DAIR BoosterPack Catalogue

The DAIR Program is longer accepting applications for cloud resources, but access to BoosterPacks and their resources remains available. BoosterPacks will be maintained and supported until January 17, 2025.

After January 17, 2025:

Screenshots should remain accurate, however where you are instructed to login to your DAIR account in AWS, you will be required to login to a personal AWS account.
Links to AWS CloudFormation scripts for the automated deployment of each sample application should remain intact and functional.
Links to GitHub repositories for downloading BoosterPack source code will remain valid as they are owned and sustained by the BoosterPack Builder (original creators of the open-source sample applications).

AI Chatbot-Powered Web Platform Starter App

Meet the Builder – 1280 Labs

This BoosterPack contains a simple implementation of a variety of LLMs for users to compare the performance and cost of different models. A large language model (LLM) is an advanced artificial intelligence system designed to understand and generate human language. They are built using deep learning techniques, particularly neural networks with many layers (hence “large”), and trained on vast amounts of text data. This training allows them to recognize patterns in language, understand context, and produce coherent and contextually appropriate text.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Time-Series AI Anomaly Detection

Meet the Builder – Chillwall AI

Anomaly detection applications are software tools designed to identify irregular patterns or deviations in data. These automated systems detect spikes, drops, and other abnormal occurrences over time, which can lead to issues such as defects, injuries, theft, system failures, and financial losses. By identifying these anomalies early, organizations can take timely actions to prevent negative impacts on downstream models, system functionality, and reporting.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Automated Document Classification and Discovery

Meet the Builder – FormKiQ

An Automated Document Classification and Discovery application is a type of software that uses Natural Language Processing (NLP), artificial intelligence (AI), and machine learning (ML) algorithms to automatically categorize and tag documents based on their content, allowing users to quickly search and retrieve documents (i.e., discovery) based on generated keywords or document tags.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Cloud-based Platform for Geospatial Intelligence with Machine Learning

Meet the Builder – Ecosystem Informatics

This BoosterPack provides a Geospatial-AI lnformation Toolbox (GAIT) that combines geospatial intelligence and machine learning. We’ll show you how to build a modular, scalable, cloud-based platform that provides an alternative to more expensive, proprietary software suites.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Automated Data Pipeline (ADP)

Meet the Builder – Intelius

An automated data pipeline (ADP) is an end-to-end solution for automating the ingestion, transformation, storage, and presentation of data on a scalable platform. This BoosterPack demonstrates how a series of open-source tools can be integrated to create an ADP.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Automated Static and Dynamic Application Security Testing

Meet the Builder – Parabellyx

This BoosterPack provides a set of open-source tools to help automate the process of checking your source code for vulnerabilities via static application security testing (SAST) and dynamic application security testing (DAST).

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Build private blockchain networks on Hyperledger Fabric

Meet the Builder – Senofi

This BoosterPack showcases how Hyperledger Fabric, a modular architecture DLT platform, provides the ability to build a permissioned blockchain network. These networks can perform Smart Contracts, a type of secure, real-time transaction among partners across a business network.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Automate Cloud Orchestration with Kubernetes

Builder: BigBitBus

This Boosterpack is intended to get you to market faster and decrease the downtime of applications developed and deployed using good Kubernetes practices.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Watch the webinar

IoT – Digitize any Physical Space

Builder: reelyActive

This BoosterPack provides a deployable Sample Solution that allows users to observe and study the application of Bluetooth Low Energy Real-Time Location Systems (BLE RTLS) and Kibana to solve the problem of automatically collecting and reporting data about who/what is where/how within the physical spaces of a business’s operations.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Time-Series Prediction with Machine Learning

Builder: BluWave-ai

This BoosterPack demonstrates the application of machine learning to develop models that provide good predictions for time-series data.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Automatic Recommendation System Using Machine Learning

Builder: Carla Margalef Bentabol

This BoosterPack demonstrates how a Collaborative Filtering Deep Model is used to provide recommendations to users based on their past preferences and similarities to other users. This is very useful for software developers needing an Automatic Recommendation System.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Deploy

Apption Data Assessment Tool

Builder: Apption

This BoosterPack introduces a user-friendly solution for analyzing unstructured data, auto-identify data-types and dynamically creating an optimal database schema. The solution can recognize over 30 data types and flag sensitive data such as first and last names which are included in a summary report.

Read the Flight Plan to learn more.

See the Sample Solution to learn how.

Currently unsupported.