Deeplearning and dev ops azure

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

2. 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

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

5. To a Standard Definition
Your Neural Network is a

6. Programming
Algorithm
Data
Answers

7. Machine Learning
Algorithm
Data
Answers

8. Machine Learning
Model
Data
Answers

9. Machine Learning
Model
Data
Answers

10. Machine Learning
Predictions
Model
Answers
Data
Data

11. DATA
Algorithm
Output
Traditionally

12. DAT
A
Algorithm
Output

13. DATA
Output
DATA
Model
Model
Predictions

14. 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

15. Developers
Data Scientists Operations

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

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

18. 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:

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

20. Step 1: Prepare your environment

22. 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

23. Demo: Azure Machine Learning

24. Step 2: Create a ML pipeline to
deliver a model

25. Pipelines
Azure ML Service
Pipelines
Azure
Pipelines

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

27. 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

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

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

30. Submit the pipeline to the
cluster

31. Demo: Create an Azure ML Pipeline

32. 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

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

35. Continuous Integration

36. 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

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

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

39. Demo: Setup Azure Pipeline for AMLS
Pipeline

40. Step 3: Deploy your model

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

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

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

44. Demo: Deploy with Azure
DevOps

45. Complete Pipeline