Hitachi Solutions America's Projects
A public repo to host the splash page for the HSAL Readme.md.
Helm Charts ready to launch on Azure Kubernetes Serice (AKS)
Azure Search Knowledge Mining Accelerator
Solution accelerator for Customer Complaint Management
Azure Analytics End to End with Azure Synapse - Deployment Accelerator
This is a solution accelerator for creating personalized content recommendations based on user activity.
Solution accelerator to help developers build an end-to-end Customer 360 solution using Azure Synapse Analytics and Dynamics 360 Customer Insights
This Solution Accelerator is an end-to-end example on how to enable personalized customer experiences for retail scenarios by leveraging Azure Synapse Analytics, Azure Cosmos DB, Azure Machine Learning Services, Azure Data Lake Storage
This accelerator was built to provide developers with all of the resources needed to build a solution to find ideal replaceable parts comparing with its charecteristics for avoiding supplieir chain part procurement issue using Azure Synapse Analytics and Azure Machine Learning.
This accelerator was built to provide developers with all of the resources needed to build a solution to identify the top factors for revenue growth from an e-commerce platform using Azure Synapse Analytics and Azure Machine Learning.
CAF L300 Workshop focused on Ready, Govern, Manage and Adopt
A reference example with sample code for developers interested publishing transactable, Software as a-Service offers in the Microsoft commercial marketplace.
Template to deploy a single Data Landing Zone of the Data Management & Analytics Scenario (former Enterprise-Scale Analytics). The Data Landing Zone is a logical construct and a unit of scale in the architecture that enables data retention and execution of data workloads for generating insights and value with data.
Template to deploy the Data Management Zone of the Data Management & Analytics Scenario (former Enterprise-Scale Analytics). The Data Management Zone provides data governance and management capabilities for the data platform of an organization.
Template to deploy a Data Product for analytics and data science use-cases into a Data Landing Zone of the Data Management & Analytics Scenario (former Enterprise-Scale Analytics). The Data Product template can be used by cross-functional teams to create insights and products for external users.
This repo is the public repository which hosts the TPC benchmarks we use to gauge system performance.
Digital Documentation for Shipping industry (Quote to Order) solution accelerator
Data Platform in 30 Days scripts and templates
The Financial Industry Business Ontology (FIBO) defines the sets of things that are of interest in financial business applications and the ways that those things can relate to one another. In this way, FIBO can give meaning to any data (e.g., spreadsheets, relational databases, XML documents) that describe the business of finance.
Bitnami Docker Image for Apache Airflow
This repository provides holistic architecture design and reference implementation for industry cloud based on proven success of large scale deployments and at-scale adoption with customers and partners.
Official docker images for the influxdata stack
Official Helm Chart Repository for InfluxData Applications
A Kubernetes Operator based on the Operator SDK for syncing resources in Keycloak
Bitnami collection of Grafana dashboards
Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.