Trianz: Best Practices with AWS Machine Learning

Amazon SageMaker is perhaps the most important tool for managing machine learning projects on AWS. This application enables developers and data scientists to build, train, and deploy robust machine learning projects with 10 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 and 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.

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

First your organization needs to identify what the problem is and be able to 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 not to collect and analyze every last piece of data—rather, you should prioritize. As they say, “move the algorithm instead of your data.” Working from within the model and within the algorithm reduces compute times and is a cleaner, more cut and dry 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 important, as this is still critical to the machine learning functions of the artificial intelligence. It is at this point that 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 more cleverly to customer needs, adapting itself accordingly. Lastly, metrics yet again will provide ongoing support in your mission to measure successes and failures as they arise.

Trianz offers itself as an Amazon machine learning service that can utilize the cloud for intelligent, flexible, robust application development and management. For the banking and financial industries, we are introducing this emerging technology along with cognitive and robotic process automation and advanced analytics. Our marketing analytics service leverages the best market assessment tools to gain insights from not only machine learning but pattern recognition, scenario/sensitivity modeling, and statistical regression analysis, to name a few. 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.

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