Data science is one of the vastest fields existing on the planet. It requires the knowledge of a plethora of niches and hands one experience of working in a real-world problem. Even if you try to somehow forget that there is a huge pile of mathematics involved at the back, the entire machine learning pipeline is tiresome altogether. Right from data preprocessing to feature extraction, selection, engineering, algorithm selection, and hyper parameter tuning, there is a lot more on the plate of a data scientist.
Data Science and Business
There is no end to the extensive list of tasks that a data scientist has to go through. They have to stay at par with the latest trends, dive into data to find insights understand the business requirements and find the relevancy of data to them. And this is probably one of the reasons why not many excel at the job. The complex requirements for the data science industry pose a huge barrier to entry for people. Most end up other profitable and high yielding jobs, other than going through the rigorous nightmare of data science.
But for businesses, this isn’t a great thing. Ever since the wave of digitization has swept them off their feet, organizations and enterprises are channelizing their energies through cutting edge technologies. This means that once the core shift to the digital has taken place, it is up to the organizations to leverage new technologies to make the most of it. Assuming that the tech titans and the Fortune 500 companies are the first ones to implement and make the best of business, it ultimately comes down to small and medium enterprises to handle the burden of the market and be able to satisfy the customer demands with efficiency.
SMEs, therefore, had two choices down the road of the cutting edge competition. Either give on their dream of data science, which is basically saying not listening to what the core processes of the business is saying, or shift to an analogous model of data science. And obviously, most of them are doing the latter and deriving insights like a pro in the market. There has been a tremendous shift in the thought process of leaders across small and medium organizations, The technologies that they once saw as threats and disruptors are now being perceived as the means to business sustainability.
Emergence of AutoML
Therefore, the need for data science has more than just magnified and considering the fact that there is an urgent shortage in the market, businesses are turning to an alternative solution known as AutoML. As the name suggests, AutoML is one technology that aims at automating the machine learning pipeline of the data science stream. It is a quintessential step towards democratizing machine learning that was otherwise trapped in its extensive pipeline, requiring a large amount of human supervision.
With AutoML in the picture, organizations have the potential to develop analytical pipelines and solve sophisticated business problems. Meanwhile, for data scientists, this means freeing up a lot of their time and concentrating on what really matters. And it’s not a direct replacement of data science, because even though it seems like a lot, machine learning is just one aspect of data science. For organizations that invest in AutoML can successfully generate the model using state of the art diagnostics and predictive analytics. There are no doubts about the fact that AutoML automates the entire end to end process of applying ML to real-world problems while working with big data analytics in the picture, there are many risks involved.
What Needs to Be Looked Upon?
Let’s take the example of the famous Google Flu Trends, which was launched back in 2009 with the aim of predicting the present. Since that was a decade ago, the underlying idea of deriving useful patterns and insights from search strings made absolute sense since millions of users begin their digital journey by looking up for something on the search engine Google. However, things weren’t as picturesque as they seem. Google Flu Trends has to shut down in 2015 since the forecasts made by the tool overestimated the flu levels by as much as 100 percent in relation to the data provided by the center of disease control.
More recently, when organizations are turning to AutoML, there is a similar kind of risks in the picture. In a recent Kaggle competition, an autoML was pitted against some of the best data scientists out there. The autoML engine finished seconds, after leading most of the way. But, as we move onto democratizing machine learning consulting services, it raises an important question about the interplay of data into the hands of AI and the managing roles around it.
The point is that even though AutoML tries to automate nuances like feature selection, representation tweaking, model architectures etc. it also adds rigor as the complexity of these tasks grows. But it cannot replace expert knowledge and bridging the gap between the two will be one of the most difficult challenges for researches and scientists venturing into the field. Similarly, knowing when to tweak the representation engineering for maximum effects is only a matter of experience that AutoML currently cannot accomplish.
Even though AutoMl raises several questions on the ethics and management of artificial intelligence, it could become one of the front test running technologies in the foreseeable future. Until the gap between expertise and automation is bridged, organizations harnessing AutoML must stay careful and aware of what they want and what is the engine yielding.