Best Practices with AWS Machine Learning

Simplifying Machine Learning Processes with SageMaker

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.

a customer interacting with an AI chatbot.

AWS Machine Learning Best Practices

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:

  1. Establish what the problem is and what success looks like

  2. Acquire relevant and accurate data

  3. Move the data through the kernel of the database instead
    of exporting it

  4. Perform thorough testing

  5. Don’t drop any seemingly unnecessary data while
    training the machine learning algorithm

  6. Deploy and automate

  7. Evaluate success with metrics

How Machine Learning is Transforming the Insurance Industry

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:

a customer interacting with an AI chatbot.

Claims Processing

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.

AI Chatbots

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.

How Trianz Can Help You Implement Machine Learning on AWS

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.

Trianz is an Advanced AWS Consulting Partner.

Experience the Trianz Difference

Trianz enables digital transformations through effective strategies and excellence in execution. Collaborating with business and technology leaders, we help formulate and execute operational strategies to achieve intended business outcomes by bringing the best of consulting, technology, and execution models.

Powered by knowledge, research, and perspectives, we enable clients to transform their business ecosystems and achieve superior performance by leveraging infrastructure, cloud, analytics, digital, and security paradigms. Reach out to get in touch or learn more.


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