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

Think of AutoML as an assistant, not a replacement. Currently, data scientists manually carry out all the time-consuming elements of data preprocessing, model building, exploitability 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. 

As you’ll see, there’s no replacement for a talented, experienced data scientist, who has significant intuition, creativity and, most importantly, domain knowledge, which he uses to define and solve business problems. Your role is crucial in the data preparation phase and then again after the model is generated, to interpret and apply the insights provided by the AutoML system.

Here are 5 ways that AutoML adds value to your work:

  1. Solve problems quickly and accurately: AutoML takes on preprocessing tasks like data cleaning and inputting; feature engineering; model, pipeline, and metrics selection; hyperparameter 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. Deliver better explainability: The visualizations and reports generated by AutoML help back up data scientists who are facing questions from stakeholders and management figures, as well as regulation. When you present your process and results in a clear, dynamic way, you’ll find it easier to convince your listeners. 
  5. 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 postprocessing 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. 

In general, AutoML helps you to find high performing, high speed solutions, with far less effort. If you imagine the classic machine learning workflow, you’ll see that many steps can be greatly improved with the input of AutoML.

This is how the AutoML workflow differs from the classic workflow:

A classic machine learning workflow typically follows this path:

  1. Formulate your question. Every data modeling process begins with the right question. It requires an open mind and creative thinking to be able to change the question as you travel through the workflow.
  2. Acquire the data. Gather data to create your own database, or mine existing data to arrive at a training dataset.
  3. Preprocess the data. Extensive data preprocessing tasks include checking for errors, duplications, and impossibilities, and feature engineering, before extracting the relevant data that answers your question.
  4. Create the model. At this point, you’ll begin building the model you need. You’ll try different models to choose the one that’s most effective, adjust the hyperparameters, run the training dataset, and validate your results.
  5. Communicate the results. Create simple visualizations that can include graphs, charts, PowerPoint presentations, and more to present the results of your investigations. 
  6. Deploy the model. After a long process of preparing and training your model, you’re ready to deploy it.

If you work with AutoML, you’ll find that you can automate all the steps involved in exploring and modeling the data (numbers 3 and 4 above). AutoML also assists with communicating the data, condensing the entire 6-step workflow to save you time and effort.

If you’re still feeling uncertain about how much AutoML could help you with data preprocessing and model building, this infographic will show all the ways that it could lighten your burden.

How AutoML can speed up your data preprocessing

1. Data cleaning: Removing unwanted values, deduping, spotting impossibilities
2. Imputation: Handling missing data by substituting it with estimated values
3. Sample generation: Creating a synthetic dataset based on the data already gathered
4. Data transformation: Converting data into a different format or structure, prior to modeling 
5. Feature engineering: Choosing, extracting, and creating features to power your algorithms
6. Feature stacking: Combining multiple feature sets to produce better results
7. Feature embedding: Converting features into vectors embedded in different layers of your model
8. Feature selection: Selecting a subset of features to reduce dimensionality, simplify the model, and reduce overfit and training times

“Garbage in, garbage out,” as they say — your model is only as good as the data you feed it. So your added value lies in the data you will optimize for the model, according to the insights you’ve received from the system. 

In freeing you from the busy work, AutoML enables you to focus on the data itself and get high performing results with little to no effort — the very definition of a superstar!