Automation: What Does That Mean for Machine Learning and Data Science?

Predictive and prescriptive analytics is essential for business success in digital transformation. As a result, businesses are attempting to glean various insights from data, particularly Big Data. Data scientists with expert-level expertise in artificial intelligence (AI) and machine learning (ML) techniques are in high demand to complete the task of value extraction from Big Data. Nevertheless, there aren't many of these expensive, highly qualified professionals.

Here, automation has a significant impact. We can do routine and complicated tasks more quickly by automating machine learning. Most tasks that used to require skilled data scientists to do can now be completed by AutoML (automated machine learning).

As a result, businesses may engage these data scientists for more creative tasks that require human intellect. So, while AutoML technologies do not completely replace data scientists, they assist in offloading some of their everyday chores. Before moving onto the details of Automation, do check out the IBM data science course in Pune, if you want to become a data scientist from the ground up. 

Data Science (AI) Life Cycle Automation

Data science and machine learning are fields where automation is constantly changing. The various tasks covered by the data science life cycle include ML at some point in the process. Automation has been used at various phases in developing AI solutions. Data scientists are in charge of finishing every phase of the AI model's life cycle.

• Data cleansing: Gathering pertinent data is the initial stage in developing any AI system. There are a number of sources where you can gather this information. Hence, a data scientist's primary responsibility is to clean and prepare the data. The cleaning phase entails formatting, error correction, and data preparation as necessary. Cleaning equipment is utilized to automate the procedure.

• Data visualization: In the life cycle of data science, data visualization is a crucial component. Here, the data is represented visually by graphs, charts, and other elements. The creation of components is automated using visualization tools. Since the data scientists are still responsible for the analysis, this process is only partially automated.

• Model Building: The model construction phase can be totally automated. Validation, tuning and choosing the most optimized model benefit greatly from using autoML tools. These models are quite effective and deliver precise results.

• Continuous monitoring: All AI models require ongoing maintenance and monitoring once deployed. These regular maintenance procedures are necessary to guarantee the model's accuracy over time. A suitable retraining procedure is also set up to maintain and boost the output's accuracy. The usual tasks are also carried out here by automated tools, but humans are still kept informed and can intervene if necessary.

We can see that several steps in this process are only partially automated because further result interpretation requires human intelligence. Most monotonous and time-consuming tasks are completed by automation.

Automated Machine Learning (AutoML)

You might have heard of AutoML before but what exactly is AutoML? 

Automated machine learning (AutoML) refers to a collection of programs and libraries. The model selection process is automated using these tools and libraries. Organizations looking to extract the most value from a given data collection frequently use AutoML. Thus, AutoML is now a necessary component of every data science effort.

AutoML Tool

Automation aims to provide effective results by completing repetitive operations quickly and effectively. The aim of AutoML is comparable. The selection of the data science model is sped up using AutoML tools and platforms. From a specific collection of facts, it creates the best model possible.

Are Careers for Data Scientists in Danger from AutoML?

The simple answer is "NO," AutoML was not designed to take the data scientist role. The why is now the question. We must have a basic understanding of the machine-learning pipeline to respond. The four phases of the machine learning pipeline are as follows:

• Data gathering.
• Preparation of data.
• Modeling.
• Deployment.

Some time-consuming and repetitive activities in the ML pipeline are automated using AutoML. Let's investigate which particular components are automated.

Data preparation is the first stage of the process that is automated. The process of preparing data is time-consuming and occasionally repetitious. AutoML frameworks aid in data cleaning, formatting, and processing.

The modeling stage is the second to be automated. The majority of AutoML tools are only used during the modeling stage. An ML pipeline's models each have a unique collection of hyperparameters. AutoML takes care of the necessary performance optimization and provides the best model with the most appropriate set of parameters.

This knowledge leads us to the conclusion that AutoML will not replace positions for data scientists. Instead, it exists to aid in accelerating some ML pipeline steps. Therefore, data scientists can focus on jobs with high value and adjust their skill sets accordingly. You just have to keep upgrading your skills and tools. To learn more about the latest ML tools, head to a comprehensive machine learning course in Pune now! 

Conclusion

In complicated business processes, data science, AI, and ML play a crucial role. Yet, developing a viable AI solution is difficult, given the time and money required. Building AI apps has been simpler with the evaluation of automation technologies. Combining AI with AutoML and RPA can be a successful strategy for the commercial sector.