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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;
- 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.).
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?