Will it empower or forbid?

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The popularity of AutoML solutions has increased in recent years. There are many companies offering a wide range of solutions. These solutions focus on common business problems and tasks frequently pursued by data scientists.

Below is the list of top AutoML solutions. This list is based on AIMultiple, a technology industry analyst.

DataRobot, Dataiku, H2O, Compellon, Enhencer, Akkio, TPOT, dotData, BigML, Prevision.io, TIMi Suite, B2Metric, MLJAR, DMWay, Auto-sklearn, Aible, Auto-WEKA, Tazi.ai, PurePredictive, Caret, Xpanse AI, OptiScorer, Auger.ai

The technology giants are also leveraging their existing infrastructure to push AutoML solutions. They have products to build, deploy and scale ML solutions. Most cloud platforms have dedicated products focused on image recognition and text analysis. They also offer products for building high-performance ML models on structured data. These solutions are quite good in features selection, model selection, and model tuning.

As per ResearchAndMarkets, the AutoML market generated a revenue of $269.6 million in 2019. It is expected to hit $1.5 billion by 2030. It also suggests that cloud-based AutoML solutions are being preferred. As they offer scalability and flexibility to customize solutions.

Many tasks performed by data scientists are repetitive and time-consuming. This limits the ability of the data science team to work on more business problems. Most data science teams end up focusing only on business-critical issues. Here is how AutoML would empower the data science team

Time-Saving

  • AutoML will enable automation of repetitive and manual tasks that are susceptible to human errors
  • AutoML will reduce the effort required on data cleaning, exploration, and feature engineering from weeks to days
  • AutoML will make the model selection and performance monitoring easy
  • Hyper-parameter tuning can be completely automated using AutoML

Level Playing Field

  • High performing pre-trained ML model will be accessible to everyone
  • AutoML will make machine learning attainable to many small and medium business
  • Will make it easy to use unstructured data in making business decisions
  • Will increase participation of non-technical users in solving data-oriented problems
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AutoML will create a ripple effect across the life-cycle of a data science project. It will be changing the landscape of data science jobs. Below are typical stages in a data science…

Continue reading: https://towardsdatascience.com/how-automl-could-drive-the-future-of-data-science-jobs-173c5b48dc5c?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com