Amazon SageMaker is perhaps the most valuable tool for managing machine learning projects on AWS. This platform enables developers and data scientists to build, train, and deploy robust machine learning projects with ten times better-performing algorithms, 70% less cost, easy management, and one-click deployment.
The addition of Amazon artificial intelligence to machine learning projects is simple. It provides forecasting models, image and video analysis, advanced text analysis, document analysis, voice-to-text implementation, and more.
This manifestation of artificial intelligence is machine learning in action and a powerful reminder of the technology’s utility. The great news here is that the integration of AI requires no user knowledge of machine learning and can be implemented with the push of a button.
First, your organization needs to identify the problem and determine if and when the solution is found. Metrics, in this case, are a useful tool as the numbers and trends will illuminate the project’s status and end goal. Make sure you are only dedicated to parsing relevant resources and try to prioritize rather than collect and analyze every last piece of data. With machine learning, it is crucial to always move the algorithm instead of your data.
Working from within the model and algorithm reduces compute times and is a cleaner, more cut and dried way of running your data. Next, testing will allow you to provision additional edits to the application and overcome possible setbacks at launch.
In this time, AI tools will collect relevant info — some useful, some not. Make sure you do not fully discard information no longer determined as necessary, as this is still critical to the machine learning functions of the artificial intelligence.
It is at this point that those automated processes can be secured, and your product finally launched. Make your program smarter and quicker by not solely relying on your data scientists and engineers alone for insight. This is where customer feedback also educates the system on how to respond cleverly to customer needs, adapting itself accordingly. Lastly, metrics, as previously mentioned, will provide ongoing support in your mission to measure successes and failures as they arise.
The following are our best practices for managing machine learning projects on AWS:
Establish what the problem is and what success looks like
Acquire relevant and accurate data
Move the data through the kernel of the database instead
of exporting it
Perform thorough testing
Don’t drop any seemingly unnecessary data while
training the machine learning algorithm
Deploy and automate
Evaluate success with metrics
With the vast amount of data the insurance industry has at its disposal, machine learning can enhance its operations throughout the entire process lifecycle. Here are a few ways the insurance industry can use machine learning to transform its practices:
Dealing with thousands of claims and customer queries is a daunting, time-consuming task for insurers. Machine learning can significantly improve these processes, moving claims through the initial report, analysis, and contact with the customer more efficiently and with fewer errors.
With claims investigations becoming more accurate with the help of machine learning, the technology can spot red flags for false or illegitimate claims, meaning genuine claims can be processed, accepted, and resolved much more quickly.
For instance, AI can streamline the claims process by automating administrative tasks with AI software when a customer opens a claim. This cuts out some of the initial processing typically handled by managers, resulting in less intervention in the claims process.
The administrative savings made by streamlining the claims process can then be passed onto customers in the form of lower premiums or additional added value. This frees up your employee workforce to focus on more complex claims that require direct customer contact.
Since AI chatbots can review claims, check policy details, and perform fraud detection before sending instructions for the claim settlement payments, cycle times can be greatly reduced between the customer and insurer. This not only helps the customer experience but also allows for more accurate reporting.
The lifecycle of individual claims can also be updated in real-time as the chatbot responds, ensuring the continued flow of information as it is processed. Through chatbots, machine learning can reduce the impact of fraudulent claims and eliminate human errors and inaccuracies identified through data patterns using machine learning models.
Effective underwriting requires the processing of vast amounts of data to reduce uncertainties. Machine learning tools can help underwriters by combining insights from multiple sources of analyzed data. This allows for a more precise, data-driven approach to calculating premiums.
Machine learning tools can use predictive algorithms of past claim activities and data to identify future risk. The underwriter can utilize the information to translate these risk factors into a suggested premium based on all of the historical data analyzed with the least amount of uncertainty possible.
Nearly every insurance sector stands to benefit significantly from machine learning analytics, as advanced algorithms can explore a customer's lifestyle, risk factors, medical records, financial stability, and previous insurance claims to create a dynamic and accurate profile.
Trianz offers an Amazon machine learning service that can utilize the cloud for intelligent, flexible, robust application development and management. We are introducing this emerging technology and cognitive and robotic process automation and advanced analytics for the banking, financial, and insurance industries.
Our marketing analytics service leverages the best market assessment tools to gain insights from not only machine learning but pattern recognition, scenario/sensitivity modeling, statistical regression analysis, and more. Lastly, Trianz deploys machine learning for predictive analytics, which grants organizations the ability to foresee trends and problems in order to keep a competitive edge.
Rest assured, our best practices will always be in place as Trianz utilizes AWS, the cloud, artificial intelligence, and especially machine learning as it comes to managing your projects. With the wealth of emerging technologies spreading throughout the market and vastly differently enterprises, you can be confident in our ability to further streamline business processes and procedures to enhance the quality of your products and services.
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
The Cloud is the Key to Transformation Success… Transitioning your applications to the cloud is undeniably a critical factor to a successful digital transformation endeavor. It’s more than just a lift-and-shift, however. Let’s explore several things that you need to consider before migrating your applications to the cloud, including: Readiness of your application portfolio Where to begin – the right business case and migration strategy Technology requirements and considerationsExplore
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore
Container Orchestration or Compute Service? Amazon Web Services (AWS) offers a range of cloud computing services to meet enterprise needs. Included in its service offering is the elastic compute service (ECS) and elastic compute cloud (EC2). Choosing between these two services can be difficult, as one focuses on virtualization while the other manages containerization. In the following article, we will explore the differences between Amazon ECS and EC2 to help you better understand which service is right for your use case.Explore