Automate your machine learning with AutoML

Anyone who has spent time tuning machine learning models will tell you that it is an iterative and tedious process that requires many hours of trial and error. The most time-consuming parts of machine learning are feature engineering and tuning parameters. Any tool that removes the tediousness of those elements is going to save developers many hours of performing repetitive tasks and testing.

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It automates each step of the ML workflow, enabling you to build quality ML models with high scale, efficiency, and productivity. AutoML makes it easy to train and evaluate machine learning models and automating repetitive tasks allows people to focus on the data and the business problems they are trying to solve.

Practically all major cloud service providers offer some variant of an AutoML service. Some examples are Amazon SageMaker, Google Clouds Vertex AI, and Azure Machine Learning. You can also use AutoML within Databricks on whatever cloud service provider that supports Databricks if you prefer that approach instead.

Do I need to know machine learning?

The nice thing about AutoML is that you can develop machine learning models without requiring significant domain knowledge, time, or resources. With automated machine learning, you can iterate through models and get production-ready ML models easily. You only need to provide the data and labels and define what the model should do and the all the rest, the feature selection, hyperparameter search, scaling, and normalization techniques, are all applied automatically and tested. AutoML will usually also help prevent over-fitting and imbalanced data in your models with minimal effort from the user.

Many automated machine learning methods support a variety of machine learning tasks including classification, regression, time series forecasting, image segmentation, text analysis, and many more and you can perform all these tasks without knowing exactly how any of them actually works.

When should I use AutoML?

You can apply AutoML when you want to train and tune a model without extensive programming knowledge or without any domain knowledge. Even if you do have the required knowledge to build and test your own models you might use AutoML for a quicker and easier workflow and therefore save on time and resources. The automated approach allows for quick prototyping of ML models with good results enabling an agile workflow.

Conclusion

Machine learning is an iterative and tedious process that requires many hours of trial and error. AutoML automates the time-consuming, iterative tasks of machine learning model development enabling you to build quality ML models. It allows people to focus on the data and the business problems instead of the details of ML. With AutoML, you can develop machine learning models without requiring significant domain knowledge and you can iterate through models and get production-ready ML models easily. You only need to provide the data and labels and define what the model should do. Many different tasks can be solved using AutoML including classification, regression, time series forecasting, image segmentation, text analysis, and many more and AutoML allows for a quicker and easier workflow regardless of your skill level with ML.

Want to know more?

Please get in touch with us if you’d like to know more about AutoML and maybe try it out for yourself. Only requests with corporate email addresses will be handled. By entering your information in our form, you consent to us storing that information in the sole purpose to get in touch with you.

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