1) Nephos is a hybrid cloud-enabled AI workflow service that allows users to visually build and run AI workflows in the cloud. It provides a visual workflow editor to drag and drop datasets, executables, and draw connecting edges to create workflows. 2) The 7 steps to using Nephos include: setting up a cloud cluster, registering resources like datasets and executables, creating the first AI workflow, running the workflow, experimenting on workflows, collaborating with others, and scheduling recurring workflow runs as jobs. 3) Key features of Nephos include supporting hybrid clouds, using open source technologies like Pegasus and Docker, and being built by experienced AI practitioners to simplify complex AI workflows.

1. 7 Steps to Simplifying your AI
Workflows
Visually Building and Running AI workflows in the Cloud

2. About Me
• Vishnu Vettrivel
• @cloudronin
• Open source enthusiast
• Developer with around 20 years
of experience building and
scaling successful big data and AI
platforms.
• Architect and co-founder of the
Nephos project
• hybrid cloud enabled AI workflow
service

3. A brief background to AI workflows
• Computationally expensive
• Large data sets
• Multiple binaries/executables/scripts
• Orchestration and Data flow intensive
• Iterative in nature
• Elasticity of Cloud will help
• Can get pretty complex pretty quickly
• Versioning is important

4. Introduction to Nephos

5. Nephos: High Level overview
• Visual workflow service
• Built for hybrid clouds
• Built on open source technologies
• Pegasus
• Htcondor
• Docker
• Built by Experienced AI/Big Data practioners

6. Step 1: Setup a new Cluster
• Adding a cluster is a 1-click operation*
• Unless you want to setup on a private cloud
• Define cluster Configuration
• Cluster size
• Autoscaling parameters
• Instance types
• Libraries to attach

7. Step 2: Register your Resources
• Resources could either be
• Datasets (External or Internal)
• Executables (or Scripts)
• Define arguments
• Inputs
• Outputs
• Position bindings

8. Step 3: Create your first AI workflow
• Drag and Drop Datasets
• Drag and Drop Executables
• Draw your connecting edges
• Configure Nodes if needed
• Modify Name and other metadata as needed
• Get ready to run your workflow

9. Step 4: Running your Workflow
• Click Run
• Watch Run Log output
• Progress Indicator
• Wait for completion (or error )
• Download/Preview output if succesful
• Debug if needed
• Modify workflow if needed
• Repeat Rinse

10. Step 5: Experimenting on your workflows
• Download/Preview output
• Analyze results
• Decide if improvements needed
• Modify workflow parameters/nodes
• Re-run workflow

11. Step 6: Collaborating on your workflow
• Comment your workflows
• Share your workflow with your team
• Clone your team members workflow
• Modify the clone and share back
• Repeat, Rinse!

12. Step 7: Scheduling your workflow runs
• Decide which workflow you want to ’jobify’
• Select and create a job from it
• Configure Job parameters
• Setup alerting if needed

13. Questions