Why Do Kubernetes and Containers Go Hand in Hand with Machine Learning?As an increasing number of enterprises ride the wave of digitization that has swept the current IT landscape, an amalgamation of modern technologies such as Machine Learning (ML) and Artificial Intelligence (AI) has grown rampant within organizations.
As the technologies that form the framework for an enterprise’s complex IT infrastructure grow increasingly sophisticated, the inclusion of a cloud-native environment and the use of containers within that environment are becoming a matter of routine.
Fortunately for enterprise owners, Kubernetes, and the overall phenomenon of effective container deployment goes hand in hand with the ML tech and can be introduced to the cloud-native model, which in turn offers multiple benefits, including the implementation of effective business strategies, and fostering security.
When it comes to ML technology, the applications of the technology are numerous and highly varied – with everything from simple detection of fraud and cybercrime, tailor-made customer experience to an operation as sophisticated as a supply chain optimization, which makes clear the lucrativeness of ML.
Furthermore, the multitude of advantages offered by ML are further evident by Gartner’s predictions, which state that a staggering number of seven out of 10 of all enterprises will be relying on some form of AI by 2021.
ML, AI, and BusinessesFor businesses to utilize the most out of AI and ML and apply it to new business groups like DevOps and DevSecOps, they must have a robust IT infrastructure to rely on.
A robust IT environment is one where data scientists can experiment with diverse data sets, computing models, algorithms – without their experiments slowing other operations down, or taking a toll on the IT staff.
For the effective implementation of ML within businesses, enterprises need to figure out a way to deploy code in a repeatable manner, across both local and cloud environments, along with a connection to all the data sources that they need.
For the modern enterprise, the essential tool that helps them fulfill their goals is time, the dire need for an IT environment that supports the rapid-paced development of code.
Speaking of containers, since they speed up the process of the deployment of an enterprise’s applications by packaging code in a ‘wrapper’ along with its specific runtime requirements – these qualities also make containers the perfect candidate, and consequently, the ideal match for ML, and AI.
With that out of the way, the three phases of an AI project where the inclusion of a container-based environment can prove to be extremely lucrative include exploration, training, and deployment. These processes are explained below.
ExplorationWhen it comes to building an AI model, the norm that data scientists follow sees them experimenting with different data sets, along with various ML algorithms, to determine which data sets and algorithms to use, so that they may predict the outcomes with an increased level of efficiency and accuracy.
Typically, data scientists rely on an arsenal of libraries and frameworks for creating ML models for a variety of situations, problems across multiple industries. As data scientists try to uncover new revenue streams and work towards achieving an organization’s business goals, they also need to be equipped with the ability to run tests and speedily perform them.
Despite the nascent use of AI tech, data is already emerging, showing that organizations that enable data scientists and engineers to use containerized development have the edge over their competitors.
A report by Ottawa-based DevOps engineer Gary Stevens found that Canadian web hosting provider HostPapa outperformed other leading web hosts, thanks to its early adoption of Kubernetes.
The inclusion of containers within the exploration aspect of an AI or ML project provides data teams the liberty of packaging these libraries, according to their specific domains, deploy algorithms accordingly, along with pointing to the right data source as per their needs.
With the successful implementation of container-based programs such as Kubernetes, data scientists have access to an isolated environment; they can then customize for their exploration, without the hassle of having to manage multiple libraries and frameworks in a shared environment.
TrainingAfter the model has been devised, an AI program needs to be trained against large volumes of data, and across various platforms to maximize the accuracy of the model, and reduce any menial resource usage.
Taking into account the fact that training an AI model is a highly compute-intensive operation, containers can prove to be extremely beneficial in scaling workloads, along with making communication across the other nodes quickly. On a regular basis, however, a member of the IT team or a scheduler identifies the optimal node.
Furthermore, the use of containers also enables a modern data management plane to be exercised, which dramatically impacts and makes easier the management of data within an AI model. Additionally, data scientists also have the advantage of running their AI or ML project on several different types of hardware, such as GPUs, which in turn, enables them to adhere to the hardware platform that provides the most exceptional accuracy.
Deployment Perhaps the trickiest aspect of an AI project, the production, and deployment phase of a machine-learning application will often see the combination of multiple ML models- each of which serves different purposes.
With the inclusion of containers within the ML application, IT teams can deploy each specific model as a separate microservice. A microservice, if you were unaware, refers to an independent and lightweight program that can be reused by developers in other applications as well.
Containers not only provide a portable, isolated, and consistent environment for the rapid deployment of ML and AI models. It also has the potential to change the course of the IT landscape today, since it enables businesses to achieve their goals faster, and better.