Automation: The Future of Data Science and Machine Learning?
Machine learning is the ability for a system to alter its own programming. But when a system can do this, are humans still necessary?
In the age of digital transformation, predictive and prescriptive analytics are key to business success. As a result, organizations are trying to extract many different types of insights from the data, specifically Big Data.
To accomplish the task of value extraction from Big Data, data scientists with 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 few in number.
Here is where automation plays an important role. Automating machine learning can help us to complete both routine and complex jobs more efficiently. AutoML (automated machine learning) can perform most of the tasks once performed by 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 is evolving continuously. The data science life cycle covers a wide 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 cleansing - To build any AI solution, the first step is to collect relevant data. These data can be collected from different sources. So, the basic task of a data scientist is to clean and prepare the data. The cleaning part involves formatting, removing errors and preparing the data as needed. 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 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 set up to maintain and improve the accuracy of the output. Here also, automated tools are used to do the routine jobs, although, humans are also kept in the loop with potential for human intervention when necessary.
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 organizations wanting to get the best possible result out of a given set of data. So, AutoML is now an integral part of any data science project.
Goal of AutoML
The general purpose of any automation is to complete repetitive tasks quickly and effectively, and to produce efficient results. The goal of AutoML is similar. AutoML tools/platforms are used to shorten the life cycle of the 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 most 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?
The clear answer is 'NO', AutoML is not made to replace data scientist jobs. Now, the question is why? To answer this, we need to understand the machine learning pipeline a bit. The machine learning pipeline mainly consists of four stages:
- Data collection.
- Data preparation.
AutoML is used to automate some of the tasks in the ML pipeline, which are time consuming and repetitive in nature. Let's explore which specific parts are automated.
The first stage that is automated is the data preparation part of the process. Data preparation takes a lot of time and can be repetitive in nature. AutoML frameworks help to clean, format and process the data.
The second stage that gets automated is the modeling stage. Most of the AutoML tools are used in the modeling stage only. Each model in an ML pipeline has its own set of hyperparameters. AutoML does the performance tuning involved and returns the best model with the most suitable set of parameters.
From this insight, we can conclude that AutoML is not going to replace data scientist jobs. Instead, it is there to help them accelerate some parts of the ML pipeline. So, the data scientists 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 business process requirements.
RPA – Combined with AI and ML
A robotic process automation (RPA) is not a new concept.
Process automation (PA) has been used for many years, but its implementation was limited to certain fields.
PA is mainly used for rule based, repetitive and mundane tasks, where less human assistance is required. RPA robots are not intelligent enough to process unstructured or semi-structured data. RPA systems are not built to have cognitive intelligence.
This is where the importance of AI bots comes into play. AI robots are designed and built to have cognitive abilities that emulate some of what human beings can do. AI bots can apply logic, solve problems and self-learn from experience. AI is also using machine learning, deep learning and natural language processing (NLP) to build robust systems that act more like human beings.
Currently, 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, the automation capabilities that are involved will increase exponentially. So, in the entire automation process, AI bots can be used where human-like intelligence is required (like when applying logic, making decisions or self-learning). The rest of the repetitive, mundane and rule based tasks can be a part of the duties 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 for repetitive and mundane tasks.
Reduce human errors.
Reduce organizational costs as the bots are low cost components.
Increase automation processes when combined with AI and ML.
Improve customer satisfaction.
Free data scientists to work on complex tasks.
Who is 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 that will need to be addressed as it gains a larger market are:
Scalability and management of bots.
Security and privacy.
RPA failure when there is a change in process.
Elimination of jobs currently held by humans.
The Future of RPA
The global automation market is going to scale up rapidly. RPA adoption as a part of this 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. AI combined with AutoML and RPA can be a winning strategy for the business world.
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