Category: Learning Levels

As organizations migrate applications to the cloud, they need tools to assess application portfolios for cloud readiness. CAST Highlight provides automated analysis of custom applications, identifying issues like outdated code, cloud migration blockers, and opportunities for cloud-native services. By integrating with AWS Migration Hub Journeys, CAST offers a template to orchestrate analysis of large portfolios across teams to accelerate cloud migration and modernization initiatives.

TextRay is an information extraction solution that automatically extracts data from scanned documents using deep learning models. It leverages AWS services to provide a scalable and cost-effective way to process documents while reducing errors and turnaround time. An AWS CloudFormation template simplifies deployment, while a pre-trained base model demonstrates TextRay’s precision in extracting tabular and form data into structured CSV outputs.

The Amazon Athena Teradata Connector enables Athena to query data in Teradata Vantage using SQL, and comprises two AWS Lambda functions for metadata and record reading. This post describes deploying the connector, creating a Lambda layer for the Teradata JDBC driver, and running queries on Teradata from Athena, including a federated query joining Teradata and S3 data. This provides a scalable, serverless way to analyze data across different data stores without data duplication.

Connecting IoT devices securely to the cloud can be complex, but AWS IoT ExpressLink simplifies this by providing connectivity modules with built-in security from AWS Partners. Cardinal Peak used these modules to accelerate IoT product prototyping, avoiding hardware redesigns, simplifying firmware, using pre-installed certificates, streamlining manufacturing, and enabling efficient cloud onboarding. Overall, AWS IoT ExpressLink helped Cardinal Peak save over 6 months of engineering time.

NextBillion.ai provides mapping solutions for enterprises, aiming to deliver precise estimated time of arrival (ETA). It developed ENZYME, a system leveraging AWS services like Amazon SageMaker to evaluate map quality and improve ETA accuracy through machine learning. By feeding industry data into custom models, ENZYME reduces the mean absolute percentage error between estimated and actual arrival times by 10-20% compared to regular maps.

nOps Compute Copilot extends the capabilities of the open-source Karpenter Kubernetes cluster autoscaler, adding awareness of your AWS compute commitments like Reserved Instances and Savings Plans. It also analyzes Amazon EC2 Spot pricing data and termination risk to intelligently select the most cost-effective and stable Spot Instances for workloads. nOps automatically manages your Karpenter configurations, updating NodePools based on its cost optimization analysis and recommendations.

Companies often need to securely share digital resources when collaborating, and the extranet provides a mechanism for this using cloud platforms like AWS. Alkira’s Extranet-as-a-Service (EaaS) solution enables secure global connectivity, network segmentation, NAT for overlapping IPs, and integrated security for partner access to AWS applications. Alkira reinvents networking for the cloud era and allows enterprises to build networks with cloud-like speed, agility, and scale.

As data volumes grow, visibility into key metrics becomes crucial for optimizing reliability, performance, and cost. Pariveda’s observability solution leverages AWS services to build operational dashboards displaying AWS Glue job details like runtimes, status, and computational load. By unlocking deeper insights, users can pinpoint optimization opportunities, troubleshoot issues faster, and drive greater efficiency across their data pipelines as workloads scale.

Using the AWS console to manage cloud infrastructure can be convenient and user-friendly, especially for beginners or small projects. Yet, it also carries inherent challenges when used at scale. In this post, we will dive deep into how ControlMonkey can assist you with transitioning manually-created environments to an Infrastructure as Code approach, integrating seamlessly with GitOps and Hashicorp Terraform.

Generative AI adoption is rapidly growing to meet the massive data needs of modern machine learning models. Manually labeling data can be time-consuming but AWS has collaborated with Datasaur to offer solutions addressing data labeling challenges using generative AI. Datasaur’s NLP Platform automates annotation tasks and integrates with AWS services, and its LLM Labs evaluates large language models’ performance and cost for labeling.