AI News, BOOK REVIEW: What is DataRobot?
What is DataRobot?
The best machine learning models have little to no organizational value unless they are rapidly operationalized within the business.
Organizations can now derive business value from machine learning in minutes, instead of waiting months to write scoring code and deal with the underlying infrastructure.
A Business Analyst's Journey into Machine Learning through Kaggle
Instead, I stand on the shoulders of giants — that is, the data scientists and engineers who built the DataRobot machine learning platform.
As Product Manager and Business Analyst at DataRobot, I operate at the nexus of three areas: working with users (my personal passion), working with builders (typically engineers), and working with business interests (C-suite executives).
My challenge — what will help the product team at DataRobot understand and relate to a typical DataRobot end user’s experience?
(I’ll explain the oops in the lessons learned, below.) I then create a blend/ensemble model using my top model (XGBoost) along with the top two TensorFlow models.
The business problem this competition is trying to solve is to help Red Hat identify which customers have the most potential business value for Red Hat’s business, based on their characteristics and activities.
Instead of following DataRobot’s fundamental flow of testing model results starting with a small sample size and then iteratively running the better performing models at a higher sample size percentage, I went all in and ran these at a 100% sample size.
Because I ran this model using all the data from the initial dataset in one big bang, I wasn’t able to test the model against data that the model hadn’t seen before.
Third, I didn’t take advantage of another of DataRobot’s great strengths -- the tools to visually identify important features I could have used to build better models and, as a result, provide better business insights.
DataRobot has some neat visualizations which helped the business analyst in me understand the data I was working with while the fiery competitor in me could, in the same tool, build and tweak models.
For example, a really simple and interesting visualization I used showed the distribution of data points across the days of the week (0 is Sunday, 6 is Saturday).
It automatically tested hundreds of predictive models, found the optimal one for my Kaggle data, and vaulted me, in a matter of hours, to the top 2% of a renowned data science competition.
I started this exercise to better understand my target end user and came away feeling really good about making space for you up on the shoulders of giants.
Machine Learning Life Cycle
Here is a step-by-step example of how a hospital might use machine learning to improve patient outcomes and ROI: The machine learning life cycle is important because it delineates the role of every person in a company in data science initiatives, ranging from business to engineering.
It makes data exploration and model building much easier and more accessible, allowing those who understand the business problem behind the data science project to rapidly build and test dozens of models in a fraction of the time it would take using traditional methods.
Additionally, DataRobot’s unique, built-in allow for never-before-possible levels of model interpretability and insight communication without any extra work on your end, making it easier than ever to communicate the value of machine learning to users throughout the organization and beyond.
How Do Model Blueprints Add Value to DataRobot?
Each modeling approach is what we call a model blueprint: a unique sequence of data processing, feature engineering, algorithm training, algorithm tuning, and more.
With the help of our Kaggle-top-ranked data scientists, DataRobot built a comprehensive, best-in-class machine learning framework to help anyone develop and deploy great models regardless of data science skill level.
These algorithms are extremely powerful and help reveal insights from massive amount of data, but some of them suffer from interpretability challenges due to their complex nature – what are often referred to as “black box” models.
Each model blueprint is a sequence of building blocks that helps answer a number of important questions, including: For every building block, DataRobot shares full documentation that contains:
This complete set of documentation not only helps new users learn from the automation process, but also helps experienced users justify the models DataRobot builds.
It ensures each and every step of the model building process is being valued and addressed, which eliminates the confusion and mystery of the black box effect.
Machine learning is a subset of artificial intelligence (AI) that allows computers to learn to perform tasks and make predictions without being explicitly programmed.
To put it simply, the algorithm learns by example, and then we apply those self-learning algorithms to similar data and make predictions about future trends. Machine learning has practical implications across industry sectors, including healthcare, insurance, energy, marketing, manufacturing, financial technology (fintech), and more.
Without teams of difficult-to-find data scientists at their disposal, companies are limited in the number of models they are able to develop and test – and often those models take so long to develop, they are outdated by the time they are complete.
DataRobot strives to make machine learning more accessible to everyone in every organization by incorporating the knowledge and best practices of world’s best data scientists into a fully automated modeling platform that you can use regardless of data science experience or coding knowledge, delivering insights an order of magnitude faster than was previously possible.
- On Sunday, June 16, 2019
WEBINAR | Machine Learning with DataRobot
Edan Kabatchnik, our SVP of Product and SQLstream Founder, demos how to build a streaming application in 30 minutes using DataRobot's ML models.
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