{"id":1459,"date":"2022-01-10T18:32:11","date_gmt":"2022-01-10T18:32:11","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/01\/10\/how-reliaquest-uses-amazon-sagemaker-to-accelerate-its-ai-innovation-by-35x\/"},"modified":"2022-01-10T18:32:11","modified_gmt":"2022-01-10T18:32:11","slug":"how-reliaquest-uses-amazon-sagemaker-to-accelerate-its-ai-innovation-by-35x","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/01\/10\/how-reliaquest-uses-amazon-sagemaker-to-accelerate-its-ai-innovation-by-35x\/","title":{"rendered":"How ReliaQuest uses\u00a0Amazon\u00a0SageMaker\u00a0to accelerate its AI\u00a0innovation by\u00a035x\u00a0"},"content":{"rendered":"
\n

\"\"<\/a>Cybersecurity continues to be a top concern for enterprises. Yet the constantly evolving threat landscape that they face makes it harder than ever to be confident in their cybersecurity protections.<\/p>\n

To\u00a0address\u00a0this,\u00a0ReliaQuest<\/a> built\u00a0GreyMatter<\/a>,\u00a0an Open XDR-as-a-Service platform\u00a0that brings together telemetry from any security and business solution, whether on-premises or in one or multiple clouds, to unify detection, investigation, response, and resilience.<\/p>\n

In 2021, ReliaQuest turned to\u00a0AWS\u00a0to\u00a0help it\u00a0enhance its\u00a0artificial intelligence (AI)\u00a0capabilities and\u00a0build new features faster.<\/p>\n

Using Amazon SageMaker<\/a>, Amazon Elastic Container Registry<\/a> (ECR), and AWS Step Functions<\/a>,\u00a0ReliaQuest\u00a0reduced the time needed to deploy and test critical new AI capabilities for its\u00a0GreyMatter\u00a0platform from eighteen months to two weeks. This increased the speed of its AI innovation by 35x.<\/p>\n\n\n\n
\"\"<\/a><\/td>\n\n

\u201cThis innovative architecture has dramatically decreased the time to value of ReliaQuest\u2019s data science initiatives.<\/strong><\/em><\/p>\n

Now, we can truly focus on what\u2019s most important \u2013 developing powerful solutions to further improve the security of our customer\u2019s environments in an ever-changing threat landscape.\u201d<\/strong><\/em><\/p>\n

Lauren Jenkins, Snr Product Manager, Data Science, ReliaQuest<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Using AI to enhance\u00a0the\u00a0performance of human analysts<\/h2>\n

GreyMatter takes a fundamentally new approach to cybersecurity, pairing advanced software with a team of highly-trained security analysts to deliver drastically improved security effectiveness and efficiency.<\/p>\n

Although ReliaQuest\u2019s security analysts are some of the best-trained security talent in the industry, a single analyst may receive hundreds of new security incidents on any given day. These analysts must review each incident to determine the threat level and the optimal response method.<\/p>\n

To streamline this process, and reduce time to resolution, ReliaQuest set out to develop an AI-driven recommendation system that automatically matches new security incidents to similar previous occurrences. This enhanced the speed with which human analysts can identify the incident type as well as the best next action.<\/p>\n

Using\u00a0Amazon\u00a0SageMaker\u00a0to\u00a0put AI to work faster<\/h2>\n

ReliaQuest had developed an initial machine learning (ML) model, but it was missing the supporting infrastructure to utilize it.<\/p>\n

To solve this, ReliaQuest\u2019s Data Scientist, Mattie Langford, and ML Ops Engineer, Riley Rohloff, turned to Amazon SageMaker. SageMaker is an end-to-end ML platform that helps developers and data scientists quickly and easily build, train, and deploy ML models.<\/p>\n

Amazon\u00a0SageMaker\u00a0accelerates the deployment of ML workloads by\u00a0simplifying the ML build process. It provides a broad set of ML capabilities on top of\u00a0fully-managed\u00a0infrastructure. This removes\u00a0the undifferentiated heavy lifting\u00a0that\u00a0too-often hinders\u00a0ML development.<\/p>\n

ReliaQuest chose SageMaker because of its built-in hosting feature, a key capability that enabled ReliaQuest to quickly deploy its initial pre-trained model onto fully-managed infrastructure.<\/p>\n

ReliaQuest also used Amazon ECR to store its pre-trained model\u00a0images, using\u00a0Amazon ECRs\u00a0fully-managed\u00a0container registry that makes it easy to store, manage, share, and deploy container images and artifacts, such as pre-trained ML models,\u00a0anywhere.<\/p>\n

ReliaQuest chose Amazon ECR because of its native integration with Amazon SageMaker. This enabled it to serve custom model images for both training and predictions, the latter via a custom Flask application it had built.<\/p>\n

Using Amazon SageMaker and Amazon ECR, a single ReliaQuest team developed, tested, and deployed its pre-trained model behind a managed endpoint quickly and efficiently, without needing to hand-off to or depend on other teams for support.<\/p>\n

Using\u00a0AWS Step Functions to\u00a0automatically\u00a0retrain and improve model performance<\/h2>\n

In addition, ReliaQuest\u00a0was able to build\u00a0an entire\u00a0orchestration layer for\u00a0their ML workflow using\u00a0AWS Step Functions,\u00a0a low-code visual workflow service\u00a0that can\u00a0orchestrate AWS services, automate business processes, and\u00a0enable\u00a0serverless applications.<\/p>\n

ReliaQuest chose AWS Step Functions because of its deep functionality\u00a0and integration with\u00a0other\u00a0AWS services. This enabled\u00a0ReliaQuest to build a fully automated learning loop for its model,\u00a0including:<\/p>\n