are now found throughout our daily life: digital assistants, facial recognition, photo captioning, banking services, and product recommendations. The difficult part about integrating AI or ML into an application is not the technology, or the math, or the science or the algorithms. The challenge is getting the model deployed into a production environment and keeping it operational and supportable.
Software development teams know how to deliver business applications and cloud services. AI/ML teams know how to develop models that can transform a business. But when it comes to putting the two together to implement an application pipeline specific to AI/ML — to automate it and wrap it around good deployment practices — the process needs some effort to be successful.
Read about it in the Azure blog.