• Home
  • Overview
  • StartupGuide
  • UserGuide
    User DataLake Annotation Notebook Training Model Deployment Edge Custom Image
  • References
    CLI Document SDK Document API Document
  • FAQ
  • Overview
    • Function of ABEJA Platform
    • Common Specifications
    • Authentication
    • Security of ABEJA Platform
    • Instance Type
    • Available Images
      • all-cpu
      • all-gpu
      • base
    • Developer Tools
      • ABEJA Platform CLI
      • ABEJA Platform SDK
  • Getting Started Guide
    • Model development using Notebook/Template
      • Data acquisition and dataset creation
      • Create a ML model without coding
      • Create an inference API without coding
    • Machine learning process guide
      • Use of annotation tool
      • Train model
      • Create model
      • Create Web API
      • Switch Web API
    • Appendix
      • Upload your dataset
      • Create web API with CLI
      • Switch web API seamlessly with ABEJA CLI
  • UserGuide
    • User
      • Add User
    • DataLake
      • Channel
      • Data Source
      • Upload File to Datalake
        • Data Upload Guide
        • Upload file to Datalake using CLI
        • Upload file to Datalake using SDK
        • Upload file to Datalake using Curl
    • Annotation
      • work flow
      • Screen Description
      • Creating a user
      • Create a Project
        • Register S3 credential
        • Register GCS Credentials
      • Upload your data
      • Create a work
      • Create Tasks
      • Assign annotators to a work
      • Perform annotation work
      • Review the work
      • Output the results
        • Output with JSON format
        • Output to the ABEJA Platform Dataset
      • Appendix
        • Appendix: Types of roles
        • Appendix: Types of Templates
        • Appendix:Features of organizations connected to the ABEJA Platform
        • Appendix: How it differs from the old annotations
    • (Old)Annotation
      • (Old)Annotation Templates
      • (Old)Data Upload Guide
      • (Old)Description of management screen
      • (Old)Worker create guide
      • (Old)Project create guide
      • (Old)Data Download Guide
      • (Old)Annotation Result Schema
      • (Old)Object Detection Annotation Manual
      • (Old)Pre-inference function
    • Notebook
      • Create a notebook
      • Stop and delete notebooks
      • Update Notebook
      • Notebook environment
    • Training
      • Dataset
      • Training Handler Function
      • Configuration file for Training
      • train-local
      • Template Training (Object Detection)
    • Model
      • Model and Model Version
      • Model Handler Function FOR 18.10
      • Model handler function FOR 19.04 +
    • Deployment
      • Trigger
      • Format of scheduler
      • Template Deployment (Object Detection)
      • Autoscaling feature of HTTP service
    • Edge
      • About Edge
    • Custom Image
      • Make Custom Image
      • Make Repository
      • Regist Custom Image
      • Use Custom Image
  • References
    • ABEJA Platform CLI
      • CONFIG COMMAND
        • init
        • show
        • switch
        • list
        • delete
      • DATALAKE COMMAND
        • create-channel
        • describe-channels
        • archive-channel
        • download
        • upload
      • DATASET COMMAND
        • create-dataset
        • delete-dataset
        • describe-datasets
        • create-dataset-item
        • delete-dataset-item
        • describe-dataset-items
        • update-dataset-item
        • import-from-datalake
      • TRAINING COMMAND
        • init
        • create-job-definition
        • describe-job-definitions
        • create-version
        • describe-versions
        • update-version
        • archive-version
        • unarchive-version
        • describe-training-models
        • create-job
        • describe-jobs
        • archive-job
        • unarchive-job
        • debug-local
        • train-local
      • MODEL COMMAND
        • check-endpoint-image
        • check-endpoint-json
        • create-deployment
        • create-endpoint
        • create-model
        • create-service
        • create-trigger
        • create-version
        • delete-deployment
        • delete-endpoint
        • delete-model
        • delete-service
        • delete-version
        • describe-deployments
        • describe-endpoints
        • describe-models
        • describe-service-logs
        • describe-services
        • describe-versions
        • download-versions
        • run-local
        • run-local-server
        • start-service
        • stop-service
        • submit-run
        • update-endpoint
      • startapp command
  • FAQ
  • Appendix
    • Glossary
    • About CONTENT-TYPE
    • About CURL
    • Acknowledgments & License
    • Service Updates

Getting Started Guide

Introduction

In this start guide, we explain how to use for those who want to use ABEJA Platform for the first time.

Use cases

  1. Model Development using Notebook / Template

    • Data Acquisition / Dataset Creation
    • Create a ML model without coding
    • Create an inference API without coding
  2. Machine Lerning Process Guide

    • Use of Annotation Tool
    • Training Model
    • Create Model
    • Create Web API
    • Swithch Web API
  3. Appendix

    • Upload Your Dataset
    • Create Web API with CLI
    • Switch Web API Seamlessly with ABEJA CLI

Updated on 26 Jun 2017

ABEJA Platform SDK Model development using Notebook/Template

Copyright © 2019 ABEJA, Inc. All rights reserved.