Machine learning has always been one of the biggest advances in computing history and is now considered to play an important role in big data and analytics. Big data analytics is a huge challenge from a business perspective. For example, activities such as understanding a large number of different data formats, analyzing data preparation, and filtering redundant data can consume significant resources. Recruiting Data Scientists experts are an expensive proposition, not a means for every company. Experts believe that machine learning can automate many of the tasks associated with analytics—whether it's routine or complex. Automated machine learning frees up a large amount of resources that can be used for more complex and innovative work. Machine learning seems to have been moving in this direction.
Automation in the context of information technology
On the IT side, automation is the connection of different systems and software, enabling them to do specific work without any human intervention. In the IT industry, automation systems can perform simple and complex tasks. An example of a simple job might be to integrate a form with a PDF and send the document to the correct recipient, while providing off-site backups can be an example of a complex job.
To do your job well, you need to program or give clear guidance to your automation system. The program or instruction set needs to be updated by people each time an automated system is required to modify its scope of work. Although automated systems are effective in their work, errors can occur for a variety of reasons. When an error occurs, the root cause needs to be identified and corrected. Obviously, to do your own work, the automation system is completely dependent on humans. The more complex the nature of the work, the higher the likelihood of errors and problems.
Typically, regular and repeatable jobs are assigned to the automation system. A common example of IT industry automation is the automated testing of web-based user interfaces. Test cases are fed into the automation script and the user interface is tested accordingly. (For more practical applications of machine learning, see Machine Learning and Hadoop in Next Generation Fraud Detection.)
The argument for automation is that it performs routine and repeatable tasks and frees employees to do more complex and creative tasks. However, it has also been argued that automation has ruled out a large number of jobs or roles previously performed by humans. Now, as machine learning enters various industries, automation can add a new dimension.
The future of automated machine learning?
The essence of machine learning is the ability of the system to continuously learn from the data and evolve without human intervention. Machine learning can act like a human brain. For example, the recommendation engine can evaluate the user's unique preferences and tastes on an e-commerce site and provide recommendations that best suit the user's choice of products and services. Given this capability, machine learning is considered an ideal choice for automating complex tasks related to big data and analytics. The main limitations of traditional automation systems have been overcome and human intervention is not always possible. A number of case studies have shown that machine learning can perform complex data analysis tasks, which will be discussed later in this article.
As already pointed out, big data analytics is a challenging offer for businesses that can be partially licensed to machine learning systems. From a business perspective, this can bring many benefits, such as releasing data science resources to get more ideas and mission-critical tasks, higher workloads, less time to complete tasks, and cost-effectiveness.
In 2015, MIT researchers began researching a data science tool that could create predictive data models from large amounts of raw data using techniques called deep feature synthesis algorithms. The algorithm claimed by scientists can combine the best features of machine learning. According to scientists, they have tested three different data sets and expanded the test coverage to more data sets. Researchers James Max Kanter and Kalyan Veeramachaneni said in an article that they will deliver a speech at the International Conference on Data Science and Analysis. "Using the automatic adjustment process, we optimize the entire path without human intervention, enabling it to generalize to different data. set".
Let's take a look at the complexity of the task: the algorithm has a feature called auto-tuning that allows you to get or extract insight or value from raw data such as age or gender, and then create a predictive data model. The algorithm uses a complex mathematical function and a probability theory called Gauss Copula. Therefore, it is easy to understand the complexity that an algorithm can handle. This technology also won the competition awards.
Machine learning can replace jobs
Being discussed around the world, machine learning may replace many jobs because it performs tasks with the efficiency of the human brain. In fact, machine learning will replace data scientists with some concerns that seem to have the basis for such concerns.
For ordinary users who do not have data analysis skills but analyze the needs to varying degrees in their daily lives, it is not feasible to use a computer that can analyze huge amounts of data and provide analytical data. But natural language processing (NLP) technology can overcome this limitation by teaching computers to accept and process human natural language. In this way, ordinary users do not need complex analysis functions or skills.
IBM believes that the need for data scientists can be minimized or eliminated through its Watson Natural Language Analysis platform. According to Marc Atschuller, Watson's vice president of business analysis and business intelligence, "With a cognitive system like Watson, you are just asking your question - or if you have no problems, you just upload your data, Watson can Look at it and infer what you might want to know."
Automation is the next logical step in machine learning, and we've had an impact in everyday life - e-commerce sites, Facebook friends suggestions, LinkedIn web recommendations and Airbnb search rankings. Considering the examples given, there is no doubt that it can be attributed to the output quality produced by the automated machine learning system. For all the qualities and benefits, the idea of machine learning causing huge unemployment seems to be a bit overreacting. Machines have replaced humans for decades in many parts of our lives, but humans have evolved and adapted to maintain industry relevance. According to the point of view, machine learning is just another wave that people will adapt to for all its destructiveness.