Welcome to insideBIGDATA's annual technology predictions round-up! The big data industry has significant inertia moving into 2023. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. There are many reasons why a customer would choose to implement their architecture on multiple clouds whether it's technology, market, or business-driven. When this happens, many times this leads to transactional and operational data being stored on multiple cloud platforms. The challenge this brings is how to gain insight into these without resorting to implementing multiple disparate data platforms. Historically data virtualization tools have been ...

Containers and Kubernetes are proving to be very valuable in helping accelerate Artificial Intelligence (AI) and Machine Learning (ML) lifecycle for organizations worldwide. ExxonMobil, BMW, Volkswagen, Discover Financial Services, Ministry of Defense (Israel), Boston Children's Hospital, are some organizations have operationalized Red Hat OpenShift, industry leading Kubernetes-based container platform, to accelerate data science workflows, and build intelligent applications. These intelligent applications are helping achieve key business goals and providing competitive differentiation.

We expect the landscape to be an integrated edge-to-core-to-cloud solution enabling what today is called IoT, Big Data, Fast Data and AI. Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything--until we reconcile how that technology fits into the bigger picture. Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring.

Seldon Core focuses on solving the last step in any machine learning project to help companies put models into production, to solve real-world problems and maximise the return on investment. Data scientists are freed to focus on creating better models while devops teams are able to manage deployments more effectively using tools they understand. Instead of just serving up single models behind an API endpoint, Seldon Core allows complex runtime inference graphs to be deployed in containers as microservices. Efficiency -- traditional infrastructure stacks and devops processes don't translate well to machine learning, and there is limited open-source innovation in this space, which forces companies to build their own at great expense or to use a proprietary service. Also, data engineers with the necessary multidisciplinary skillset spanning ML and ops are very scarce.