AWS FeedField Notes: Data-Driven Risk Analysis with Amazon Neptune and Amazon Elasticsearch Service This blog post is co-authored with Charles Crouspeyre and Angad Srivastava. Charles is Director at Accenture Applied Intelligence and ASEAN AI SME (Subject Matter Expert) and Angad is Data and Analytics Consultant at AWS and NLP (Natural Language Processing) expert. Together, they…
AWS FeedCreating a notification workflow from sensitive data discover with Amazon Macie, Amazon EventBridge, AWS Lambda, and Slack Following the example of the EU in implementing the General Data Protection Regulation (GDPR), many countries are implementing similar data protection laws. In response, many companies are forming teams that are responsible for data protection. Considering the…
AWS FeedFrictionless hosting of containerized ASP.NET web apps using Amazon Lightsail This post is written by Fahad Mustafa, Cloud Application Architect, AWS Professional Services There are many ways to deploy ASP.NET web apps to AWS. Each with its own use cases and differing pricing models. But what if you have a small website and database…
AWS FeedDevelop and deploy ML models using Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot Data generates new value to businesses through insights and building predictive models. However, although data is plentiful, available data scientists are far and few. Despite our attempts in recent years to produce data scientists from academia and elsewhere, we still…
AWS FeedChoosing a CI/CD approach: AWS Services with BigHat Biosciences Founded in 2019, BigHat Biosciences’ mission is to improve human health by reimagining antibody discovery and engineering to create better antibodies faster. Their integrated computational + experimental approach speeds up antibody design and discovery by combining high-speed molecular characterization with machine learning technologies to guide…
AWS FeedAmazon SageMaker Named as the Outright Leader in Enterprise MLOps Platforms Over the last few years, Machine Learning (ML) has proven its worth in helping organizations increase efficiency and foster innovation. As ML matures, the focus naturally shifts from experimentation to production. ML processes need to be streamlined, standardized, and automated to build, train,…