Open source has been one of the key drivers of the fast development and innovation of Data Science since its early stages. The community quickly adapted the usage of open source projects including frameworks, data services, automation technologies, and public data sets. They utilised these sets of technologies to exchange insights and build upon a community knowledge to fuel collaboration, innovation, and adoption. Data Scientists require an environment that meets their needs. This environment should evolve with the rapidly changing field of Data Science. Additionally, it should incorporate the fundamental principles of failing fast and cheap.
Having a platform for open source Data Science is crucial, as it allows individuals and organisations to leverage the power of the open source community and collaborate on data science projects. A platform can provide access to a wide range of tools and resources, allow collaboration, and streamline the process of data analysis and modeling. It could also be a centralised source for sharing insights and knowledge, leading to more efficient and effective decision-making.
We will reflect on how the collaborative and inspiring nature of open source shaped the way Data Science has evolved over the past. Teams joined forces across all industries to solve common problems collaboratively.
Let us have a look on the importance of sharing knowledge, accessibility of technologies, and education in shaping and influencing the future of Data Science. With humanity, sustainability, and inter-cultural applicability as its foundation, we can contribute our diverse ideas to a mutual purpose.
As a Junior Solutions Architect at Red Hat, I design optimal IT solutions for clients using Red Hat's products and technologies. Alongside my passion for Open Source Technologies, my Master's Degree in Data Science reflected my interest in MRI image segmentation and biomedical topics. Having worked across diverse technological fields, I am driven to use my skills for positive impact.
After her studies in computer science with a thesis in image recognition, Miriam started working as consultant with customers across industries and sectors. As part of innovative green and brownfield projects, she focused on microservice architectures, data-driven applications, DevOps GitOps MLOps and container platforms, while pursuing an advanced educational path on Applied IT Security. Sometimes above, sometimes below the Kubernetes or Cloud API, driven by a passion for Open Source technologies and culture, Miriam proceeded working directly with customers as Platform - and Solutions Architect.
As respected thought leader, and staunch advocate for the bridging of technology and business priorities, Miriam is now leading Solution Architecture teams at Red Hat towards customer success and innovation - the open way.
