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When it comes to modern software development, most of us have started expecting that new and improved software features can appear incrementally on almost any given day. This applies to the consumer applications software applications running on the web, mobile along with enterprise software.
Continuous Delivery comes in the picture as a set of principles that are aimed at enhancing the throughput of delivering software to production, all in a very secure and trustworthy manner. With more and more organizations moving to data-driven or AI-driven ideologies, it is becoming increasingly important to include data science and data engineering approaches into the software development process for avoiding problems that hinder collaboration and alignment.
With that being said, this integration brings with itself some challenges such as a higher number of transforming artifacts, size and portability issues, different skills and working process problems in the workforce, etc.
Continuous Delivery for machine learning is the technical approach that is followed to solve these challenges while bringing together groups in order to develop, deliver, and improve the machine learning applications.
Let us now understand the meaning of Continuous Delivery for machine learning.
In order to understand the meaning of continuous delivery for machine learning, it is first important to get the meaning of continuous learning and the place where its principles originated from. Continuous Delivery is a fundamentally a software engineering approach in which the teams produce the software in short life cycles while ensuring that the software can be reliably released in any given time.
It creates a repeatable and reliable process for releasing software, automating almost everything, and building high quality. In addition, continuous delivery is the capability to get changes of all types involving the production, new features, experiments, etc, into the production process or in the hands of users in a safe manner.
Any changes to the machine learning models are just a type of change that requires management and released into production. Other than code, it needs a CD toolset to be extended so it can easily handle new artifacts.
Developing an effective Continuous Delivery approach allows overcoming challenges so that they are applicable to machine learning applications while calling this continuous delivery for machine learning. This approach also speeds up the process of the continuous intelligence cycle.
Continuous delivery for machine learning is a software engineering approach that involves a cross-functional team producing applications based on code, data, models in small and safe increments that can be reproduced and reliably released at any given time and in short cycles.
The important principles of continuous delivery for machine learning involved in this definition are:
In conclusion, it must be noted that continuous delivery for machine learning is an extremely effective process of moving the development of applications from proof-of-concept programming to professional state-of-the-art software engineering.