Ectron Corporation is a San Diego, CA-based company that develops advanced digitization and data processing solutions focused predominantly on IoT, i.e., collecting data from assets at the Edge and providing real-time data analytics on the Edge and the Cloud.

DATA MODELING AND DATABASE/DATA DESIGN

  • Data Labeling
  • Data Preparation
  • Creation of Custom Datasets
  • Classification and Grouping of Data using Multiple Algorithms
  • Azure Data Lake and Daat Factory

Consulting expertise for building basic data models and data infrastructure (data lake, data factory etc.)

AI/ML ALGORITHM DESIGN AND ANALYTICS

Expertise on ML Studio, as well as building dataflow and pipelines for AI/ML. Our team can build, train, and deploy machine learning models within Azure.

  • Azure AI/ML Learning Studio
  • Automated Machine Learning
  • Integrated CI/CD, Machine Learning Pipelines and Model Management
  • Expertise in Python Development Environments and Machine Learning Frameworks
  • Custom Algorithm and Model Development in C/C++ and Visual Studio
  • Signal and Image Processing
  • Building Frameworks for AI/ML

Our expertise includes real-time data analytics in conjunction with Edge devices to provide sub-millisecond results. This is accomplished with Ectron’s own Microsoft Azure-certified Edge computers with AI/ML capabilities and infrastructure that utilize Microsoft Azure.

DATA MINING AND DEPLOYMENT

Deploying and tuning models and solutions with ML Ops, refining the deployment of solutions with building experiments and using the following:

  • Pipelines and CI/CD
  • Managed Endpoints (Ectron or 3rd Party Hardware Systems)
  • Hybrid and Multi-Cloud Solutions
  • Security and Policies
  • Monitoring and Analysis
  • Creating and Auditing Policies

AZURE

  • Building Private Instances of Azure
  • Virtual Machines (Linux and Windows)
  • Azure Blob Storage
    • Azure Event Grid
    • Machine Learning Dataflows and Ontologies
    • Azure App Services

Design custom setups from the ground up based on requirements w. multiple data sources, data types, building workflows.