ML is getting automated and easy: What does it mean for your career?

CS50 project Now

Harsh truth is that building ML has become fairly easy. Completing this project: will not take more than 20 minutes to someone who knows TensorFlow.

latest code
TF code
  • Deep understanding of the algorithms
  • Deep understanding of the mathematics
  • Deep understanding of the engineering

GCP, AWS embedded ML

Going even further, one does not even need to code at all thanks to the use of cloud service providers. Most of them allow for codeless machine learning.

What does it mean for Data Science?

The value is no longer in applying an algorithm that can be learned on one online course. There are so many people who can do this. Sometimes it feels that everyone is doing an ML certificate, which is good but it does mean that the value of data science is elsewhere.

  • Scalability still poses a problem, especially when dealing with complex systems. Despite all the advancement of the platform to handle scalability, many still have a fair amount of complexity when it comes to the implementation in the systems especially with legacy or other. In addition, the problem above does not deal with building a descent Data and ML pipeline that should be required to deal with this kind of data set.
  • Business understanding. Being able to drive a data strategy and see the value when driving the business is useful and create business value
  • Mathematical and statistical accuracy. Having almost anyone able to run an ML algorithm does not mean the application is correct. Multiple issues can still happen. Some are very classical but there are many cases where untrained people won’t be able to detect mistakes. In addition, many models require a more sophisticated approach that needs a deeper mathematical understanding. This is where mathematicians and statisticians still shine in the ability to ensure the correctness of what is happening.
  • In-depth knowledge is still required. Most projects in the online course are easy… True. Let think of the example above. Was the project useful? yes, it recognized the traffic signs with 98% accuracy but does it make it useful? The answer is obviously no. Pictures were well-framed data, data fairly limited, quality is good. If you were to use this in real life just for capturing the speed limit for instance, the car will have to handle real-time processing and the camera will see a million things on the road. It is not because you solve a data science problem that the problem is solved

What does it mean for Data Scientists & Data Analysts?

Sharpen your skills. You cannot just be a guy doing nice notebooks out-of-the-box models.



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Strategy/Data/Leadership ~~ Twitter data science ~~ ex-gojek~~ web3 enthusiast