Discover the differences between SaaS, IaaS and PaaS and why these are important distinctions for machine learning projects. SaaS vs PaaS vs IaaS: What’s the difference & how to choose. Examples and how to differentiate everything as a service platforms.
When it comes to your machine learning and data analytics projects, would you be better off choosing an SaaS, IaaS or PaaS platform? These are the choices you will face when looking for a platform to build and deploy your analytics, machine learning, artificial intelligence applications. Is there an easy way of summarizing the difference between these three variations of the cloud?
What is SaaS? Software as a Service definition
At the other end of the scale, so-called SaaS applications (Software as a Service) provide a service that end users can utilize directly. No action is required other than signing up for the service to activate their account. PaaS solutions are based on an intermediate model complete with a ready-to-use technical environment that each company can use to design and roll out its own applications.
What is IaaS? Infrastructure as a Service definition
First of all, let’s go back to basics. IaaS services (Infrastructure as a Service) use the cloud to give companies a chance to tap into raw capacity, meaning processing power, storage and bandwidth. The organization is responsible for deploying and configuring all the required components, from the operating system through to the business applications and infrastructure software (databases, web servers, cache managers, etc.).
What is PaaS? Platform as a Service definition
For those who enjoy food analogies, you will appreciate the infamous “Pizza as a Service” model, and the pizza one-liners that ban’t be topped. The only issue is that that these articles were published in 2014 and now only cover a fraction of what a Platform as a Service is or should be. A PaaS providers host the hardware and software on their own cloud infrastructures. PaaS frees developers from having to install anything, including hardware and software to develop or deploy their machine learning and analytics applications. PaaS solutions are touted as simple to use and super convenient. Organizations only have to pay as they go representing considerable cost savings.
What are the differences between PaaS, IaaS and SaaS?
The comparative advantages of the three solutions have long been summarized with a diagram representing an extremely dated analysis that does not fully reflect how data applications have developed… and how their requirements have changed:
Accelerate the project lifecycle
In terms of data projects, the overriding criterion is the time-to-market. Digital transformation has left its mark and set the pace. The aim is to design and deploy projects within short timeframes without removing the possibility of developing bespoke applications. On the contrary, an organization’s data contain all the specific features of its business activities, and analytic applications must be capable of extracting the very essence of those data. This dual requirement – speed and personalization – is especially suited to PaaS solutions.
Incorporate technological innovations
The software stack for data projects is complex and constantly changing. Whether looking at big data technologies or algorithms for machine learning or analytics, software innovation regularly spawns new components. But organizations still need to be quick in incorporating them so that they can be used to lead projects. That is why it is effective to have a PaaS specifically focused on analytical applications, where the vendor is responsible for qualifying the value of any new features and subsequently ensuring that they are correctly integrated and automated.
Keep the freedom of choosing the cloud service provider
Microsoft Azure, Amazon Web Services, Google Cloud Platform, OVH… when it comes to cloud service providers, companies are spoilt for choice, especially since they can also opt for a hybrid cloud solution in order to keep their data on their own infrastructures while running applications in the cloud. The good news is that technologies have matured to such an extent, like containers, that PaaS services can now be agnostic. Basically, the resources are available to make PaaS services independent of the underlying technologies and therefore capable of running in any cloud environment.
What’s included in a platform as a service?
For machine learning and data applications, organizations need to take a new look at their potential options with other analytical criteria. Discover a multi-cloud PaaS solution that is capable of ramping up your most specific data projects. Ask for a demo now! Although the result shows PaaS solutions in a favorable light, it still only offers an initial insight. The success of a data project also depends on the chosen organizational structure and methodology, as well as its ability to engage users. These are just some of the areas where a Platform as a Service can pay dividends.
For more articles on cloud infrastructure, data, analytics, machine learning, and data science, follow me Paul Sinaï on Towards Data Science.
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