Datacentre Solutions published an article by Civo Chief Innovation Officer Josh Mesout, in which he highlights the challenges and complexities of deploying machine learning (ML) projects and the significance of open source tools.

The current costs and education required to use hyperscaler machine learning platforms are prohibitive, which is why it is essential to streamline this process. By investing in and expanding the cloud native ecosystem, you can create a more disruptive and cost-effective pathway for machine learning.

Chief Innovation Officer Josh Mesout spoke about the barriers to adoption, saying:

“Developers find themselves investing enormous amounts of time managing and reconfiguring intricate components throughout their infrastructure. Consequently, ML may be inaccessible to smaller companies. ML projects can be compared to mining for diamonds, where a vast number of hours and labor are put into gaining a seemingly small but hugely valuable reward. As such, only bigger organizations are only to capitalize on the opportunities.”

From this, the rising demands in time and resources have spurred a notable shift towards open source methods. Particularly for smaller businesses, open source presents a compelling alternative by reducing complexity, lowering entry barriers, and providing a cost-effective and resource-efficient approach to executing ML algorithms.

As ML continues to shape various industries, open source solutions emerge as a catalyst for innovation and customization. The growing significance of open source empowers smaller businesses, enabling them to embrace ML with reduced barriers and access cutting-edge expertise. By leveraging open source tools, organizations can maximize their ML capabilities and tailor solutions to their specific needs.

“To promote ongoing adoption and accessibility of ML, the industry must collaborate as a community to establish a flourishing open source cloud ecosystem, beginning with interoperable tools.”

Civo’s first machine learning offering, known as Kubeflow as a Service (KFaas), seeks to address and eliminate the prevailing obstacles to ML adoption. KFaas is an open-source platform for machine learning designed to handle every phase of the process from start to finish.

Developers can readily immerse themselves in a machine learning ecosystem geared towards cloud-native applications with zero configuration required. It equips users with all the necessary resources to experiment, train, and roll out models in under an hour. Therefore, it caters to businesses of various sizes, ranging from startups to large corporations, offering clear pricing structures and robust security measures.

Josh Mesout summarized his thoughts on the future of machine learning by saying:

“Developers want user-friendly tools that don't necessitate lengthy onboarding processes to get algorithms up and running and that position them well to start reaping the benefits of AI. Easy access is a must, and they don’t want to have to continuously learn how to operate different proprietary tooling.”

Click here to learn more about machine learning and words from Chief Innovation Officer Josh Mesout and the full release from Datacentre Solutions.