NorthGravity Launches AutoML to Simplify Machine Learning for Analysts

Democratizing machine learning with AutoML empowers organizations to seamlessly perform advanced data science and analytics at scale within the NorthGravity Platform (NGP).

The NGP helps companies utilize machine learning and data at scale, creating a competitive advantage for their business. The NGP includes a cloud data warehouse, ELT framework, dataflow automation, AutoML, self-service ML insights and global data governance.  

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What is AutoML?

AutoML (Automated machine learning) automates the tasks by applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. The high degree of automation in AutoML allows non-experts to use machine learning models and techniques without requiring them to become experts. Automating the process of applying machine learning end-to-end also offers the advantages of producing more straightforward solutions, faster creation of those solutions, and models that often outperform hand-designed models. AutoML can be used to compare the relative importance of each factor in a prediction model.

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What does production AutoML look like in the NGP?

AutoML is fully integrated within the NGP. Clients can run, test, and move advanced Machining Learning models to production in seconds with the five steps below.

1. Pick your input dataset.
2. Select what do you want to forecast (target).
3. Run the AutoML.
4. Receive the model's output, email and dashboard.
5. Schedule or trigger the AutoML pipeline.

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Why should you care?

Successful machine learning adoption has a proven positive impact on revenue and a clear cost reduction fueling companies to lap their competitors.

What direct value does AutoML bring?

• Democratize the power of machine learning across your business, empower the market experts to ask impactful questions, and access on-demand predictive results
• The starting point for Data Scientists, reducing the days or weeks to uncover what data and models are important to start with.

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