AutoML Is Not Enough
The Data Science Workflow is Fundamentally Flawed
AutoML— automating the process of machine learning to solve real-world problems—has gained popularity because it makes data science easier, faster, more affordable, and less cumbersome. Considering data scientists are in high demand but low supply, they are often overburdened by their workloads. AutoML is an excellent tool to enable data scientists to more effectively bring value to their organizations, because it so heavily augments their intuition and supports them in speedy problem solving —but is it enough? No! AutoML on its own is not enough to support machine learning in the modern business landscape, one which moves and evolves at an ever-quickening pace while drowning in veritable oceans of data. What we need is a paradigm shift.
KEY PRODUCT FEATURES
In just seconds, connect and perform exploratory data analysis (EDA) to understand your dataset and begin building machine learning models.
kraken ml pipeline
With Pipeline, you can quickly implement your EDA findings, conduct light data transformations, and start building machine learning models faster.
Kraken hosts and automates the machine learning pipeline making its AutoML processes incredibly fast, and accessible from anywhere you have an internet connection
Kraken offers a Scenario Builder to give you the “what-if” workbench you need to design action plans and improve business outcomes.
Publish and automate predictions directly to your data environment, export predictions to an excel file, or deploy your model to an API endpoint for real-time predictions in just a couple of clicks.
Kraken aims to make AI as explainable as possible by providing Drivers, model metrics, and Prediction Influencers to surface actionable insights. There’s no ‘black box’ here.