DataRobot and Snowflake have designed a new integration to help customers harness AI and accelerate their data-to-value times.

The new integration simplifies data movement between DataRobot’s enterprise AI platform and Snowflake’s enterprise data platform.

The DataRobot enterprise AI platform provides automation across the entire AI lifecycle, including the organisation, build, deployment, running and management of AI assets. 

Snowflake’s enterprise data platform is built for the cloud that allows all users to gain limitless insight from their data at a fraction of the cost of legacy solutions. 

With the new integration, users can now read data in Snowflake, create predictions in DataRobot, and send those decisions back to Snowflake seamlessly, removing the need for scripts and technical development.

New Zealand-based personal lending marketplace Harmoney is one joint customer that leverages DataRobot and Snowflake for its data science.

Harmoney uses DataRobot and Snowflake technologies to gather, understand, and use critical data that informs the entire customer journey to make sure the experience is as delightful as possible.

Harmoney data and analytics manager Miles Davis explains further: “With Snowflake, we can get all of our data into one warehouse, and with DataRobot, we can make sense of all of the data we are giving it to derive deep predictions.”

“The new integration takes these best-in-class technologies a step further, creating a seamless way to maximize the value of our models. We’re already reaping the benefits of the integration between these platforms and can’t imagine life without them.”

DataRobot VP of alliances Michael Setticasi says that Harmoney and other customers have asked for a full circle integration so they can gain insight and act on data to drive business incomes.

The integration builds on DataRobot and Snowflake’s previous partnership, which was designed to allow users to bring in data from Snowflake into DataRobot. This new integration delivers even more value by sending predictions from DataRobot back into Snowflake directly, removing the need for scripts, technical development, or other steps to get results back to the data warehouse.

DataRobot recently appointed new president and chief operating officer Dan Wright. Wright from AppDynamics, where he was also chief operating officer.

DataRobot founder and CEO Jeremy Achin says that Wright’s passion for excellence is perfectly aligned with what DataRobot aspires to be. 

In 2019 DataRobot acquired three companies (Cursor, ParallelM and Paxata), grew to more than 1200 employees, and partnered with customers in more than 35 countries.

Wright adds, “With its combination of a large and rapidly-growing market, cutting-edge technology, a visionary founder with a passionate team, and support from top investors, DataRobot is uniquely positioned to lead the AI revolution.”

“Jeremy and the rest of the team are laser-focused on maximising their positive impact on customers, employees, communities, and the world. I am honoured to join them on that mission and excited to build on the tremendous foundation they have put in place to create an iconic company.” 

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DataRobot, a company best known for creating automated machine learning models known as AutoML, announced today that it intends to acquire Paxata, a data prep platform startup. The companies did not reveal the purchase price.

Paxata  raised a total of $90 million before today’s acquisition, according to the company.

Up until now, DataRobot  has concentrated mostly on the machine learning and data science aspect of the workflow — building and testing the model, then putting it into production. The data prep was left to other vendors like Paxata, but DataRobot, which raised $206 million in September, saw an opportunity to fill in a gap in their platform with Paxata.

“We’ve identified, because we’ve been focused on machine learning for so long, a number of key data prep capabilities that are required for machine learning to be successful. And so we see an opportunity to really build out a unique and compelling data prep for machine learning offering that’s powered by the Paxata product, but takes the knowledge and understanding and the integration with the machine learning platform from DataRobot,” Phil Gurbacki, SVP of product development and customer experience at DataRobot, told TechCrunch.

Prakash Nanduri, CEO and co-founder at Paxata, says the two companies were a great fit and it made a lot of sense to come together. “DataRobot has got a significant number of customers, and every one of their customers have a data and information management problem. For us, the deal allows us to rapidly increase the number of customers that are able to go from data to value. By coming together, the value to the customer is increased at an exponential level,” he explained.

DataRobot is based in Boston, while Paxata is in Redwood City, Calif. The plan moving forward is to make Paxata a west coast office, and all of the company’s almost 100 employees will become part of DataRobot when the deal closes.

While the two companies are working together to integrate Paxata more fully into the DataRobot platform, the companies also plan to let Paxata continue to exist as a standalone product.

DataRobot has raised more than $431 million, according to PitchBook data. It raised $206 million of that in its last round. At the time, the company indicated it would be looking for acquisition opportunities when it made sense.

This match-up seems particularly good, given how well the two companies’ capabilities complement one another, and how much customer overlap they have. The deal is expected to close before the end of the year.

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The boons of machine learning have been leveraged in the industry in the past many years. With its increasing implementation, the ML tools have also evolved with time. Today, people can easily work with machine learning owing to its easy-to-use, user-friendly tools. As the gathering of data and turning it into actionable insights has been automated enough, people with some knowledge of technology and motivation can work with ML.

These tools possess the strength to handle the mundane work of collecting data, adding structure and consistency where possible, and then starting the calculation. The modern-day tools can simplify the data gathering process and keeping that information in rows and columns.

Such user-friendly features are paving the way for people who work with numbers, spreadsheets and data towards machine learning while eliminating the need to be great at programming and data science.

Below are the five tools that simplify using machine learning algorithms.

Splunk

Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies and generating predictions for future values.

DataRobot

DataRobot incorporates a variety of regression techniques, ranging from the simplest (linear regression) to complicated statistical classic regression models, to more complex techniques including gradient boosting and neural networks. The platform can also solve simple binary classification problems, as well as highly complex multiclass classification problems with up to 100 different categories. Imagine being able to predict which product a customer is likely to purchase next, or why a customer is likely to churn, with a high degree of accuracy. With DataRobot it’s easy to automate the creation of machine learning models like this – with unprecedented transparency so you can understand and trust the predictions they make.

H2O

H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

RapidMiner

RapidMiner’s automated machine learning can exponentially reduce the time and effort required to create predictive models for all businesses and organizations regardless of size, resources or industry. With its Auto Model, it’s possible to build predictive models in just 5 clicks. There’s no need for technical expertise. All users need to do is upload his data and specify the outcomes he wants, then Auto Model will produce the high-value insights he needs. RapidMiner Auto Model is part of a path to fully automated data science, from data exploration to modeling to production, when combined with Turbo Prep and Model Ops in RapidMiner Studio Enterprise.

BigML 

BigML’s AutoML is an Automated Machine Learning tool for BigML. The first version of AutoML helps automate the complete Machine Learning pipeline, not only the model selection. To boot, it’s pretty easy to execute. The user needs to give it training and validation datasets and it will give back a Fusion with the best possible models using the least possible number of features. BigML’s AutoML performs three main operations: Feature Generation, Feature Selection, and Model Selection.

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