As we continue to see the benefits of Artificial Intelligence (AI), Machine Learning (ML) remains challenging for many businesses due to its complexity and time-consuming nature. It is therefore important to understand how open-source approaches and the use of interoperability tooling can help create a more accessible and cost-effective way to run ML algorithms.
Computer Weekly, a technological news outlet, released an article about this ongoing problem with a guest post from Chief Innovation Officer at Civo, Josh Mesout. In this piece, Mesout spoke about how open source tooling can be easily adjusted for specific cases, reducing complexity in extracting insights from ML:
The demanding nature of running ML is bringing open source approaches to the fore to start cutting down on this complexity and reducing barriers to entry. For smaller firms, it delivers a cost-effective and resource-efficient way of running ML algorithms. Indeed, many businesses simply do not have the time to invest two months getting up to speed with platforms like AWS SageMaker before accessing ML insights.
Establishing a developer community can help keep lines of communication open, leading to greater engagement and collaboration. Recognizing and rewarding community members for their efforts and setting ground rules for conduct are crucial in sustaining a thriving open source cloud ecosystem that prioritizes developer welfare.
Mesout spoke about the importance of building communities by outlining the following:
If we are to drive continuous adoption and accessibility of ML, we need to work together as a community to build a thriving open source cloud ecosystem… By empowering the developer community with the open source tooling they need, anything is possible.
Click here to learn more about machine learning and words from Chief Innovation Officer Josh Mesout and the full release from Computer Weekly here.