Bringing Automated Machine Learning to the All-in-One Data Warehouse

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SlicingDice and BigML partner to bring the very first Data Warehouse embedding Automated Machine Learning. This All-in-One solution will provide guardrails for thousands of organizations struggling to keep up with insight discovery and decision automation goals.

Due to the accelerating data growth in our decade, the focus for all businesses has naturally turned to data collection, storing, and computation. Nevertheless, what companies really need is getting useful insights from their data to ultimately automate decision-making processes. This is where Machine Learning can add tremendous value. By applying the right Machine Learning techniques to solve a specific business problem, any company can increase their revenue, optimize resources, improve processes, and automate manual, error-prone tasks. With this vision in mind, BigML and SlicingDice, the leading Machine Learning platform and the unique All-in-One data solution company respectively, are joining forces to provide a more complete and uniform solution that helps businesses get to the desired insights hidden in their data much faster.

BigML and SlicingDice’s partnership embodies the strong commitment from both companies to bring powerful solutions to data problems in a simple fashion. SlicingDice offers an All-in-One cloud-based Data Solution that is easy to use, fast and cost-effective for any data challenge. Thus, the end customers do not need to spend excessive time configuring, pre-processing, and managing their data. SlicingDice will provide Machine Learning-ready datasets for companies to start working on their Machine Learning projects through the BigML platform, which will be seamlessly integrated into the SlicingDice site. As a result, thousands of organizations can make the best of their data by having it all organized and accessible from one platform, to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery, and Topic Modeling tasks thanks to the underlying BigML platform.

This integration is ideal for tomorrow’s data-driven companies that have large volumes of data and the need to carefully manage their data in a cost-effective manner. Take, for example, a large IoT project that needs to deploy over 150 thousand sensors distributed in several regions with billions of insertions per day. Normally, this type of data could be very costly and difficult to manage and maintain, but with the SlicingDice and BigML’s integrated approach, data and process complexities are abstracted while risks are mitigated. The client can then not only visualize all this data in real-time business dashboards for hundreds of users located in different geographical areas but also apply Machine Learning to truly start automating decision making, with a very accessible solution, clearly optimizing their resources in a traceable and proven way.

Francisco Martín, BigML’s CEO shared, “It is critically important to acknowledge the costly challenges enterprises face when having to prepare their data for Machine Learning and automate Machine Learning workflows by themselves. By joining forces with SlicingDice, we aim to drastically simplify such initiatives. Our joint customers will be able to focus on becoming truly data-driven businesses with agile and adaptable decision-making capabilities able to meet the ever-shifting competitive and demand-driven dynamics in their respective industries.”

SlicingDice’s CEO, Gabriel Menegatti, wishes “to enable any and every company to be able to tackle their data challenges using simpler tools, which delivers value to them faster. We wanted to offer companies a data solution that is comprehensive, fast and cheap. We built that. Now, by leveraging BigML’s technology, those companies can take their analytics to the next level, using Machine Learning to take the next step in their data journeys. We’re sure companies can seize this opportunity and ensure data is treated as an asset and not just an operational bottleneck.”

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