Our world moves at a rapid pace and with every turn of our planet, the data we collect and use to keep up this pace grows. For enterprises to avoid drowning in data-quicksand, it is essential that they scale their AI capabilities to unearth the insights within the data. A majority of enterprises are stuck in the experimentation phase, and with the proliferation of cloud technologies there is no room for inefficiency.1 While there has been some improvement over recent years, the latest State of AI report by Deloitte has found that only 27% of surveyed businesses were Transformers and had achieved substantial AI deployments at scale.2 So, the question arises; how can enterprises shift towards this industrialised and repeatable AI process at scale and reap the benefits of this incredible technology?
Learning from history
As they say, necessity is the mother of invention. Travel a decade back in history, and you would find that the entire software industry was going through a similar scaling challenge. Time taken to deploy new software features at scale was quite high due to the siloed way of working between developers and the operations community. The entire software development process was divided into multiple independent links and executed by different disparate teams. Fast forward and many high-tech enterprises are now churning out production code every minute at scale with the highest levels of reliability; all thanks to the movement called ‘DevOps’.
But what exactly is DevOps?
Under a DevOps model, the development team and operations team work together towards a common software development goal. This team works with a set of guiding principles focused on systems thinking, enhanced feedback loops and inculcating a culture of continuous experimentation and learning. The outcome is shortening the cycle time of code development to deployment in production environments ensuring high quality.
So how can we apply a similar model to the development and deployment of AI?
Introducing Machine Learning Operations (MLOps)
The recent and rapid innovation of cloud-based technologies has provided a plethora of tool sets available to analyse, process and model data. There is also an increasingly skilled and talented workforce of machine learning and data engineers. What’s missing? A framework to bind the technologies and resources so that enterprises can efficiently deliver AI at scale.
Designed with similar principles to DevOps; MLOps focuses on standardisation, optimisation, and automation of ML model deployment activities to achieve scale. The goal of MLOps is to smoothen the process of getting an ML model from an ideation stage to production in the shortest possible time, with minimal risk. It is a methodology that unifies ML development and ML operations. The focus of MLOps is to promote:
Key components of MLOps
Some of the key components of establishing MLOps across the ML lifecycle are highlighted below:
What’s the conclusion?
There is no doubt that embracing MLOps helps to increase the productivity, speed, and reliability of the ML lifecycle while also reducing risk to the enterprise. For MLOps to successfully contribute to the scaling of AI across the business, enterprises need to keep in mind that MLOps requires a multi-disciplinary eco-system, hence the right mix of skillsets should be brought in across the enterprise to lay the foundation for MLOps. Scaling AI is not just for data scientists anymore, it’s a team game and everyone has a role to play.