What is ML?
Machine learning is the process of analyzing a volume of data by an algorithm with the ability to make decisions based on previous experience. Machine learning is the process of analyzing a volume of data by an algorithm with the ability to make decisions based on previous experience.
The capitalization of the implementation of machine learning in business is growing by more than 44% every year. The dynamics of such growth is justified not only by the involvement of large companies in the development, but also by the availability of this technology for businesses of any size. And what exactly can machine learning give for a business?
- Automation of routine processes.
- Partial or complete replacement of the human resource.
Machine learning services greatly enhance the capability, flexibility and resiliency of any enterprise. Forward-thinking organizations choose this technology to drive holistic growth, high employee productivity, and customer satisfaction.
MLOps technology benefits
Many businesses achieve success through the use of machine learning in just a number of areas, and this is just the beginning of the process. Initially, there will be many experiments with such technology, but then it will be necessary to integrate ML models into business applications and processes to enable this technology to scale throughout the enterprise.
For enterprise-wide integration, many organizations do not have the right skills, processes, and tools. To successfully use this technology at full scale, companies must invest in MLOps solutions that include processes, tools, and technology that streamline and standardize every stage of the ML lifecycle, from model development to practical application. The emerging direction of MLOps will bring flexibility and speed to the ML life cycle. The value of this technology is comparable to the value of DevOps for the software development life cycle.
To move from experimentation with this technology to full application of this technology, enterprises need reliable and efficient MLOps processes. MLOps not only provides organizations with a competitive edge, but also enables other use cases for machine learning. This results in other benefits as well, including building a pool of effective and talented people through improved skills and a more productive collaboration environment, as well as higher profits, better customer service, and faster revenue growth.
The benefits for enterprises
In vertical industries, such technologies and methods are being successfully deployed, providing organizations with tangible and real results.
For example, in the financial services industry, banks can better identify and meet the needs of their customers using predictive ML models that take into account huge amounts of interconnected measurements. Predictive ML models are also capable of identifying and limiting risks. Banks can detect cyber threats, track and capture customer fraud, and predict risks associated with new products. The top three use cases for machine learning are fraud detection and mitigation, personal financial advisory services, and credit rating and credit analysis.
Companies in the manufacturing industry are adopting automation extensively and are now equipping equipment and processes with the necessary tools. They use machine learning modelling to reengineer and optimize production so they can quickly meet demand and respond to future changes. The end result is a flexible and fault-tolerant manufacturing process. The top use cases for ML identified in the manufacturing industry include performance enhancement, root cause analysis, and supply chain management.
Retail has a lot of customers: frequent or random shoppers, those under 20 and over 40 who work as teachers and lawyers. And attempts to describe them with two or three business rules lead to errors. For example, you can miss out on a client who spends a significant amount of money every month, simply because she is young and does not fit into the rules. Therefore, retailers strive to increase the accuracy of segmentation, but this also means the complexity of the model. And this is where ML comes in handy: it improves the accuracy of forecasts and allows you to answer pressing questions.
Warehouse Planning – Sales Forecasting
For example, you can predict purchases in a particular store – the model will show what will be bought in it in the near future. Then the store administrator will be able to order the right product from the warehouse in time. Analysis of purchases in a particular outlet will help to form a display of goods. So, if a lot of male customers come to the store, the department with men’s products should not be placed in the far corner.
All in all, ML can drive economic growth by increasing and replacing labour and capital costs, spurring innovation and wealth creation, and reinvestment.
