Automated machine learning and the future of data science
Every company, whether it is big or small, wants to get something big out of their data, and for the same reason, they are adopting automated machine learning. With the increasing use of automated machine learning, there is a question raised on the existence of data scientists and their value to their organization.
Machine learning is a part of Artificial Intelligence that helps in finding the right model that contains the right set of data aggregation tools, marketing ETL tools, and open-source data aggregation tools that assist in the effective break down of complex and big data silos to drive valuable insights from them. As automated machine learning helps in automating an efficient model-building process, it does not take the place of data scientists as their work is not just implementing a machine learning model. Machine learning helps the data scientist to select the right model for the required set of data and allow them to focus on other different kinds of work.
Machine Learning identify patterns in data and make decisions with minimal human intervention. The machine learning systems provide some significant breakthroughs owing to which are used in various industries such as financial services, healthcare, retail, banking, and many others. The main aim of automated machine learning is to lighten a load of data scientists so that they can focus more on the processes that require another level of creativity and thinking. To help you get a more clear picture of automated machine learning and how they affect the future of data science, let us discuss its life-cycle:
- Business case development: The very first step involves the identification of problems that a business has to solve. During the problem identification process, the data scientists have to determine where machine learning can help the business to fulfill its goals or objectives in order to gain success. For determining the areas that require more focus, the team of data scientists must have a good understanding of business processes, knowledge regarding the potential markets, and information about the regulatory landscape within which the business operates.
- Acquisition of data: Machine learning has the ability to consume an infinite amount of data from unlimited resources. The better the quality of input data, the better will be the quality of the model. Automated machine learning assists the data scientist in the procurement of high-quality data sources from both outside and within an organization as well as accelerate the process.
- Feature Engineering: The third step in machine learning is all about providing structure to the input data. The correctly structured data helps in the creation of an effective model and optimize its hyperparameters. The structurization of data into a precise format is known as feature engineering. Feature engineering helps the data scientist to curate the data in such a way that it becomes usable for the model. The automated machine learning automates the process of feature engineering that allows the data scientist to divert their attention to other important tasks. It is one of the most essential steps to help organizations to produce better results in model development.
- Evaluation of Model and Business Impact: Last but not least, this step ensures that the right model is selected for the given set of data. To ensure that everything falls at just the right place, the data science team must evaluate the model and validate it to make sure that it brings the expected results. It is also crucial for the data scientists to monitor the model to ensure that it works as per the requirements to achieve the business objectives. For effectively evaluating the model, the team of data scientists should have a close engagement with the business for identifying metrics that have organizational value.
Automated machine learning has become a crucial part of the data-driven organizations as it helps the data scientists to automate the cumbersome and repetitive process of selecting the right model and optimizing the hyper-parameters. The technology is shaping the future of data science and helping the data scientists by helping them to divert their focus on the important part of the data aggregation services. By moving their attention to other processes in the data science life cycle, the data scientists can enhance their skills and further accelerate the process of big data aggregation.