Product Launch Announcement: Mateverse High Level — V 1.0

The product that let you train ML models in 5 minutes, and brought Machine Learning to IFTTT just got even more advanced.

Some of us are already implementing Machine Learning in our businesses, and some, are still contemplating its use. Statistics predict a huge increase in the use of artificial intelligence by 2021. Many have written repeatedly on the subject of how transformative the AI technology is going to be, and calling AI the third Industrial Revolution. We are not going to dive into that, but let’s take a look at how Mateverse comes into the picture.

TL;DR to the story of the AI’s tremendous influence in society is: Machine learning is seeping into every other business organization. As a result, it’s making their processes (internal and external) tremendously easy.

What started with large companies, culminating in manifold sales conversion with the ability to cater to unique customer demands, started making its way into smaller organizations. Areas such as analytics, sales desk, UI and much more, underwent large transformations within the smaller companies. Now, word is spreading faster than ever.

Source: McKinsey Global Institute

Frankly, nothing ever prevented the smaller organizations from implementing what the bigger ones could, other than the cost.This expensive affair becomes quite an ordeal for smaller organizations. Soon enough AI, and its essential counterpart, Machine Learning was made accessible through Open Source algorithms, and the availability of pre-trained ML models.

Another fact, that not many take notice of, is that while such rampant adaptation is happening around companies worldwide, guess who bears the brunt for the ML project gone awry? They are the Analysts and the Data Scientists, who have shouldered the solo role of pushing the analytics with little to no help from other departments. There are numerous cases of Data Scientists switching companies, or ending up at highly unsatisfactory jobs, either because they do not get to do what they can, or because they are made to do Himalayan tasks with insufficient infrastructure.

We really can not blame anyone, considering that AI and ML are very new technologies trying to shake off the hype and fear around them.

We at Mate Labs started building our product- Mateverse, upon identifying, and knowing all too well, the struggles of the industry having tried our hands at various projects before. As we attempted to come up with a solid solution for some time, to the tremendous, and mundane processes that is involved in Machine Learning, Mateverse V1.0, is finally out there. Lit..era..lly, fighting the tech corporates of the world, and finding the will in a community dedicated to helping us bring AI to the masses.

The struggle (grit) is real.

After testing the idea with Mateverse beta Version, we got an overwhelming response from the smaller businesses and analysts behind huge walls of MNCs that previously had no good idea about how ML could help their businesses, or didn’t have a lot of room to play. Having to not write a single line of code for the Modelling, Mateverse has made it several times easier for companies to deploy predictive models, and bring accelerating impact to their businesses.

Coming back to the factors that deter ML implementations for businesses , we’ll connect the dots and talk to you as to how and where Mateverse is a cost-cutter, as well as an “effort-cutter”.

Perhaps, the best way to take you through how Mateverse does the cost and effort cutting, would be to go through the steps of Machine Learning. Let’s take one of the best examples of building a Predictive Analysis Model

While present solutions in Automated Machine Learning seems like the following: Get Data — -> Perfect Predictions It definitely does not work that way.

The best way to piss off a Data Scientist would be to assume that it is all too easy with some data at hand. Worse, incomplete and inconsistent data can never accomplish a project.

Still, there are ways that we can make it easier for Data Scientists to Deploy a Model faster, by which I mean, way faster.

To Deploy a Predictive Model (to take the best example), the steps involved could be summarized as follows:

Data Collection

In the Data Collection phase, Mateverse supports all common types of databases HDFS/RDBMS/NoSQL, or you can just upload a CSV file and a database is created. As simple as that.