Evolution of Analytics

​​

Looking at the evolution of analytics as a whole, we see a drive towards intelligent processing that is necessitated by data diversity and complexity in needs. In recent times, AI has taken inroads into the field and has made major contributions such as machine learning based analytics.

​

Problem of Machine Learning

​​

Overall, machine learning is generally difficult to apply within an enterprise because it is based on a paradigm that is uncommon to various IT professionals: architects, data engineers, programmers or analysts alike. Instead of a streamlined workflow, users must now adapt to experiment-driven pipelines which do not fit with their familiar data processing pipelines or SQL databases.

​

For example, they can no longer design their models, upload data then "interact" by means of querying or mining to gain insight. Moreover, they need a diverse toolset and techniques to prepare data, experiment with different algorithms, train the mathematical models, deploy for production and then run inference on new unseen data. All this makes ML difficult and very cumbersome to apply.

​

​

Flek the Solution

​

To solve this problem, Flek offers a unified ML platform consisting of a running engine, server and programming toolkit that makes it easy to build models and then use them for exploratory and predictive analytics using the familiar database-like pipeline.

​

Thanks to the scalable architecture, users can also test their models locally, then deploy them remotely on the cloud similar to the way database models are deployed and shared today.