What does the following names invoke in you: Sunil Gavaskar, Rod Laver, Alfred Nobel, Mahatma Gandhi, John Maynard Keynes, Bill Gates, Elon Musk etc. Each of them have been pioneers in their respective fields. Many aspirants have used their life stories as a template to begin theirs. When they say, the whole world lend an ear. Their prowess has been so respected that their opinions become axioms. If we talk about world leaders in technology,it would not be incorrect to assume that Google has been at the forefront of Machine Learning for years and has used it to build a lot of its product such as key word search, ad word analytics, Cloud AI etc. This blog will highlight where does ML fit as a cog in the Google scheme of things.
From the horses's mouth
The discussion henceforth will be based upon the paper published in 2015 by Google employees by the name 'Hidden Technical Debt in Machine Learning Systems'.It discusses ML as something that causes significant 'Technical Debt'. This means the amount of effort, cost and infrastructure that goes in to keep the ML system up and running during the monitoring phase is huge. Much to our dislike, it categorically stresses on the fact that ML Code is a tiny little part in the entire ensemble. This can be seen from the diagram below
As seen in the above block, ML(tiny block at the center) seems to be overwhelmed by the presence of other facilitators. But is this depiction oversimplified ?...Does it mean that ML is just the ghost in the machine?... Lets see how we can understand this.
From the horses's mouth
The discussion henceforth will be based upon the paper published in 2015 by Google employees by the name 'Hidden Technical Debt in Machine Learning Systems'.It discusses ML as something that causes significant 'Technical Debt'. This means the amount of effort, cost and infrastructure that goes in to keep the ML system up and running during the monitoring phase is huge. Much to our dislike, it categorically stresses on the fact that ML Code is a tiny little part in the entire ensemble. This can be seen from the diagram below
As seen in the above block, ML(tiny block at the center) seems to be overwhelmed by the presence of other facilitators. But is this depiction oversimplified ?...Does it mean that ML is just the ghost in the machine?... Lets see how we can understand this.
- Data Collection, Verification, Feature Extraction and Analysis tools are all part of a Machine Learning set up. It has been well documented that type of data to a certain extent governs what ML algorithm will be applied to it. What then follows is a group of steps including, but not limited to, data cleaning, verification, feature extraction etc.
- Configuration and Serving infrastructure all come under the purview of Network Architectures. It is a given that these activities are undertaken once at the start where necessity to create a system dictates proceedings. The system has to be then monitored to ensure seamlessness. However, an ML needs to be monitored daily and changes incorporated as and when there are modification in requirements.
- Monitoring an ML requires more effort in comparison to a Network. ML demands continuous review to keep pace with the changing dynamics. Network compositions are often static which can be upgraded quite easily. Using a tiny block of ML as a placeholder for all the minutiae activities is not justified.
- If we were to remove the ML block, will the set up even make sense. It will be labelled as a collection of fancy things that do nothing. It is like assembling a laptop but sans a processor.
On the funnier vein of things, I think the paper must have been written by Warehousing and Big Data professionals. There is an unending fight between experts who claim to create something tangible against those they claim are superficial.
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