Amazon Web Services(AWS) has drastically changed the cloud computing landscape. It has become the world’s largest cloud computing service overtaking giants like Oracle, Microsoft etc. It has become the cash cow for amazon making billions of dollars of profit each year. It has a provided lots of small and large companies with a comprehensive enterprise cloud solution, they have created a complete secured cloud services platform offering its customers with facilities of computing power, database storage, content delivery and many other functionalities to help any business scale and grow in simpler terms we can say that AWS allows you to run web applications in the cloud and host dynamic websites, also they allow you to securely store all your files on the cloud and access them anywhere and manage them using databases. A great application for the content delivery platform has been using to its content delivery network all of these basic functionalities of AWS have made them very popular with large and small institution alike. To improve their product even further AWS has started to offer the deepest and broadest suit machine learning and AI services with the supporting cloud infrastructure. Putting the power of AI and machine learning in the hands of every data scientist developer and practitioner. It has helped a lot of its customers to get started and improve upon their machine learning skills also the businesses have benefited hugely to improve their products and other services.
The Amazon Web Services machine learning and artificial intelligence infrastructure have been a cost-effective, scalable and high performing solution to its customers, they have created various business case solutions for the customers using their services some of the key use cases for these solutions have been optimizing business operations and improving customer experience through features like:
Intelligent contact centres: This feature is used by organizations to personalise the customer service interaction for every customer this way the customer satisfaction improves and we can also get better visibility for important business matrix. Organisations can deploy AI and ML services to create and use intelligent chatbots, real-time voice analytics with sentiment analysis, assist the agent with the best course of action provides smart routing for calls and messages and finally provide post-call analytics.
The benefits of such an arrangement are improvement in agent efficiency by the reduction of call volumes helping solve the customer issues quickly through the use of solutions provided by AI and offload repetitive issues to a chatbot. Another benefit is improvement in customer satisfaction through a drastic reduction in call weight times and resolution of their issues by the agent. The third benefit of this service is the reduction in cost and identification of new avenues for business through the capture of more data to better the product itself.
Personalization: With the improvement in web technologies and the ability to deliver more sophisticated digital experiences for the users it has become more of a norm for the customer to expect a certain amount of personalized experience from each brand. Every brand is expected to provide relevant choices to the customer from the millions of different choices they find online. A customer is more likely to engage with the brand if their needs are met this is the case across all the sectors be it media, entertainment, hospitality, travel, retail etc. Customers today expect curated experience across all digital channels, machine learning is a great tool to provide said personalized experiences through creating detailed models of customer this interaction results in the improvement of customer engagement, conversion, margin, revenue. Their service is also integrateable with any existing website, app, email marketing system. This gives the organization a great personalized product for its customers.
Intelligent document processing: Organizations in various industries such as legal services, healthcare, financial services etc have a requirement of processing a large number of documents. These documents can be legal notices, contracts, patient forms, loan application, invoices etc containing a lot of essential information for business processes. These documents may contain applicants names, entities, history of payment or health records, using automatic data extraction and analysis, using ml and ai AWS can extract and process millions of documents and understand the relationship between the documents both internally and externally. Therefore, this increases the speed and efficiency of processing data on the documents. The task which use to take over months of work could be completed within days, this feature also presents a lot of cost savings and improves employee productivity by enabling them to spend time on tasks that add business value.
Intelligent search: The information that exists on a companies servers and various other disjointed forms of data storage need to be organized and used by the employees to find useful documents or information within the piles of data not only is it hard to find the required documents on time but often other searching tools that don't employ AI can be fairly outmatched by the request made by the workers. AWS offers amazon Kendra as an intelligent search service powered by machine learning, it uses natural language search capabilities to return accurate content from unstructured data.
This feature boosts the workforce productivity as it unlocks the insights buried in the data and is especially useful for data scientist or ML practitioners to go through a huge pile of data used for training models. This also helps the customers of the organization to find what they need faster and easier.
Fraud Detection: Fraud can be very costly for businesses to manage and might even cause billions of dollars and damages to the companies bottom line. AWS uses machine learning and uses a service called Amazon Fraud Detector which then fully manages the time consuming and expensive fraud detection. This detector automates the process of building training and deploying a machine learning model for fraud detection which saves a very large upfront expense of hiring a team or using old generation fraud detection since it uses machine learning and it is fed with the historical data of that particular organization it provides a good fraud detection with a low false-positive rate which suites better than a one-size-fits-all model.
Business Forecasting: Ever since the advent of spreadsheet software paired with data collection there has been a huge demand for prediction and forecasting of business metrics companies nowadays use complex financial planning software to forecast future business outcomes such as financial performance, bottom-line growth, growth in demand, the requirement of supply etc. These tools are usually built on time series data and which is historic series of data, but these kinds of predictions solely depend on the assumptions that the past would look like the future. It fails to combine data series over time and doesn't take into account the new condition setup. Amazon web services have made a tool called amazon forecast which uses machine learning to combine different data streams with the historical time series analysis and gives a better forecast based on the data provided. This technique has made amazon forecast up to 50% more accurate than just time series data forecasting alone.
Amazon Sage Maker Studio: Amazon Sage Maker Studio is a comprehensive machine learning service that has inbuilt tools to help developers data scientist and other practitioners prepare build, train and deploy high-quality machine learning models. It is full of features such as labelling, statistical biased detection, training, hosting, tuning, feature engineering etc. It is an integrated development environment specifically designed for machine learning. It is a tool that can greatly reduce cost for spot training data labelling and drastically improved team productivity it supports various other frameworks such as PyTorch, TensorFlow, Mxnet etc. It helps to deploy the same model to the cloud.
Amazon Lookout for Vision: Amazon Lookout for Vision is a machine learning service that detects anomalies and defects using computer vision(CV). With this product, a manufacturing company can employ a camera and detect missing component in the product irregularity in the production line damage to the structure or minuscule defects in the production of silicon chips. This is a great product for dealing with the inconsistency in manual inspection and greatly improving quality control. This, therefore, increases the productivity of the production line decreases the operational costs and continuously improves its accuracy through feedback to become even better at anomaly detection even reroot production to decrease downtime.
Amazon CodeGuru: A developer after writing code has been in a situation where the output has been wrong just because of a small error in the code. This is a very tedious task to review every single line of code and debug the critical issues. This product by AWS can be used in the existing software development workflow to automate code review and continuously monitor the applications performance and production. Machine learning can be used to find hard-to-catch bugs during application development and improve code quality. It can also be used to identify security vulnerabilities and reduce the run time of an application by removing code inefficiencies and significantly decrease the compute cost.
Amazon has been continuously working on AWS and improving its performance reducing its computing cost and bringing in new features some machine learning features that can unlock the future potential of various industries are listed below:
Healthcare: Various new applications are being developed to unlock the healthcare data such as amazon health lake, amazon comprehend medical, amazon transcribed medical.
Manufacturing and another industrial sector: AWS has built various products to monitor the behaviour of machines find out abnormality in the production line, improve operations. Products such as amazon monitron, amazon lookout for equipment, AWS panorama.
Software and DevOps: New platforms and development environments are being built with embedded machine learning to improve DevOps and the quality of code products such as amazon DevOps guru, amazon code guru reviewer and amazon code guru profiler have been made to facilitate better software development.
Apart from all these AWS is also making it far easier to create machine learning models using amazon sage maker providing tools like amazon sage maker autopilot, amazon sage maker ground truth, amazon sage maker jump-start, amazon sage maker data wrangler, amazon sage maker feature store, amazon sage maker debugger, amazon sage maker clarify, amazon sage maker model monitor, distributed training, amazon sage maker edge manager, amazon sage maker pipelines.
All of these tools and development kits have made it easier than ever to develop machine learning models, use those models on the cloud, test them and deploy bringing out the potential hidden gems within the vast amount of data that is produced in today's day and age. In conclusion, AWS has been a great apparatus for Machine Learning and AI services. Bringing a vast array of tools for both enterprise and educational institutions to get the benefit of the full potential of artificial intelligence.