Boosting enterprise machine learning with automated feature engineering

Categories: Machine Learning

Machine learning. The very name suggests there’s little involvement required from actual people. It’s a bit surprising to note, then, that perhaps the most limiting factor in data science and machine learning today is people. People add complexity. People add the risk of error. And people add a lot of time.

However, we’ll always need people to come up with the overarching prediction problems to solve and to make the ultimate choices to solve them, but there is a lot of data science work now that’s being done manually, and is repeated in project after project. This is where the promises of machine learning come in. Once human input is completed, automation takes over, reducing complexity, risk, and therefore, time. This frees up humans to focus on the most critical tasks they need to accomplish to advance their projects.

Feature engineering, or the process of transforming raw data into a chosen representation of the underlying problem to be solved, is the data science process most ripe for automation. Before any machine learning can be applied to a business problem, data scientists need to define the problem itself. They use their domain knowledge to extract new and crucial variables from raw data, and then apply machine learning to that information. This can mean manually brainstorming and calculating variables and questions to try to answer and can take weeks or longer.

Tools and platforms that automate and streamline manual tasks like feature engineering are finally making their way into the enterprise. This technology empowers organizations for data science success.

Here are a few of the best ways to get started:

Structure the development process

Generally, building end-to-end machine learning models today is an ad hoc process in which every new dataset requires figuring out a solution from scratch. It’s like if every time a car company developed a new sedan, it reinvented the wheel.

Automating manually intensive processes, like feature engineering and feature extraction, provides a structured way to approach the taxing process of preparing raw data for machine learning. It has the added bonus of providing a framework that can be leveraged for future projects.

 

Feature Labs uses the Deep Feature Synthesis (DFS) algorithm to automatically build features

DFS brainstorms, calculates, and recommends the most predictive features for any machine learning problem

Understanding the top features provides valuable insights into your business

Develop and deploy faster

When an organization takes up a data science project, they usually don’t have time to wait for results. If you’re coming up with solutions to fight credit card fraud, for example, every day matters.

Automated tools help you quickly implement accurate machine learning solutions. When you implement feature engineering and machine learning tools, you can shave weeks off the timeline of manual feature engineering processes. With these tools, you often don’t have to recode in order to deploy machine learning projects to production. A data science platform can then help you provision computational resources to run them.

Use a platform for enterprise machine learning

Cloudera Data Science Workbench (CDSW) can supercharge machine learning projects in a number of ways. Improving collaboration among teammates and code versioning capabilities, for example, help you create results that are reproducible across projects. You never start from scratch.

And let’s not forget the IT side of things. Data science projects can strain IT departments that are already strapped for time and resources. Platforms that are secure by default, and flexible — ones that let you run in the cloud, on-premises, or both — free up your IT department to focus on the business-critical aspects of machine learning.

Finally, there are the benefits of easy integration when it comes to platforms and tools. For example, Feature Labs’ Python APIs let developers build solutions directly inside CDSW. This means that users can take advantage of CDSW’s code versioning, collaboration, and other features to deliver reproducible results.

Removing the bottlenecks in the data science process

Data science and machine learning have come a long way, and are a critical part of solving enterprise problems. They can still be complicated and taxing, though. So, take advantage of the tools available to ease the burden on your data scientists — and your IT team — to get the most out of every machine learning initiative.

 

Contact Feature Labs to learn more about making your data machine-learning ready.

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