Accelerate Machine Learning with a Unified Analytics Architecture

Introduction

The list of potential industries to be transformed by machine learning (ML) has exploded in recent years and is poised to continue that trajectory. We can see this in the increasing number of data scientists employed across the globe. In time, nearly all major industries will have embedded ML into the core of their businesses, automating away mundane and repetitive decisions that are better made by algorithms than humans. But the adoption discrepancy between potential industries and actual industries continues to widen.

This report dives into the wide range of reasons so many ML initiatives fail, and why the majority of those failures occur at the proof-of-concept (POC) stage, right at the inflection point where teams are ready to put their work into production. Of those that don’t fail, studies have shown that 40% to 80% of successful projects take from one month to a year, or more, to finally reach that finish line.

At the end of this report, you will have an understanding of a new data architecture that streamlines the daily workflows of data scientists and enables the seamless transition of models from development into production. We will cover the evolution of data lakes and data warehouses, looking at their strengths and weaknesses, and how they are being used cooperatively in many current production data architectures. We’ll explain where cooperation falls short, and discuss the reasons behind the movement to merge these two concepts into a unified analytics ...