TenPoint7 | Understanding MLaaS (Machine Learning-as-a-Service). Making ML easy for SMBs to adopt.
20180
post-template-default,single,single-post,postid-20180,single-format-standard,ajax_fade,page_not_loaded,,qode-title-hidden,qode-theme-ver-10.1.1,wpb-js-composer js-comp-ver-4.11.2.1,vc_responsive

Understanding MLaaS (Machine Learning-as-a-Service). Making ML easy for SMBs to adopt.

Our most recent blog posts shed light on the benefits that MLaaS (Machine Learning-as-a-Service) solutions can deliver for SMBs (small medium sized businesses) helping them overcome the common barriers that prevent such businesses from becoming data driven.

However, the acronym MLaaS itself is heavily steeped in technical jargon, relatively new, and thus not well understood.

So, what is MLaaS?

Simply put, MLaaS is a licensing and delivery model in which Machine Learning (ML) software or models are licensed on a subscription basis, centrally hosted and managed by the vendor providing that service. Yes, you guess it right – just like SaaS.

Aren’t big players like Amazon, MS, Google, IBM and others are already providing some form of MLaaS?

Yes, that’s right.  Microsoft has Azure ML, Amazon has Amazon ML, Google with their Cloud Platform ML, IBM with Watson, and so on. However, all these MLaaS products are geared toward a technical user – data scientists/analysts and developers. These products provide a plethora of visualization tools that guide the user through the process of creating ML models quickly without having to learn the underlying complex algorithms and technology.

Sounds great, but do current MLaaS offerings provide solutions for all types of enterprises and businesses?

These MLaaS product offerings certainly provide benefits to larger enterprises that have scaling data science teams and mature software engineering teams that are looking to accelerate deployment of ML driven products and features.

But these products don’t necessarily help SMBs also looking to adopt ML rapidly to remain competitive. Such businesses are looking for a no-hassle solution that they don’t have to build and worry about maintaining. Data science talent is scare and costly, and these businesses don’t have the time and financial wherewithal to build an in-house data science team. SMBs want complete ready-to-go ML based solutions delivered to them that meet their specific business use case needs.

TenPoint7 Cloud and MLaaS

Our MLaaS solution, TenPoint7 Cloud, makes ML adoption simple and easy for SMBs.

TenPoint7 Cloud primarily consists of two components that SMBs get to benefit:

  • Platform: a scalable analytics infrastructure consisting of storage, compute, database, network, data ingestion and repository of configurable ML algorithms. SMBs needn’t overthink the complexity of internally managing such an infrastructure that is typically required to build and support ML powered solutions. The platform is fully managed by TenPoint7.
  • Business Insight Apps: TenPoint7’s growing library of configurable apps that predict business outcomes for specific use cases. App examples include: revenue forecasting, labor optimization, customer/market sentiment, churn analysis, and understanding content relevance for content marketing initiatives. SMBs no longer need to ponder how they can compete for data science talent to build such solutions. These apps, custom or pre-built, are also fully managed and powered by TenPoint7 Cloud platform.

Easy consumption of TenPoint7 Cloud – one size doesn’t fit all

The current maturity level of technology infrastructure for SMBs is varied. Keeping that in mind, TenPoint7 Cloud provides a few options on how the hosted ML business apps are delivered:

  • Browser: simple, elegant user experience of the solution accessible via browser
  • Web services integration: using API end points between TenPoint7 Cloud and target SMB product/tool
  • Upload: app updates regularly deployed and integrated on SMB owned infrastructure

Democratizing MLaaS

With solutions like TenPoint7 Cloud, we believe that SMBs now have a feasible and realistic option that allows them to adopt ML so they can remain competitive and deliver on rising customer expectations. ML no longer needs to be reserved for big enterprises with deep pockets. SMBs can be part of this data revolution now!

Shane (Co-Founder, Shane@TenPoint7.com)

No Comments

Post A Comment