As ML applications are maturing over time and becoming an indispensable component of industries for making faster and accurate decisions for critical and high-value transactions. In all the above scenarios, the expected ML response is accurate, fast, and reliable. Scalability, maintainability, and adaptability also become critical as we move towards making ML one of the main components of enterprise-level applications. Hence, designing of end to end system keeping requirements of ML becomes important. The process of defining an interface, algorithm, data infrastructure, and hardware for ML Learning system to meet specific requirements of reliability, scalability, maintainability, and adaptability.

Have you been over all of the introductory machine learning tutorials out there? Have you read all the algorithm theory you can handle? But still don't have any idea how to design a real world machine learning system? Not sure what kind of software architecture is useful? And even if you did, would you still have virtually no idea how to deploy and maintain it afterwards?

Engineers strive to remove barriers that block innovation in all aspects of software engineering. Currently, in addition to deploying technology products, there is an amalgamation of technology and data models or just deploying a plethora of AI models. In this article, we will cover the horizontal approach of serving data science models from an architectural perspective. DevOps emerged when agile software engineering matured around 2009. Today, as data science products mature, ML Ops is emerging as a counterpart to traditional devops.

There is a lot of talk about the fourth industrial revolution centered around AI. If we are at the start of the fourth industrial we also have the unusual honour of being the first to name our revolution before it's occurred. The technology that has driven the revolution in AI is machine learning. And when it comes to capitalising on the new generation of deployed machine learning solutions there are practical difficulties we must address. In 1987 the economist Robert Solow quipped "You can see the computer age everywehere but in the productivity statistics".