Impact of Automation in Data Science and Machine Learning

Impact of Automation in Data Science and Machine Learning

Impact of Automation in Data Science and Machine Learning

Overview –

In the age of digital transformation, predictive and prescriptive analytics are key to business success.  As a result, organizations are trying to extract the extreme insight from the data(specifically Bigdata).To accomplish the task of value extraction from Bigdata, data scientists having expert level knowledge in artificial intelligence (AI) and machine learning (ML) tools are in great demand. But, these highly skilled experts are very costly and short in number.

Here automation plays an important role. Automating machine learning can help us to complete both the routine and complex jobs more efficiently. AutoML (automated machine learning) can perform most of the tasks once performed by the talented data scientists. As a result, the organizations can use these data scientists for more innovative jobs, where human intelligence is a must. So, AutoML tools are not a replacement for data scientists, but they help to offload their routine tasks.

In this article, we will be exploring the impact of automaton in the field of AI and ML.

Automation in data science (AI) life cycle

Automation in the field of data science and ML are evolving continuously. Data science life cycle covers a wider range of tasks, where ML is a part of the entire process. Automation has been implemented at different stages of AI solution building. Data scientists are responsible for completing all the life cycle tasks to build the AI model.

Let us explore the areas where automation has been implemented in the AI development process.

  • Data cleaning – To build any AI solution, the first step is to collect relevant data. Now, these data can be collected from different sources. So, the basic task of a data scientist is to clean and prepare the data. The cleansing part involves formatting, removing errors and prepare the data as per need. In this part, cleaning tools are used to partly automate the process.
  • Data visualization – Data visualization is a very important step in the data science life cycle. Here, the data is visualized by creating graphs, charts and other visual components. Visualization tools are used to automate the process of creating components. This step is also partially automated, as the analysis part is still done by the data scientists.
  • Model building – Model building part can be fully automated. AutoML tools are very useful for validation, tuning and selecting the most optimized model. These models are highly efficient and produce accurate output.
  • Continuous monitoring – All AI models need a continuous monitoring and maintenance after deployment. These routine maintenance activities are required to ensure the accuracy of the model over the time period. A proper retraining process is also setup to maintain and improve the accuracy of the output. Here also, automated tools are used to do the routine jobs. Although, human intervention is also kept in the loop.

In this process, we can find that some steps are partially automated as human intelligence is required to further interpret the result. Automation is mostly used to complete the time consuming and repetitive jobs.

Automated Machine Learning (AutoML)

  • What is AutoML?

Automated machine learning (AutoML) is a term used to define a set of tools and libraries. These tools and libraries are used to automate the model selection process. AutoML is widely accepted by the organizations to get the best possible result out of a given set of data. So, AutoML is now a integral part of any data science project.

  • Goal of AutoML

The general purpose of any automation is to complete the repetitive tasks quickly, effectively and produce efficient result. The goal of AutoML is also similar. AutoML tools/platforms are used to shorten the life cycle of data science model selection process. It produces the best model out of a given set of data.

  • AutoML tools and libraries

In the AutoML domain, lots of tools, libraries and platforms are available. Some of the popular tools are AutoKeras, Auto-WEKA and Auto-sklearn. Different cloud platforms are also available for managing the entire AutoML life cycle.  Some of the popular cloud platforms are Azure ML, Amazon ML, GCP and IBM Watson. These cloud platforms are also called Machine Learning as a Service (MLaaS).

Is AutoML a risk to Data Scientist jobs in the future?

The clear answer is ‘NO’, AutoML is not made to replace the data scientist job.  Now, the question is why? To answer this, we need to understand the ML (machine learning) pipeline a bit.  The machine learning pipeline mainly consists of four stages – data collection, data preparation, modeling and deployment. AutoML is used to automate some of the tasks in the ML pipeline, which are time consuming and repetitive in nature. Let us explore, which specific parts are automated.

The first stage, which is automated is the data preparation part. Data preparation takes a lot of time and repetitive in nature. AutoML frameworks help to clean, format and process the data.

The second stage, which is automated is the modeling part. Most of the AutoML tools are used in the modeling part only. Each model in ML pipeline has its own set of hyperparameters. AutoML does the performance tuning and returns the best model with most suitable set of parameters.

From this insight, we can conclude that AutoML is not to replace data scientist job. Rather, it is there to help them accelerate some parts of the ML pipeline. So, the data scientist can focus on the high value tasks and tune their skill sets accordingly.

What is Robotic process automation (RPA)?

Robotic process automation (RPA) is a very interesting area in the context of automation. RPA can be defined as an implementation of software technology, based on business logic and data input to automate repetitive high volume tasks. RPA can be used for both simple and complex tasks. These robots (RPA) should be designed carefully to meet the business process requirements.

RPA – Combined with AI and ML

A  Robotic process automation (RPA) is not a new concept. PA is there for many years, but its implementation was limited to certain fields only. PA is mainly used for rule based, repetitive and mundane tasks, where less human assistance is required. But, RPA robots are not intelligent enough to process unstructured or semi-structured data.  RPA is not build to have cognitive intelligence. Here comes the importance of AI bots.AI robots are designed and build to have cognitive abilities like human beings.AI bots can apply logic, solve problems and self-learn from experience(like human being).AI is also having machine learning, deep learning and NLP to build robust system which acts like human beings.

Now, RPA and AI can work in isolation and bring good benefits to the business process. But, if RPA and AI (with ML, NLP etc.) are combined together, the automation capabilities will increase manifold. So, in the entire automation process, AI bots can be used, where human like intelligence is required (like applying logic, taking decisions, self-learning etc.).And, the rest of the repetitive, mundane and rule based tasks can be a part of RPA bots. So, the combination of AI and RPA can bring revolution to the automation process. It will increase processing speed, productivity, efficiency and overall ROI.

Business benefits of RPA

Implementation of RPA along with AI/ML provides the following benefits

  • Reduce staffing costs in repetitive and mundane tasks
  • Reduce human errors
  • Reduce organizational costs as the bots are low cost components
  • Increase automation process combined with AI and ML
  • Improve customer satisfaction
  • Enable data scientists to work on complex tasks

Who are using RPA?

Some of the companies who are implementing RPA are – Walmart, AT&T, E&Y, Anthem, Deutsche Bank, Capgemini and many more. RPA is used across multiple domains like finance, banking, insurance, healthcare, telecom etc.

Challenges of RPA

Some of the challenges of RPA implementation are mentioned below.

  • Scalability and management of bots
  • Security and privacy
  • RPA failure when there is a change in process
  • Eliminate human jobs which are repetitive and tedious in nature

Future of RPA

Global automation market is going to scale-up rapidly in the coming days. RPA adoption as a part of automation effort is also going to increase significantly. The major driving factors are performance and cost savings. RPA in combination with AI/ML, NLP and BPM tools will surely give a tremendous boost to the hyperautomation effort.


Data science, AI and ML are playing a significant role in the world of complex business process. But, building a successful AI solution is challenging, considering the effort and investment. With the evaluation of automation tools, it is now become easier to build AI applications. So, the AI combined with AutoML and RPA can be a winning strategy for the business world.

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