Artificial intelligence & machine learning has promised to provide new heights of success in all the fields. This is the reason why several businesses are keen to use these technologies for their success and growth. However, both of these technologies require expert data scientists, researchers, programming experts and engineers but currently, as per the demand there is a huge shortage of skilled experts. This is where auto machine learning becomes important for businesses from all over the world.
The ability of automated machine learning for automating some or the majority of the long, time-consuming tasks related to machine learning compensates for the lack of artificial intelligence or machine learning experts. It not only decreases the shortage gaps but also boosts the productivity of the current data scientists throughout the world.
It promises to automate all the major repetitive tasks such as data prep, feature selection or choosing the data sources. This allows business analysts and marketing experts to focus more on other essential tasks. Now data scientists can create more models in less time with improved quality & accuracy of all the models. They can also fine-tune several more new algorithms.
How current Auto Machine Learning Tools can negatively impact Business?
As per the Gartner, it is predicted that by the year 2020, more than 40 percent of all the tasks related to data science can be automated. This automation, will, of course, lead to higher productivity of data science and machine learning experts. This will also cause a broader use of data or analytics.
As of now, the majority of the auto machine learning tools focus more on building the models rather than complete automation of a specific business function like marketing analytics, customer behavior or any other customer analytics. This may be the problem for various businesses, as it will again cost extra time and money for businesses.
Moreover, many current auto machine learning tools do not address various problems like feature engineering, data unification, data selection, and continuous data preparation. Knowing about all the large volumes of data, its use and identifying the non-obvious patterns are still a major challenge for all the experts globally. Often, they are not ready to properly analyze real-time streaming data. And if the data are not analyzed effectively then, it can result in flawed results, analytics and patterns. It may lead to ineffective business strategies that may cause poor decision making and business loss.
Auto Machine Learning for Model Building Automation
Today, there are numerous companies that are using automated machine learning for automating internal processes. It is used especially in building machine learning models. Some of the big names that are using automated machine learning for automating the process of building machine learning models are Google and Facebook.
Every month, Facebook tests and train thousands of machine learning models (about 30,000). For dealing with so many models, Facebook has built an assembly line and they have even created their own Auto machine learning engineer called Asimo. This auto ml engineer can automatically generate the improved versions of present machine learning models.
Now coming to Google, they are developing different auto-machine learning techniques that can automate the whole design of the machine learning models. Not only this, but it can also automate the process of finding different optimization methods. Currently, the company is on its way to develop the process for machine-generated architectures.
Auto Machine Learning for the Automation of End-to-End Business Processes
Some of the companies that are successfully and actively using auto machine learning for the entire automation of specific business processes are ZestFinance, Zylotech and DataRobot. In business, once the problem is defined then the machine learning models are built and it is very much possible to automate the whole business processes.
DataRobot is designed in such a way that it can automate the whole automation of predictive analytics. It can automate all the modeling lifecycles including data ingestion, transformations and algorithm selection. This platform is not only limited to these applications and can be used for others as well. Furthermore, DataRobot is also customizable that helps users for specific deployments like high volume predictions and building a large variety of different models. With this, experts can easily and quickly build models and even apply algorithms for making a predictive analysis.
Coming to ZestFinance, it is a platform that is designed for automating the whole process of specific underwriting tasks. Some of its applications are the automation of model training and deployment, explanations for compliance and data assimilation. This platform uses machine learning that analyzes the non-traditional and traditional credit data for scoring the potential borrowers having thin or no files. It can also be used for training or deploying machine learning-based models for specific applications like marketing, fraud prevention and others. ZestFinance helps all the financial analysts or creditors for taking the right lending decisions and risk assessments.
Zylotech platform is designed for automating the entire customer analytics. It includes an EAE also called embedded analytics engine along with several auto machine learning models that automates the whole process of machine learning for customer analytics including feature engineering, unification, data prep, model selection and discovery of non-obvious patterns. With this platform, you or any data science and machine learning expert can leverage the complete data in real-time that also enables one-to-one customer interactions.
How is Auto Machine Learning helping Businesses to use and implement machine learning successfully?
Most of you are familiar with the phrase “data is the new oil”, but now, it seems that data is far more valuable than oil or even gold. Nonetheless, just like crude oil needs processing for its application in the real-world, data must also be refined and cleaned after which you can draw insights from the embedded models for making the right decisions for your business. At first, no data is valuable as it must be cleansed, enriched and analyzed after the collection.
The approach of using auto-machine learning can help all businesses use machine learning effectively and successfully. It is only machine learning that has the potential to draw effective business insights from the data. So, in the end, no matter whether you are in healthcare, marketing, retail or any industry, it is auto machine learning that you need to leverage the valuable resources from the extracted data.
There you go! This was auto machine learning. I hope that now, some of the essential aspects of machine learning are cleared. It may be the new approach or still in the development phase, but it promises great potential to all the amateurs and even the experts to use machine learning effectively without any hassle.
Lastly, no matter what, there are several companies including the big ones who are using this concept and various platforms based on this concept in different industries including business.