Onboarding

This guide will walk you through the complete onboarding process for Kaisar AI Ops.

Initial Setup

1. Account Activation

After receiving your account credentials:

  1. Navigate to https://ai.kaisar.io

  2. Log in with your provided credentials

  3. Update your profile information (if prompted)

  4. Set up multi-factor authentication (if required)

2. Profile Configuration

  1. Click on your profile icon (top-right corner)

  2. Navigate to Settings

  3. Update:

    • Display name

    • Email preferences

    • Notification settings

    • Time zone

3. Join Your Organization

  1. Navigate to Organization in the sidebar

  2. You should see your assigned organization(s)

  3. Verify your role and permissions

  4. Explore team members and groups

Organization View

Understanding Your Workspace

Dashboard Overview

The dashboard provides:

  • Recent Activity: Your latest experiments and deployments

  • Quick Stats: Summary of resources and usage

  • Notifications: System alerts and updates

  • Quick Actions: Shortcuts to common tasks

Navigation Structure

Familiarize yourself with the main sections:

Deep Learning Platform

  • Experiments: Track ML training runs

  • Models: Manage trained models

  • Datasets: Organize training data

Deployments

  • Deploy models to production

  • Monitor deployment health

  • Manage scaling and versions

First Tasks

Create Your First Experiment

  1. Go to Deep Learning Platform → Experiments

  2. Click Create

  3. Configure your experiment:

    • Name and description

    • Framework (PyTorch, TensorFlow, etc.)

    • Compute resources

    • Hyperparameters

  4. Submit the experiment

Experiments View

Upload a Dataset

  1. Go to Deep Learning Platform → Datasets

  2. Click Create

  3. Upload your dataset or connect to external storage

  4. Add metadata and tags

Datasets View

Register a Model

  1. Go to Deep Learning Platform → Models

  2. Click Create

  3. Upload or link your trained model

  4. Add version information and documentation

Models View

Best Practices

Organization

  • Use clear, descriptive names for experiments, models, and datasets

  • Add tags for easy filtering and search

  • Document your work with descriptions and README files

Collaboration

  • Share experiments with team members

  • Use groups to organize team access

  • Comment on experiments for knowledge sharing

Security

  • Never share your credentials

  • Use API tokens for programmatic access

  • Review permissions regularly

Next Steps

Now that you're onboarded:

  1. Explore the User Guide for detailed features

  2. Review IAM & Security for access management

  3. Check Troubleshooting for common issues

Getting Help

If you need assistance:

  • Check the FAQ

  • Contact your team administrator

  • Reach out to Support

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