Category: Expert (400)

Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.

For microservices that predominantly write data, Amazon DynamoDB provides an “always on” experience without the need for careful capacity planning, resharding, and database maintenance. These capabilities make DynamoDB a popular database service for various parts of game platforms like player data, game state, session history, and leaderboards. Learn how to pair Amazon DynamoDB with an analytics solution like Rockset to automatically index your data for fast search, aggregations, and joins at scale.

As organizations continue to build data lakes on AWS and adopt Amazon EMR, especially when consuming data at enterprise scale, it’s critical to govern your data lakes by establishing federated access and having fine-grained controls to access your data. Learn how to implement SAML-based authentication (AuthN) using Okta for Amazon EMR, querying data using Zeppelin notebooks, and applying column-level authorization (AuthZ) using AWS Lake Formation.

Providing a better experience at lower cost is the desired result of any organization and product. In most cases, it requires software re-architecting, planning, infrastructure configurations, benchmarking, and more. Epsagon provides a solution for monitoring and troubleshooting modern applications running on AWS. Dive deep on some of the best practices Epsagon has developed to improve the performance and reduce the cost of using serverless environments.

By hosting a Palo Alto Networks VM-Series firewall in an Amazon VPC, you can use AWS native cloud services—such as Amazon CloudWatch, Amazon Kinesis Data Streams, and AWS Lambda—to monitor your firewall for changes in configuration. This post explains why that’s desirable and walks you through the steps required to do it. You now have a way to monitor your Palo Alto Networks firewall that is very similar to how you monitor your AWS environment with AWS Config.

When streaming data comes in from a variety of sources, organizations should have the capability to ingest this data quickly and join it with other relevant business data to derive insights and provide positive experiences to customers. Learn how you can build and run a fully managed Apache Kafka-compatible Amazon MSK to ingest streaming data, and explore how to use a Kafka connect application to persist this data to Snowflake. This enables businesses to derive near real-time insights into end users’ experiences and feedback.