Automated Machine Learning (AutoML) is generating a rising buzz in data science circles. Most of the data scientists look at AutoML with fear and loathing, worrying that their jobs are on the line. Nothing could be further from the truth.

What is Automated Machine Learning?

Think of AutoML as an assistant, not a replacement. Data scientists manually carry out all the time-consuming elements of data preprocessing, model building, reports, and deployment. AutoML comes to take over those steps, freeing you to use your domain expertise to analyze results and fine-tune ML processes even further and swifter, supercharging your added value. Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science. Applying traditional machine learning methods to real-world business problems is time-consuming, resource-intensive, and challenging. It requires experts in the several disciplines, including data scientists – some of the most sought-after professionals in the job market right now. Automated machine learning changes that, making it easier to build and use machine learning models in the real world by running systematic processes on raw data and selecting models that pull the most relevant information from the data – what is often referred to as “the signal in the noise.”

Why is Automated Machine Learning Important?

  1. Solve problems quickly and accurately: AutoML takes on preprocessing tasks like data cleaning and inputting; feature engineering; model, pipeline, and metrics selection; hyper-parameter optimization; and leakage or error detection. With the power of automation, AutoML can complete these tasks faster than any human, bringing you to a solution in much less time.
  2. Deploy faster: With AutoML, there’s no need to test, retrain, and then retest your model manually. AutoML deploys the best model automatically, so that you can shift straight into production.
  3. Make fewer mistakes: AutoML can reduce human error in your results, without losing your unique creativity. You can use AutoML to train your model, test it, partition your data, and calculate model evaluation techniques in a way that lowers human error and provides more accurate results for greater value.
  4. Allocate your time effectively: With the help of AutoML, you’ll discover that you have more time to invest in other areas of your work. You can be more creative now that data processing, model preparation, and post-processing are so much faster. You’ll be able to invest your models with more domain knowledge and spend more time on data preparation, which will have a knock-on effect on the quality of your results.

AutoML is not just a tool, but a team working with you, from data ETL to model maintenance. The AutoML is not only designed for the business experts who are willing to embed machine learning with business intelligence to achieve the commercial goal, but also made for the data scientist who is tired of building framework of machine learning models and want to be more efficient. Do the valuable data science job and get rid of data processing and report generating, use AutoML as a bridge to bring your work into another level.

Want to know how you can automate your data science journey?

Request Demo