DataRobot is an AI cloud leader, delivering a unified solution for all users, all datasets, and all environments to help organizations accelerate AI deployment to production.
DataRobot has been trusted by global customers across industry sectors, including a third of Fortune 50 companies, delivering over a trillion forecasts for leading companies around the globe.
But what is its actual purpose? You might have heard of DataRobot and been told that it is very helpful for many companies, but what is the actual reason for this?
We aim to answer these questions in our article below.
The Background Of DataRobot
First created in 2012, DataRobot was started by Tom de Godoy and Jeremy Achin.
They both have an impressive amount of experience in dealing with data science and machine learning models.
It has recently received $54 million in funding from New Enterprise Associates (NEA) from Series C.
So far, the company has received about $125 million in funding from investors such as NEA, Accomplice, Intel, and IA Ventures.
Automatic Machine Learning
Automated machine learning has been revolutionizing data and AI by enabling predictive analytics to become available to businesses.
Business Intelligence (BI) tools can be used by people who are familiar with mainstream machine learning (ML) tools to build, train, deploy, and manage advanced ML models.
A data scientist can go deeper into the details of a model than a business analyst.
The DataRobot platform has changed and grown over the past years to capitalize on new innovations in the cloud open to the public.
Companies are able to choose whether they want to use the software in the public cloud or in their own data centers.
The hosted version called DataRobot Cloud Platform is currently available on AWS.
Last year at AWS re:Invent, DataRobot earned the Amazon Web Services Machine Learning Competency status.
This was backed up by their claims that customers built over 500 million models using the DataRobots Cloud for AWS.
DataRobot offers a user-friendly wizard-style interface to let you create Machine Learning models easily.
Businesses can create an online real-time predictive analytics service backed by machine learning models in just six easy steps, according to a new report from Forrester Research.
Using DataRobot To Create Machine Learning Models
To use DataRobots to create your machine learning models, you’ll first load the dataset into the platform.
It can be loaded from a Java class, a remote URL (for example, Amazon S3), a database connection, or an HDFS file system.
Once data is ingested, the platform infers data types for each feature. Both business analysts and data scientists can complete the necessary tasks to ingest the ingested dataset.
They will need to choose the target category for which they want to predict the outcome.
When the user clicks the start icon, the modeling process begins. It’s as simple as that! When DataRobot uploads a dataset to its platform, it trains each algorithm using that dataset.
Depending on the dataset size and the number of algorithms you use, it could take a few minutes to an hour to complete the job.
After the modeling process is complete, the results are displayed on a leaderboard, with models that perform best according to the chosen performance metric at the top.
You can explore each method to learn its methodology and parameters used during the training.
You can test each algorithm with a test set to see how accurate it’s at predicting results.
Once a model has become an API, it becomes available for use by others. This is then ready to deal with data of production.
People who have the API key can invoke this RESTful web service just like they could with any other service on the web
Pros Of DataRobot
DataRobot makes it easier for business analysts who want to use machine learning to observe the power of this technology.
What makes DataRobot stand apart from the rest of the tools available?
The beauty of DataRobot lies in its extensibility. You can use the tool at your own pace, depending on your comfort level and the job you’re doing.
Business analysts can use the tool as an expert, but seasoned data scientists can tweak many settings to get precise models.
We’ve built DataRobots from our own personal experience; we elegantly deal with business problems like sales forecast, customer churn analysis, anomaly detection, and time- series modeling.
Downfalls of DataRobot
There are some limitations with the platform that may be addressed in future releases, but for now they’re not too big an issue.
One of them is the fact that it cannot deal well when faced with images and unstructured data. At this time, CNNs are not capable of classifying images and objects.
The model collection includes Deep Learning models but they cannot be trained using a Convolutional Neural Network (CNN).
Customers can export a model trained by the DataRobot platform as a self-contained Java JAR file for use outside of the DataRobo platform.
However, only if they have the premium version. Offline mode is an essential feature for scenarios where the models run in offline mode
What’s Next For Auto Machine Learning?
As AI becomes democratised, and a clear leader in this process is Auto ML, every platform vendor is attempting to jump on the bandwagon.
Google has announced plans to release an AutoML API, which will be available in the future. Microsoft has already launched their own custom cognitive services.
For business analysts who want to use artificial intelligence (AI) to solve their business problems, DataRobot is currently ahead of the curve by offering a robust solution that doesn’t require them to be experts in AI.
As more companies adopt virtual assistants, we expect to see them develop new features.
Summary
DataRobot is a unique AI platform that can run in both the public cloud and on premises data centers.
Data science shortages combined with evolving privacy regulations make DataroBot very attractive for enterprises
The future of AI is in AutoML. As one of the earliest companies to enter the AI Assistant market, DataRobot holds a lot of potential to be a leader in the industry.
Even as new platform providers introduce their versions to the marketplace, we think DataRobots will remain a market leader for a long time.
