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How will the Data Scientist’s job change through automated machine learning?



Automated machine learning is a fundamental shift to machine learning and data science. Data science as it stands today, is resource-intensive, expensive and challenging. It requires skills which are in high demand. Automated Machine learning may not quite lead to the beach lifestyle for the data scientist – but automated machine learning will fundamentally change the job of a data scientist. It’s an irony that AI / ML could replace many jobs – and the first it seems is that of the data scientist himself! But we have been there before. In the 90s and 2000 we had CASE tools. Managers loved them because they were supposed to replace those expensive Programmers. That has not happened. So, would automated machine learning be any different?

Automated machine learning solves a different problem.  It does not advocate zero human intervention for data science (data in – model out).  More specifically, automated machine learning solves three technical problems: feature engineering, model selection and hyperparameter tuning. We do not discuss these here because you can find a lot about them by searching the Web. Rather, we see how fast automated machine learning is advancing

Last week, Google launched an AI platform for collaborative machine learning. It points to how rapidly automated machine learning is moving. Here are some key takeaways which  point to its rapid growith

How will the Data Scientist’s job change through automated machine learning?

In a weekend post Charles Givre pointed out that AutoML came second in a Kaggle contest. This is indeed impressive(albeit a bit contrived). At the moment, I believe AutoML is not poised to address the top 10% of the solutions (which require skill and design) – but this development shows how far AutoML has come already


So, what can we expect of AutoML in the near future? Based on the Google AtoML announcement webinar which I watched – here are seven ways in which the Data Scientist’s job will change

  1.  AutoML for research to create better / state of the art architectures like for imageenet
  2. Custom models by domains
  3. Applicable to solve 80% problems in domain
  4. Massively scale ML applications in the real world.
  5. An emphasis on transfer learning
  6. AutoML for time series
  7. AutoML for NLP
  8. Democratising Machine Learning – like spreadsheets(calculation) , SQL (data access)


Image source: Google AtoML announcement webinar 

Image source:Beach


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