Multi-Cloud Big Data and Machine Learning Interviews

Multi-Cloud Big Data and Machine Learning Interviews

Discover how organizations’ big data and machine learning projects benefit from the multi-cloud with ForePaaS.


What is this multi-cloud event about?


In this cloud advent, we talk to companies just like yours and discover how they reach success with their data projects on any cloud at any scale with the ForePaaS machine learning platform. How are organizations storing their data, exploiting their resources, and using machine learning algorithms on the multi-cloud?


David-Stéphane Fala, CDO at Jems Datafactory, Tuncay Isik CEO at, Laura Gouthez Financial Strategies Manager at FI Group, and Paul Sinaï, Co-founder CEO and CTO at ForePaaS are interviewed in the video below.


“The multi-cloud gives us the agility and flexibility to choose how we manage our resources, where we store our data, and where we run our machine learning algorithms,” said Paul Sinaï. “Organizations have the choice to partner with a public cloud provider in the Americas, Europe, or Asia, or opt for a corporate cloud configuration to meet their corporate policies and run their big data projects from within the corporate firewalls.”


What is the multi-cloud for machine learning?


There are significant advantages to adopting a multi-cloud strategy. The multi-cloud can be a hybrid cloud that includes public and private clouds. Even though companies adopt hybrid cloud strategies for their corporate reasons, the hybrid cloud presents many similar issues to the public multi-cloud; they are often more complex. Organizations should carefully weigh the advantages and disadvantages of each approach and think about how their needs will evolve. Look for a platform for your machine learning and big data projects that provides a consistent service across any cloud at any scale, supports your organization’s initial needs, and seamlessly evolves as your needs change over time, including data storage capacity, resource allocations, security, accessibility and the number of projects and users you will need to support.


Watch the testimonials



For more articles on data, analytics, machine learning, and data science, follow Paul Sinaï at Towards Data Science.


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