The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy Big Data platforms.
Platform-centric and vendor-driven architectures should be deprecated if the companies using these, consider superior technology to be their core differentiator from competitors. Amazon would never have been the giant it is today, if its ecommerce business had been built on Magento or its warehouse operations had been powered by the SAP ERP.
Technology lessons learned from the past should clearly define the roadmaps of Big Data analytics projects, otherwise yet another Data Science, IoT, Big Data, AI, DevOps or Streaming platform will lock you in and slow down your innovation and movement forward in the long run. There are numerous legacy mainframe, marketing, content management or billing platforms that slow down enterprises all over the world, as opposed to improving their processes.
Most companies have to rely on the core in-house architecture based on an ecosystem of interchangeable analytical, machine learning and AI services. It democratizes the technology stack and unlocks the advantages of plugging ready-to-go server less applications.
For the sake of simplicity, consider analytics, machine learning or AI services as smart SaaS applications that could either be deployed in your cloud or can be hosted by a 3rd party provider.
The players in this industry are more or less familiar with the server less architectures based on AWS offerings. Following this model and packaging the features of recommendation, image recognition, fraud detection, pricing optimization, risk modeling services as a seamless addition for AWS, Azure, GCE or Azure ecosystems, makes it much more attractive to prospective client companies.
The beauty of this approach lies in the ability for users to cherry pick in-house enterprise services, commercial applications and open source projects, which can then be deployed as a services.
Choose and pick self-sustained offerings for text processing, image recognition, predictive searches and many others per your requirements or desires. The additional features will bring an immediate value and could be replaced later with newer alternatives.
Third party services might be deployed as SaaS and on-premises, right in your cloud if they are packaged appropriately.
