person using a data science platform

As your datasets grow, the traditional machine learning process becomes increasingly inefficient. With potentially billions of individual data points to process, automation is often a necessary next step for any growing AI project. Thankfully, there are several data science platforms capable of making automation a breeze.

However, questions remain: When should you automate machine learning, and which workbench is best for your enterprise?

Machine Learning Without Data Science Platforms

Machine learning boils down to a few necessary steps: 1) extract data; 2) process and label the data; 3) train the processed data against a model; 4) extract insights; 5) repeat with new data.

This workflow is a very simplified version of reality, but the process is worth summarizing because most companies spend most of their time somewhere between the first two steps—in other words, time spent not extracting insights.

This problem should come as no surprise. If data is the “raw materials” of machine learning, then refining raw data is one of the most crucial steps of the process.

Common Problems in Enterprise Machine Learning

So, isn’t it only natural that most companies would spend most of their resources on data refinement?

Short answer: Yes. Long answer: Still yes, but data refinement isn’t where companies generate value from machine learning. The value of machine learning lies in the insights it makes—not in data refinement alone. Data refinement just-so-happens to be a crucial first step.

The key, then, seems to be spending much less time on data refinement and much more time on extracting insights and bringing in new data (i.e., where the value lies). However, how is it possible to shift resources to extracting insights without sacrificing quality in data processing?

The answer: Automation.

How Data Science Platforms Help Automate Machine Learning

Traditional machine learning can be cumbersome, especially with more massive datasets. It’s easy to see why: in addition to “manually” importing, structuring, and pre-processing data, you’re also tasked with having to select, deploy, and retrain models, as well as to detect declines in effectiveness and rebuild models when necessary. Each of these steps has several sub-steps and can easily consume a ton of time. It turns out data refinement isn’t the only resource killer!

Workbenches exist to automate much of these processes. Effectively implementing workbenches not only guarantees efficiency but may also identify trends and models which could have previously gone unnoticed.

However, not all workbenches are made equal. Some workbenches automate only certain parts of the machine learning process, which may be beneficial if your organization wants to give a specific area “extra care.” If you’re a small company with tons of data, however, you may want to automate the entire process.

Finding the right workbench for your enterprise can be a challenge, especially with such a large number available at all levels and capabilities. So, which workbench is right for you?

Our 3 Favorite Data Science Workbenches for Automation

Throughout our experience in AI implementation, we’ve found a small handful of workbenches which satisfy the automation needs of most companies. The three below are some of our favorites.


DataRobot is one of the leading predictive modeling platforms on the market, delivering enterprise-level machine learning solutions for several industries. The workbench automates the machine learning process by utilizing data science best practices and model customization.


Orange specializes in data visualization, which may be especially useful for those with complicated data pipelines and workflows. With Orange, building machine learning models becomes a visual – perhaps even enjoyable – experience.


Alteryx is a leading data analytics platform which incorporates machine learning into its software. One of the benefits of Alteryx is its ease-of-use and accessible design, which helps make sophisticated analytics available to even the most novice of data scientists. Don’t let the accessibility fool you, however—the software is still very powerful.