Automated Machine Learning: What Does Future of Data Science Look Like?
According to Gartner, more than 40% of the tasks in the field of Big Data and Data Science will be automated by 2020. Does it mean data scientists will eventually be superseded by automated machine and deep learning algorithms? Notable AI experts including Mikio L. Braun and Nick Elprin believe data scientists have nothing to worry about. Here’s why.
Data science and Artificial Intelligence: is data mining subject to complete automation?
We can identify several trends in modern machine learning (ML) and data science. First, there is deep learning: the recognition of images, audio and video files, and text processing in natural languages. Another trend is reinforcement learning, the technology which enables algorithms to successfully play computer and board games, and makes it possible to continually improve models based on their response to different environmental factors. There’s also ML automation – the trend that is less noticeable (because its results do not look so impressive to external observers), but no less important. Will data science automation leave top analysts and engineers jobless?
Let’s now look at the role that a data scientist plays in the overall process.
It is certainly not limited to using analysis tools (regardless of their complexity)!
According to the most popular data mining process methodology CRISP-DM, the implementation of projects in the field of data mining consists of 6 phases, and an analyst or data scientist is directly involved in each and every phase:
- Business Understanding;
- Data Understanding;
- Data Preparation;
Phases 3 and 4 involve a lot of routine work. Applying machine learning to deal with specific cases will constantly require data scientists to:
- Tune the hyperparameters for the models;
- Try out many different models;
- Explore numerous feature representations for data (standardization, stabilization of the dispersion, monotone transformations, etc.).
These routine tasks, as well as some other tasks related to data preparation and data cleaning, can be easily automated thus making a data scientist’s job much easier. However, all the other tasks in Phases 3 and 4 and the rest of CRISP-DM phases will remain, so the simplification of the daily work of analysts will not pose any threat to their profession.
It’s worth mentioning that machine learning is just one of the tools available to data scientists on top of visualization, aggregated analysis of data, statistical and econometric methods.
But even machine learning cannot be fully automated!
The importance of a data scientist’s role will, of course, remain in solving non-standard tasks, in the development and implementation of new algorithms and their combinations. While an automated algorithm can iterate through all the standard combinations and give a basic solution, a qualified specialist will then be able to build on this solution and continue to improve it.
The future of ML and Data Science looks bright – but we’d better act now
Summarizing the arguments given above, one can hardly expect that businesses will be able to fully enjoy the benefits of automated ML without the help of analysts. In any case, there will always be a need for data preparation, interpretation of results and work performed for the other phases of the CRISP-DM model described above.
These days, many companies have analysts who are constantly working with the data and deeply understand the subject matter but sometimes do not possess the required skills in machine learning. With the demand for highly skilled (and highly paid) specialists in ML growing many times faster than the supply, it is often difficult for companies to attract such experts. So, the obvious solution here will be to give your analysts access to automated ML.
Even though the concept of ML automation may not be one that all data scientists are currently familiar with, the time to get better acquainted with it is now. The demand for specialists in the field of AI and data analysis is constantly growing, and the automation of ML, which leads to the democratization of technology, will only expand its use. On the other hand, to ensure that your quest for introducing the cutting-edge technology in your company is truly successful, you will also need a reputable software development partner with the necessary skill set and experience of working in this field. Thanks to automated ML, you can free your data scientists from the burden of repetitive and time-consuming tasks so they can spend their time on tasks that are much more difficult to automate. Why not give it a try?