In modern times, Amazon is everywhere!!! And while Machine Learning has long been a part of Amazon, now it seems that ML is everywhere! From Amazon Alexa to Amazon AWS to even Amazon Prime, everything uses Machine Learning. And these are only the more common items! In fact, Amazon is heavily invested in Machine Learning Research in almost all imaginable fields like Ethical Principles, Quantum Computing, Healthcare, Robotics, Perception, Cloud services, Virtual Assistants, etc.
Amazon uses machine learning in a variety of ways to improve its products and services, and to gain a competitive edge in the market. Here are a few examples:
- Personalized product recommendations: Amazon uses machine learning algorithms to analyze customer behavior, browsing history, and purchase history to provide personalized product recommendations. This helps customers find new products they may be interested in and increases sales for Amazon.
- Fraud detection: Amazon uses machine learning to detect and prevent fraud on its platform. By analyzing patterns of behavior and transaction data, Amazon can identify suspicious activity and take action to protect both customers and merchants.
- Supply Chain Optimization: Amazon uses machine learning to optimize its warehouse operations and logistics. By analyzing data on customer demand, inventory levels, and shipping routes, Amazon can improve efficiency, reduce costs and delivery times.
- Chatbot development: Amazon uses machine learning to develop chatbot systems that can answer customer questions and help them navigate the website. This can reduce the workload of customer service representatives and improve the overall customer experience.
- Predictive Maintenance: Amazon uses machine learning to predict when machines in its warehouses and other facilities need maintenance, which helps to minimize downtime and keep operations running smoothly.
- Image and Voice Recognition: Amazon uses machine learning to improve the performance of its Alexa voice assistant and image recognition in its Rekognition service.
Amazon is such a wide company with over a trillion dollars in net worth. To manage and scale all of its businesses in various fields like shipping of products, services, online streaming and services over the Internet, and cloud management also, Amazon uses machine learning and artificial intelligence in its services so that it can meet up with the expectations of the consumers and also it can enhance the quality of its services, by looking upon the feedback and reviews given by the customers. Amazon uses the large amount of data stored in its databases over the cloud about information related to customers and provides it to feed the data into the machine learning algorithms so that they can drive some meaningful decisions and analysis from it which will further increase its business.
Now we will learn about How Amazon uses Machine Learning techniques and algorithms to drive and enhance its business and user experience for the users. But before that, you should know what exactly is Machine Learning.
What is Machine Learning?
Machine Learning is the process of analysis and study of the data statistics and analytics which can provide raw sources of data to identify the common usage patterns of the users and collect this information to deliver their services to the appropriate users by making effective business decisions. Machine Learning is a part of the Artificial Intelligence (AI) that allows the computer to learn from its experience of the machine based on the data and figures that it has processed from the past, and based on that data it continuously learns new skills and abilities to improve its performance even further. The machine learning technology doesn’t need to be programmed separately but it only requires a large set of databases to continuously learn and improve from it.
Machine Learning is applicable in vast areas of artificial intelligence like image recognition, speech recognition, prediction analytics, statistics, etc. The computer systems deployed with these machine learning algorithms are more intelligent and powerful because they can learn on their own without the need to be programmed according to the user’s adaptation. Machine Learning algorithms require a large number of databases to learn from that data and apply their analytical things to different use case scenarios in the business.
For now, let’s see some of the ways in which Amazon currently uses Machine Learning so that we can understand the full scope of its applications in the future.
How Machine learning helps Amazon?
These are the following ways in which Machine Learning helps Amazon to enhance the user experience and its recommendation systems:-
In the E-commerce market considered to be Amazon, we have millions of users across the globe that have very different choices and an unlimited range of interests in various products considering to be the brands, prices, sizes, shapes, colors, etc. So, Amazon helps to store all the data in the database that their users search, for the Machine Learning algorithms to learn from that data in the database. Machine Learning uses the purchasing history and pattern of the users and then relates to the fraud practices being carried out. Additionally, it provides specific targeted ads and recommendations to the users based on tailored promotions of different types of electronic brands.
Amazon can collect all the usage patterns and the search history of the users to recommend the products and show them similar types of products to meet up with the forecast demand of these products and services. Machine Learning plays an important role in this task as it would be so difficult if manual employees had to do this task, but Machine Learning learns from that data in the database and applies accordingly in their recommendation systems. The data scientists and researchers build and deploy high-scale machine learning algorithms that learn from a large amount of data with scalability and reliability to perform the tasks automatically on their recommendation systems and targeted advertisements business.
When a large number of users search for a particular product on Amazon at a given particular time then, it starts to be a trending position in its ads, their recommendation systems, and the Facebook and Instagram ads that they show to their users, a search engine of Amazon and various other web searches like Google, Bing, etc. It uses Statistical Machine Translation (SMT) which is a fancy way of saying that it analyses millions of documents for search-related queries. It uses SEO techniques to rank at the top in order to improve its productivity. If you want to know more about SEO, read How to Become an SEO Expert?
3. Amazon SageMaker
Amazon SageMaker is another step for Amazon toward more machine learning-based business practices. It uses terabytes of data from the Amazon databases and helps them to eliminate all the waste of packaging that is done by predicting the user buying forecast and then identifying which of the smaller products can be shipped in a small paper bag and require less packaging in order to reduce all the waste packaging. The Amazon SageMaker helps to gain insight data from the various streams Amazon warehouses present in different locations that basically tell how much packaging is right for each product and service and what amount of packaging material should be used so that it doesn’t harm the environment.
4. Amazon Prime Membership
The Amazon Prime video serves the users with appropriate recommendations of the web series that they would love to watch. It takes the data from the user’s Amazon prime account and then collects and makes a study on all the analytics and statistics to consider recommending the web series to the user based on his previous viewing and retention history. The same things happen with the Amazon Prime music services as well, users get recommended for the similar type of music that they love to hear based on their history and tailored experience. Amazon also runs various other services apart from E-commerce which also includes the Prime membership of Amazon that serves movies, songs, and fast delivery. Machine Learning also takes a leading role in those services.
For Example, if a user loves watching Fictional English movies that contain criminal, dramatic scenes and are based on some true events, then the Amazon prime service will serve the users with the appropriate recommendation of those types of shows based on these categories of the user on his viewing history and his usage. Similarly, Amazon uses the same for music. If a user loves to hear pop English songs of some singer, then he will be recommended with more songs of that type by Amazon Prime Music.
Alexa is also a part of Amazon which uses Machine Learning to predict what the user will ask the information for and then based on that gives a rich user experience when the user asks the question. Alexa also uses Artificial intelligence that improves speech recognition and helps to collect all the voice data models for regression and data analytics purposes. Data and machine learning used by Amazon Alexa play a major role in the working of its smart Natural Language Processing (NLP) and Natural Language Generation (NLG) which understand the human language and then reply to them appropriately according to the tone of the user and the context he is referring to it. Alexa Voice Services (AVS) is basically a machine learning set of algorithms that power the Amazon Alexa that are present all over the world and they regularly get updated for providing the latest information. Also, Amazon Alexa is capable of answering the questions in the voice of famous celebrities with the help of deep learning technologies.
- Amazon’s AI capabilities are designed to provide the customers with the most accurate and targeted recommendations of products. These machine learning algorithms are capable to contribute to almost 40% total business of Amazon by giving personalized buying recommendations to its customers based on their web-behavior.
- Amazon SageMaker is another groundbreaking machine learning tool developed by the ML team in the AWS department. The Amazon SageMaker is responsible for caring for the environment by choosing the most effective sized cardboard packaging for its delivery to its customers. It helps to make more sustainable packaging decisions while also meeting with the good quality of packaging.
- The machine learning algorithms in Amazon are built with several terabytes of data in mind from the various databases that store the data. This data is organized and filtered and provided to the various departments to analyze the need of the hour and meet up with situations of critical business hours when the customers order a lot at the same time.
- Amazon has developed an “Intent-based router” built using machine learning algorithms where the complaints and reviews of the customers are organized and segregated based on the expression and emotion that their complaints are showing. For example – the complaints which are less rude are resolved using the replacement of the product and which are more aggressive require the company to pay some extra credits to the customer for the losses they have faced.
Amazon is a large trillion-dollar company that mainly focuses on Machine Learning and Artificial Intelligence to serve its users and also provides a handy user-friendly experience tailoring customized recommendations and targeted ads based on their tastes in products shopping, movies, music, etc. Machine Learning helps it to reach a large number of people thereby, increasing its reach and making it more helpful by identifying users’ patterns and making decisions based on them without any human intervention. Amazon looks forward to developing more personalized digital shopping assistants for its customers to enhance its wide reach to the consumers using machine learning techniques. Hence, we have learned all about various techniques and methods through which Amazon uses Machine Learning and artificial intelligence to scale up its business to meet up with the expectations of the customers.
HOW TO FACE THIS CHALLENGE :
There are several ways to address the challenges associated with the use of machine learning by companies like Amazon:
- Transparency: Companies should be transparent about how they are using machine learning and the data they are collecting. This will help customers and regulators understand how their data is being used and make informed decisions about whether to use the company’s products and services.
- Regulation: Governments should consider implementing regulations that set standards for the use of machine learning and protect the rights of customers and other stakeholders. This could include regulations around data privacy, data security, and ethical use of machine learning.
- Ethics: Companies should establish ethical guidelines for the use of machine learning, and ensure that their technology is developed and used in a way that respects the rights and interests of all stakeholders.
- Auditing: Independent auditing of the machine learning models and processes can be done to ensure that the models are unbiased, fair and respects the privacy of individuals.
- Education: Increasing education and awareness about machine learning and its potential impact will help customers and other stakeholders understand the technology and make informed decisions about its use.
- Collaboration: Collaborating with researchers, academics, and other stakeholders can help companies identify and address the challenges associated with machine learning, and develop solutions that benefit all stakeholders.