Usama Wahab Khan, a CTO at Evolution Technologies, outlines the components and phases of building data science projects using Azure, including data sourcing, model training, and deployment. He emphasizes the advantages of using Azure's Automated Machine Learning (AutoML) for efficiency and ease, enabling users with limited programming knowledge to implement machine learning solutions. The document also details various Azure resources and services that support scalable data science initiatives.

1. Usama wahab Khan
MVP,MCT, CTO @Evolution Technologies

2. Usama Wahab Khan
Father, data Scientist, Developer/Nerd, Traveler
Twitter : @usamawahabkhan
LinkedIn : Usamawahabkhan

3. Building blocks for a Data Science Project
Data
sources
Classical ML
Deep learning
Build and train
models
Experimentation
and pipelines
Hyperparameter
tuning
DevOps for data
science
Deployment

4. Machine Learning on Azure
Domain Specific Pretrained Models
To reduce time to market
Azure
Databricks
Machine
Learning VMs
Popular Frameworks
To build machine learning and deep learning solutions TensorFlow
PyTorch ONNX
Azure Machine
Learning
Language
Speech
…
Search
Vision
Productive Services
To empower data science and development teams
Powerful Hardware
To accelerate deep learning
Scikit-Learn
PyCharm Jupyter
Familiar Data Science Tools
To simplify model development Visual Studio Code Command line
CPU GPU FPGA
From the Intelligent Cloud to the Intelligent Edge

5. Azure Machine Learning Service
Set of Azure
Cloud Services
Python
SDK
✓ Prepare Data
✓ Build Models
✓ Train Models
✓ Manage Models
✓ Track Experiments
✓ Deploy Models
That enables you to:

6. Data Science Virtual
Machines
(DSVM)
Pre-Configured environments in the
cloud for
Data Science & AI Modeling,
Development & Deployment.
Samples to get started

7. Training Infrastructure
Scale resources
Autoscale resources to only pay while
running a job
Schedule jobs
Train at cloud scale using a
framework of choice
Dependencies and Containers
Leverage system-managed AML
compute or bring your own
compute
Provision VM clusters
Use the latest NDv2 series VMs
with the NVIDIA V100 GPUs
Distribute data
Manage and share resources across
a workspace

8. Training Infrastructure
Scale resources
Autoscale resources to only pay while
running a job
Schedule jobs
Train at cloud scale using a
framework of choice
Dependencies and Containers
Leverage system-managed AML
compute or bring your own
compute
Provision VM clusters
Use the latest NDv2 series VMs
with the NVIDIA V100 GPUs
Distribute data
Manage and share resources across
a workspace

9. Prepare data Build & train models Deploy & predict
Data storage
locations
Data ingestion
Data Preparation Model building & training Model deployment
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing
Model validation
Deployment
Batch scoring
Normalization
Transformation
Validation
Featurization
Hyper-parameter tuning
Automatic model selection
Model testing Testing error
Azure Machine Learning Pipelines

10. What is automated machine learning ?
Automated machine learning, also referred to as automated ML or AutoML,
is the process of automating the time consuming, iterative tasks of machine
learning model development. It allows data scientists, analysts, and
developers to build ML models with high scale, efficiency, and productivity
all while sustaining model quality


11. Azure Automated Machine learning
Data scientists, analysts, and developers across
industries can use automated ML.
•Implement ML solutions without extensive
programming knowledge
•Save time and resources
•Leverage data science best practices
•Provide agile problem-solving

12. How azure Auto ML works?

13. Forcing on Citizen Data Scientist

14. Automated ML in Azure Machine Learning Studio
Select
dataset
Upload files
Import from
Web
Register
data source
Configure
run
Experiment
name
Target label Compute
Task type
and settings
Classification Regression Time Series

15. Supported Algorithms
LogisticRegression ElasticNet ElasticNet
StochasticGradientDescent(SGD) LightGBM LightGBM
NaiveBayes GradientBoosting GradientBoosting
C-SupportV
ectorClassification(SVC) DecisionT
ree DecisionT
ree
LinearSV
C K
NearestNeighbors K
NearestNeighbors
K
NearestNeighbors LARSLasso LARSLasso
DecisionT
ree StochasticGradientDescent(SGD) StochasticGradientDescent(SGD)
RandomForest RandomForest RandomForest
ExtremelyRandomizedT
rees ExtremelyRandomizedT
rees ExtremelyRandomizedT
rees
GradientBoosting
LightGBM

16. Demo

17. Q & A
Usama Wahab Khan
Twitter : @usamawahabkhan
LinkedIn : Usamawahabkhan

18. Thank you 