Deeplearning and dev ops azure

DevOps and Machine Learning
@vishwasnarayan5 Vishwas N
https://hacksterdude.web.app/

About me
I am a Podcaster : http://tiny.cc/vnrpodcast
I love to talking to techies
A Bibliophile
Passionate about Image dataset - Computer Vision
Now exploring Azure cloud

Machine Learning
A Machines Ability to learn
without being explicitly
programmed.

To a Standard Definition
Your Neural Network is a

Programming
Algorithm
Data
Answers

Machine Learning
Algorithm
Data
Answers

Machine Learning
Model
Data
Answers

Machine Learning
Predictions
Model
Answers
Data
Data

DATA
Algorithm
Output
Traditionally

DATA
Output
DATA
Model
Model
Predictions

Sophisticated pretrained models
To simplify solution development
Azure
Databricks
Machine Learning
VMs
Popular frameworks
To build advanced deep learning solutions TensorFlow Keras
Pytorch Onnx
Azure
Machine Learning
Language Azure Search
Vision
On-premises Cloud Edge
Productive services
To empower data science and development teams
Powerful infrastructure
To accelerate deep learning
Machine Learning on Azure
Flexible deployment
To deploy and manage models on intelligent cloud and edge

Developers
Data Scientists Operations

Ask great questions Collect the data Prepare the data
Select the algorithm Train the
model
Deploy in Real World
The data science
process

Azure Machine Learning
A fully-managed cloud service that enables you to easily build, deploy, and share predictive analytics
solutions.

What is Azure Machine Learning?
Set of Azure Cloud Services Python SDK or R Programing
and many more
✔Prepare
Data
✔Build Models
✔Train Models
✔Manage Models
✔Track Experiments
✔Deploy Models
That enables you to:

Prepare
your environment
Experiment
with your model & data
Deploy
Your model into
production

Step 1: Prepare your environment

Datasets – registered, known data sets
Experiments – Training runs
Pipelines – Training workflows
Models – Registered, versioned models
Endpoints:
Real-time Endpoints – Deployed model endpoints
Pipeline Endpoints – Training workflow endpoints
Compute – Managed compute
Environments – defined training and inference environments
Datastores – Connections to data
Azure Machine Learning

Step 2: Create a ML pipeline to
deliver a model

Pipelines
Azure ML Service
Pipelines
Azure
Pipelines

Azure Machine Learning Pipelines
Workflows of steps that can use Data
Sources, Datasets and Compute
targets
Unattended runs
Reusability
Tracking and versioning

Azure Pipelines
Orchestration for Continuous Integration
and Continuous Delivery
Gates, tasks and processes for quality
Integration with other services Trigger on
code and non-code events

Create a pipeline
Input Output
Runs a script
on a Compute Target
in a Docker container.
Parameters

Create a pipeline
Dataset Prepare data
Train the Model
with PyTorch
Processed
dataset
model
Register
the
model
Blob Storage Account Model Management

Submit the pipeline to the
cluster

Demo: Create an Azure ML Pipeline

Jupyter Notebook
Compute Target
Docker Image
Hot Data store
1. Snapshot folder and send to
experiment
2. create docker image
3. Deploy docker
and snapshot to
compute
4. Mount datastore to
compute
6. Stream stdout,
logs, metrics
5. Launch the script
7. Copy over
outputs
Experiment

Azure Machine Learning Service
Pipelines
A repeatable process to deliver
a ML model

Code and comments only (not Jupyter output)
Plus every part of the pipeline
And Infrastructure and dependencies
And maybe a subset of data
Source Control

Everything should be in
source control!
Except your training data
which should be a known, shared data source

Triggered on code change
Refresh and execute AML Pipeline
Code quality, linting, and unit testing
Pull request process
Continuous Integration

Demo: Setup Azure Pipeline for AMLS
Pipeline

Trigger on model registration
Deploy to test and staging environments
Run integration and load tests
Control: rollout, feature flags, A/B testing
Continuous Delivery

Control model rollout!
The same way you do with other software

AMLS to
deploy
The Model Score.py Environment file
Docker Image

Demo: Deploy with Azure
DevOps

Deeplearning and dev ops azure

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