Machine Learning and AI

Machine learning is a data science technique that allows computers to
use existing data to forecast future behaviors, outcomes, and trends.

Enterprise AI Maturity Model
Foundational
Wrong expectations or disappointment
Low digitization
Basic analytical capabilities
Questioning what AI is
and how to apply it
Approaching
Digitization underway
Looking to increase or optimize processes
Cautious about disruption
Hopeful on AI and
its promise
Aspirational
High digitization
Desires new business models
Achieved a data culture
Experimented and
applied AI
Mature
Understands model lifecycle
Building a foundational
data architecture
Emerging data science
and operational capability

© Microsoft Corporation
Helping you innovate across your business
Customer insights Sales insights Virtual assistants Cash flow forecasting HR insights
Churn analytics Dynamic pricing Waiting line optimization Risk management Quality assurance Resource planning
Lead scoring Intelligent chatbots Financial forecasting Employee insights
Marketing Sales Service Finance Operations Workforce
Product recommendationProduct recommendation Predictive maintenancePredictive maintenance
Demand forecastingDemand forecasting

Advanced analytics pattern in Azure
Azure Data
Lake store
Azure
Storage
HDInsightAzure Databricks
Azure ML
service
ML server
Model training
Long-term storage Data processing
Azure Data
Lake Analytics
Azure ML
Studio
SQL Server
(in-database ML)
Azure Databricks
(Spark ML)
Data Science
VM
Cosmos DB
Serving storage
SQL DB
SQL DW
Azure Analysis
Services
Cosmos DB
Batch AI
SQL DB
Azure Data
Factory
Orchestration
Azure Container
Service
Trained model hosting
SQL Server
(in-database ML)
Data collection and understanding, modeling, and deployment
Sensors and IoT
(unstructured)
Logs, files, and media
(unstructured)
Business/custom apps
(structured)
Applications
Dashboards
Power BI

Azure machine learning studio
Use Azure Machine
Learning to deploy your
model into production as
a web service in minutes
Work with familiar coding
languages such as R and
Python while benefitting
from hundreds of built-in
packages and support for
custom code
Deploy models to production
as web services that can be
called from any device,
anywhere, using any data
source
Easily share your solution
with the world on the
Azure Marketplace
A fully-managed cloud service that enables you to easily build,
deploy and share predictive analytics solutions

AZURE MACHINE LEARNING STUDIO
Platform for emerging data scientists
to graphically build and deploy
experiments
• Rapid experiment composition
• > 100 easily configured modules
for data prep, training, evaluation
• Extensibility through R & Python
• Serverless training and
deployment
Some numbers:
• 100’s of thousands of deployed
models serving billions of requests

Data Science Virtual Machines (DSVM)

Azure Machine Learning service

WHAT IS 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:

Workspace – Top level container for model management and
experimentation. Also, creates and manages storage, container
registry, key vault and app insights
Experiments – Grouping of training runs for a given script
Pipelines – Stitch together multiple stages to create machine
learning workflows
Compute – Compute resources for training or image
deployment
Models – Encapsulation of trained models
Images – Docker images containing trained models to be
deployed to a compute target
Deployments – Deployment of an image to web services, IoT
Edge and FPGAs
Data Stores – Abstraction layer over Azure storage
Azure Machine Learning
service features

Custom AI
Typical E2E Process
…
Prepare Experiment Operationalize/Deploy
Orchestrate
Azure Databricks
Machine Learning Compute
Data Science Virtual Machine
Azure ML service Azure Container Instances
Azure Kubernetes ServiceAzure Databricks
Azure Data Factory

Azure ML Concept
Model Management
Model Management in Azure ML
usually involves these four steps
Step 1:
Step 2:
Step 3:
Step 4:

Azure ML Artifact
Compute Target
Compute Target Training Deployment
Local Computer ✓
A Linux VM in Azure (such as the Data
Science Virtual Machine)
✓
Azure ML Compute ✓
Azure Databricks ✓
Azure Data Lake Analytics ✓
Apache Spark for HDInsight ✓
Azure Container Instance ✓
Azure Kubernetes Service ✓
Azure IoT Edge ✓
Field-programmable gate array (FPGA) ✓
Currently supported compute targets

Azure ML service Artifacts
Image and Registry
Image contains
Two types of images
Image Registry

Azure ML Artifact
Deployment
Deployment is an instantiation of an image. Two options:
Web service IoT Module

Why Intelligent Edge?
High-speed data processing,
analytics and shorter response
times are more essential than ever.
Intelligent Cloud
• Business agility and scalability: unlimited computing
power available on demand.
Intelligent Edge
• Can handle priority-one tasks locally
even without cloud connection.
• Can handle generated data that is too
large to pull rapidly from the cloud.
• Enables real-time processing through
intelligence in or near to local devices.
• Flexibility to accommodate data privacy related
requirements.

Azure ML service
Lets you easily implement this AI/ML Lifecycle
Azure
Machine Learning
Workflow Steps

Azure ML: How to deploy models at scale

Services
+
Customer journey Data Prep Build and Train Manage and Deploy
Apache Spark / Big Data
Python ML developer
Azure ML service
(Pandas, NumPy etc. on AML Compute)
Azure ML service
(OSS frameworks, Hyperdrive, Pipelines,
Automated ML, Model Registry)
Azure ML service
(containerize, deploy,
inference and monitor)
Azure ML service
(containerize, deploy,
inference and monitor)
Azure Databricks
(Apache Spark Dataframes,
Datasets, Delta, etc.)
Azure Databricks + Azure ML service
(Spark MLib and OSS frameworks +
Automated ML, Model Registry)

Comparable Table
Azure Machine Learning Studio Azure Machine Learning Services
Pros • Rapid development (Drag and Drop)
• Works well with relatively simple
datasets
• Pre-built ML algorithms
• Cheap
• Fast (VMs with GPUs)
• Different optimization methods,
CI/CD pipeline
• Full control during training
• Manage computing resources
(choose VM size)
• Use open source ML libraries
Cons • Can be slow
• Limited optimization methods,
operationalized architecture
• Less control during training
• Fixed computing resources
• More elaborate to build, require
deeper knowledge of machine
learning
• Deeper models need much more
data with much more memory
• Higher costs for VM with GPU

Azure Databricks
Databricks Spark as a managed service on Azure

Azure Databricks key audiences & benefits
Unified analytics platform
Integrated workspace
Easy data exploration
Collaborative experience
Interactive dashboards
Faster insights
• Best Spark & serverless
• Databricks managed Spark
Improved ETL performance
• Zero management clusters, serverless
Easy to schedule jobs
Automated workflows
Enhanced monitoring & troubleshooting
• Automated alerts & easy access to logs
Zero Management Spark
Cluster democratization (serverless)
Fast, collaborative analytics platform
accelerating time to market
No dev-ops required
Enterprise grade security
• Encryption
• End-to-end auditing
• Role-based control
• Compliance
Data scientist Data engineer CDO, VP of analytics
Provided by Microsoft and Databricks under NDA

A Z U R E D A T A B R I C K S
Microsoft Azure

A Z U R E D A T A B R I C K S C O R E A R T I F A C T S
Azure
Databricks

P R O V I S I O N I N G A Z U R E D A T A B R I C K S W O R K S P A C E

Comparing Notebooks in Azure Databricks against other IDEs
Data understanding services
Notebooks in Azure Databricks Other IDEs
No
Azure Databricks only
Yes
Spark
Python, Scala, R, SQL, Bash Shell
Provides extensive visualizations library in
addition to supporting 3rd party libraries.
Full Azure Active Directory integration
Simultaneous, multi-user collaboration
Yes
GitHub, Bitbucket
Requires software installation Yes
Pieced together, disparate solutions
No
Python, PySpark
Python, SQL, Bash Shell
Supports standard Jupyter Notebook
visualizations and libraries like Matplotlib
No
No
No
Yes, but not optimal
Execution environment
Serverless service
Kernels supported
Languages supported
Visualizations
Supports role-based access control
Collaborative workspaces
Run notebooks as scheduled jobs
Source control

M I X I N G L A N G U A G E S I N N O T E B O O K S
You can mix multiple languages in the same notebook
Normally a notebook is associated with a specific language. However, with
Azure Databricks notebooks, you can mix multiple languages in the same
notebook. This is done using the language magic command:
• %python Allows you to execute python code in a notebook (even if that notebook is not python)
• %sql Allows you to execute sql code in a notebook (even if that notebook is not sql).
• %r Allows you to execute r code in a notebook (even if that notebook is not r).
• %scala Allows you to execute scala code in a notebook (even if that notebook is not scala).
• %sh Allows you to execute shell code in your notebook.
• %fs Allows you to use Databricks Utilities - dbutils filesystem commands.
• %md To include rendered markdown

For Cloud environments
Train and evaluate machine learning architecture
Raw data
Reference data
• 0
1
Machine learning
Azure
Databricks
Scale out clusters
Azure
Databricks
Operational stores
Data
warehouse
SQL DW
Cosmos
DB
Model MGMT, experimentation,
and run history
Azure ML
Service
(preview)
Azure ML
service
(preview)

Fast, easy, and collaborative Apache Spark™-based analytics platform
Azure Databricks for machine learning modeling
Tools InfrastructureFrameworks
Provision autoscaling clusters on-demandUse best-in-class notebooks to quickly
access model performance and revert
when needed
TensorFlow, Keras, and XGBoost, all
installed and configured for Spark clusters
Enable distributed, multi-GPU training
with Horovod via a native runtime
Get seamless updates of the Spark stack
to ensure uninterrupted operations
Capture model telemetry at every
stage to enable reproduceable results
Schedule notebook activities as jobs in
and let Azure Data Factory orchestrate
the rest
Take advantage of Azure ML Services to for
simple Kubernetes Cluster deployments
Leverage parallelized ML algorithms from
battle-tested libraries

Build in your environment of choice
Custom IDEs
Visual studio tools for AINotebooksR studio PyCharm

S P A R K M A C H I N E L E A R N I N G ( M L ) O V E R V I E W
 Offers a set of parallelized machine learning algorithms (see next
slide)
 Supports Model Selection (hyperparameter tuning) using Cross
Validation and Train-Validation Split.
 Supports Java, Scala or Python apps using DataFrame-based API (as
of Spark 2.0). Benefits include:
• An uniform API across ML algorithms and across multiple languages
• Facilitates ML pipelines (enables combining multiple algorithms into a
single pipeline).
• Optimizations through Tungsten and Catalyst
• Spark MLlib comes pre-installed on Azure Databricks
• 3rd Party libraries supported include: H20 Sparkling Water, SciKit-
learn and XGBoost
Enables Parallel, Distributed ML for large datasets on Spark Clusters

M M L S P A R K
Microsoft Machine Learning Library for Apache Spark (MMLSpark) lets
you easily create scalable machine learning models for large datasets.
It includes integration of SparkML pipelines with the Microsoft
Cognitive Toolkit and OpenCV, enabling you to:
 Ingress and pre-process image data
 Featurize images and text using pre-trained deep learning models
 Train and score classification and regression models using implicit
featurization

S P A R K M L A L G O R I T H M S
Spark ML
Algorithms

D E E P L E A R N I N G
 Supports Deep Learning Libraries/frameworks including:
• Microsoft Cognitive Toolkit (CNTK)
• TensorFlow
• Keras
• Theano
 Offers Spark Deep Learning Pipelines, a suite of tools for working with
and processing images using deep learning using transfer learning. It
includes high-level APIs for common aspects of deep learning so they
can be done efficiently in a few lines of code:
Azure Databricks supports and integrates with a number of Deep Learning libraries and
frameworks to make it easy to build and deploy Deep Learning applications
Distributed Hyperparameter Tuning
Transfer Learning

D E E P L E A R N I N G P I P E L I N E S

 Transfer Learning APIs for common aspects of deep learning so they can be done
efficiently in a few lines of code

Transfer Learning

T R A N S F E R L E A R N I N G
 ImageNet –
 InceptionV3 – Created @ Google Brain
 Xception – François Chollet
 ResNet50 –
ImageNet Large Scale Visual Recognition Competition
 VGG16/VGG19 – Introduced in 2014. This network is characterized by its simplicity,
using only 3×3 convolutional layers stacked on top of each other. The 16 and 19
stand for the number of weight layers in the network. VGG stands for Visual
Geometry Group.
Pre-Trained Libraries

Ready to use clusters with built-in ML Frameworks
HorovodEstimator
for simplified distributed training on TensorFlow with Apache Spark
GPU support
49

A D V A N C E D A N A L Y T I C S O N B I G D A T A
INGEST STORE PREP & TRAIN MODEL & SERVE
Cosmos DB
Business/custom apps
(structured)
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Azure Data Lake Store Gen2Azure Data Factory Azure SQL Data
Warehouse
Azure Analysis
Services
Power BI
PolyBase
SparkR
Azure Databricks
Microsoft Azure also supports other Big Data services like Azure HDInsight, Azure Machine Learning to allow customers to tailor the above architecture to meet
their unique needs.
Real-time apps

Operationalization and management architecture
Scale out clusters
Notebooks
Azure
Databricks
Containers
AKS ACI
IoT edge
Intelligent Apps
Power BI
Docker
Model MGMT, experimentation,
and run history
Azure ML
service
(Preview)
Data warehouse
SQL DW
Operational stores
Cosmos
DB

C L I C K T O E D I T M A S T E R T I T L E S T Y L E

B I G D A T A S C A L E W I T H M I C R O S O F T M L S E R V E R
f(x)
Task
Task
Task
 Full support for R and Python
 Scale via parallel & distributed computation
 On-premises deploys to multiple platforms:
 Machine Learning Server for Hadoop
 Machine Learning Server for Linux
 Machine Learning Server for Windows
 In Azure, available within:
 ML Server on HDInsight (Hadoop + Spark)
 Machine Learning Server on Linux VM
 Data Science VM (Linux and Windows)
 ML Server provides the underlying functionality integrated into
SQL Server as Machine Learning Services (In-Database), which is
available to:
 All editions of SQL Server 2017
 It is also available in Azure SQL Database

Machine Learning Server at a glance
Platforms & Data
Tools
Languages
Algorithms
Data Sources
Rattle Mrsdeploy
RESTful API
deployment
Real-Time
Scoring
Visualization
Tool
Integration
.csv Microsoft .XDF
In-database
deployment
Operationalization
Distributed Parallelized Algorithms:
•RevoScaleR library
•MicrosoftML library
•Custom parallelization frameworks
Open source R algorithms
& visualizations:
•CRAN
•bioconductor
Plus:
•Deep Learning
•Pretrained Models
•Prebuilt Featurizers
ODBC/JDBC

ML Server for Hadoop/Spark:
In-memory, massive parallelism
User
Workstation
ML Server for Hadoop
Data
Frames
YARN Resource
Management
Spark
Executor
Worker
Task
Spark
Executor
Worker
Task
Spark
Executor
Worker
Task
.csv, xdf, hive,
parquet, ORC
ScaleR
Master Task
Finalize
r
Initiator
Edge Node
Spark
Driver
Ver. 1.6 or 2.0
ML Server
components
installed

In-database analytics with SQL Server 2017

Build intelligent apps and
AI with all your data
Analyzing all data
Easily and securely manage
data big and small
Managing all data
Simplified management and analysis through a unified deployment, governance, and tooling
Unified access to all your data with
unparalleled performance
Integrating all data

Including Relational, noSQL, Hadoop
Using Spark and SQL
With Azure Data Studio

SQL Server 2019 big data, analytics, and AI
Managed SQL Server, Spark,
and data lake
Store high volume data in a data lake and access
it easily using either SQL or Spark
Management services, admin portal, and
integrated security make it all easy to manage
SQL
Server
Data virtualization
Combine data from many sources without
moving or replicating it
Scale out compute and caching to boost
performance
T-SQL
Analytics Apps
Open
database
connectivity
NoSQL Relational
databases
HDFS
Complete AI platform
Easily feed integrated data from many sources to
your model training
Ingest and prep data and then train, store, and
operationalize your models all in one system
SQL Server External Tables
Compute pools and data pools
Spark
Scalable, shared storage (HDFS)
External
data sources
Admin portal and management services
Integrated AD-based security
SQL Server
ML Services
Spark &
Spark ML
HDFS
REST API containers
for models

Compute pool
SQL Compute
Node
SQL Compute
Node
SQL Compute
Node
…
Compute pool
SQL Compute
Node
IoT data
Directly
read from
HDFS
Persistent storage
…
Storage pool
SQL
Server
Spark
HDFS Data Node
SQL
Server
Spark
HDFS Data Node
SQL
Server
Spark
HDFS Data Node
Kubernetes pod
Analytics
Custom
apps BI
SQL Server
master instance
Node Node Node Node Node Node Node
SQL
Data mart
SQL Data
Node
SQL Data
Node
Compute pool
SQL Compute
Node
Storage Storage

Integration with Azure services
Azure and Power BI have the built-in
connectivity and integration to bring business
intelligence efforts to life
Integration can be achieved without the need
to develop complex solutions:
 Direct connect:
 Azure SQL Database
 Azure SQL Data Warehouse
 Spark on Azure HDInsight
 Power BI Desktop
 Real-time dashboards with Azure Stream Analytics
HDInsight
Storage
Event
Hubs
Machine
Learning
SQL Server
Stream
Analytics
Power BI

Power BI Desktop
Connect to various Azure
services, and create queries
 Queries can be integrated with other
data source types, not necessarily
Azure
 Datasets sourced from a Power BI
Desktop file can be refreshed

Real-time dashboards with Azure Stream Analytics
Use Azure Stream Analytics to push live, streaming data to
Power BI
 Enables real-time dashboards at scale, over data from devices and applications
 Can stream millions of events per second
 Can perform aggregation over time windows

Advanced analytics
Coalesce Azure services
together to drive advanced
analytics:
 Azure HDInsight:
Big Data processing
 Azure Machine Learning:
Predictive analytics
 Azure Data Factory:
Orchestration at scale

Describing additional capabilities
Running R and Python scripts
R Python scripts can be ran directly in
Power BI Desktop, and resulting
datasets imported into a Power BI
Desktop data model
 R, Python must be installed on the local
machine
 Only data frames are imported
 Columns typed as Complex and Vector are not
imported
 Can be refreshed with a gateway

Describing additional capabilities
Generating R and Python visuals
R and Python visuals render from R script,
accepting input fields
Benefits:
 Leverage the voluminous and growing number of out-of-
the-box plots available in R and Python
 Easily customize R and Python visuals by developing the
script
 Combine advanced analytics in visuals
 Interact with R and Python visuals in Power BI Desktop
(filter, and cross-filter are supported)

Transform your data
Using R or Python

Point and click to explain the increase/decrease
Data transformations “by example”
Relationship and data type detection
Built in integration with R and Python

The flow suggests default set of inputs for the ML model,
including those from related entities in the Dataflow.

• The model accuracy preview
report includes the top
influencers for the model,
including a breakdown of how
the different values for that
influencer affects the
outcome.

Role of Cognitive Services
making it easier to infuse AI
frameworks
TensorFlow KerasPytorch Onnx
ML
Extract intent Detect empty shelvesExtract identity
generic
specialized
effort
pioneer
expert
developer
everyone
Pre-built,
customizable
services
face OCR text vision speech translation
QnA
......
use case featuresF1 F2 F3 F4
how deep in the stack?

Cognitive Services capabilities
Infuse your apps, websites, and bots with human-like intelligence
https://azure.microsoft.com/en-us/services/cognitive-services

A variety of real-world applications
Vision Speech
Intent: PlayCall
Language Knowledge Search

Face Detection
Face API detects up to 64 human faces with high precision face location in an image. And the image can be
specified by file in bytes or valid URL.
Face Recognition
Face recognition is widely used in many scenarios including security, natural user interface, image content
analysis and management, mobile apps, and robotics. Four face recognition functions are provided: face
verification, finding similar faces, face grouping, and person identification.
Key features:
• Detect human faces and compare similar ones
• Organize images into groups based on similarity
• Identify previously tagged people in images
Vision – Face
Azure Cognitive Services

Visual Intelligence Made Easy
Easily customize your own state-of-the-art computer
vision models that fit to your unique use case. Just bring
a few examples of labeled images and let Custom Vision
do the hard work.
Custom Vision

Speech
For optimal result in Speech to Text, customize:
• acoustic models for your use environments, such as vehicles
• field-specific vocabulary and grammar, such as medical or IT
• pronunciation of abbreviations and acronyms, such as "IOU" for "I owe you."
• Speaker Recognition
• Speech to Text
• Text to Speech

LUIS – Language Understanding
LUIS aims to be the most comprehensive cloud-based service for conversational understanding,
and the easiest to use for developers with no AI expertise:
• extracts intent/action and entities from user utterances
• includes dictionaries, which can be extended
with customer-specific terms
Roadmap highlights coming up:
• 2H2018 easier to use together with
Azure Bot, Speech and QnA Maker services

Q&A
QnA Maker
Microsoft & NDA Customers only—do not share

Extract insights from customer feedback and social network postings.
I had a wonderful trip to Seattle and
enjoyed seeing the Space Needle!
• submit up to 100 calls per minute, each with up to 1,000 documents of up to 5,000 characters each
Links backed by Wikipedia
in select languages
Text Analytics

translate dynamic content in your mobile, desktop and web apps
 automatically detect languages
 transliterate into different alphabets
Text Translator –
Translation
60+ supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/language-support

Resources:
• Blog
• Case Study
• Documentation
• Samples
Content Moderator
Content Moderator helps businesses manage risks associated with user generated content
(UGC) by using machine-assisted content moderation APIs and a human review tool.
Features include:
 Detection of potential adult, racy, and offensive, illegal, and unwanted image & video content
 Identification of possible profanity and undesirable text
 Built-in human review tool for improving the results
 Workflows that allow you to add other API’s and extract more content insights
Customers using Content Moderator technologies include:
 Online marketplaces/e-Commerce sites for moderating catalogs and chatbots
 Social media/messaging/gaming platforms for moderating user content and digital assets
 Enterprises/K-12 solution providers moderating user content and information chatbots
 Global content moderation service providers using AI to augment human moderation teams

Bing Search

– Ingest paper
– Identify customers and staff
– Customer care
– Customer understanding

? Searchable content
image + markup
{ “Bank Statement": [
{ “bank”: “Rural Bank”,
“acc name”: “Joe Sample”,
“opening”:
{ “balance”: $7,701.18”,
“date”: “01/02/2011”
}
“deposits”: “$70,062.31”
…
}]
}
Applications
•
•
•
•



example Solution Architecture
Custom Vision
tuned for docs
training
live ops
learned parameters
Doc classifier
(per industry)
...
pre-processor
tagged extraction mask
Form extractors
(10s-100s per industry)
...
Domain expert
describes regions for extraction
(1 per doc class)
SSN: ###-##-#### Income: #######.## ...
...
1000’s – millions
of docs per company
{ “W2 Tax Form":
{ “year”: “2018”,
“name”: “Joe Sample”,
“address”:
{ “number”: “921”,
“street”: “Getaway Lane”
}
“1 Wages”: “$77,500.34”
…
}
}
Markup Content
...

Applications
•
•
•
•


train identities
live ops
Face
Verify
EmotionSpeaker
Verify
Assess and react:
•
•
"disgust": 0.6847,
"sadness": 0.2135,
"anger": 0.0955…
"happy": 0.999…

"happy": 0.992…
Extra security:
•



Applications
•
•
•
•


pass if needed,
provide context
$$$
chasm

no-code
management
Azure Search
Fuzzy matching
+
QnA Maker
live ops
Azure Indexing
+
QnA Editor
QnA Maker
Knowledgebase (KB)
FAQs, user manuals
channels:

training
live ops
https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/tutorials/integrate-qnamaker-luis
Operations
live data
Stores
Products
Policies
QnA Maker
QnA Maker
QnA Maker
Products KB
Stores KB
...
...
Operation utterances
LUIS
LUIS
Intent
Classifier
One intent:
- per KB
- per Operation
natural language
documents( )
dispatch
on intent
...

live ops
QnA Maker KB
...
Operations
https://github.com/Azure-Samples/cognitive-services-csharp-call-center
as needed
Azure Bot
...
intent
+
entities
troubleshoot
Sentiment
Analysis
red flag
LUISLanguage
Detection
Translation
as needed
Speech
to Text
Text to
Speech

QnA Ma
...
Operati
Azure Bot
...
Annotated logs:
+ sentiment
+ intent + entities
Applications
•
•

Key phrase
extraction
Speech
to Text
Recordings
(archival)
troubleshoot
Sentiment
Analysis
Social Media
Reviews
Voice of Customer
Evolve:
•
•
•

Automated Machine Learning
(in Machine Learning Services)

What is automated
machine learning?

AutoML

Azure Automated ML
Current Capabilities
Category Value
Compute
Target

Web UI [Azure portal - Preview]
Private Preview - 2/11
•Getting started experience
•Start an automated ml training job on your data
(upload/Blob)
•Explore data
•View progress and results for training job and each iteration
•View automated ML dashboard for an overview
Public Preview - April 2019
•Set advanced settings for the training job
•Forecasting support
•Automatically deploy a model
•Advanced data exploration (visualizations, correlations, etc.)
•View model Explainability
•Support additional 1p data sources (e.g. Azure SQL, ADLS, …)
May 2019 – General Availability
•Apply data transformations
•Support additional 1p & 3p data source
•Continue experiment in Jupyter Notebook
Microsoft confidential (NDA)
Create automated ML experiments from UI only (“no code”)
• Get started with ML quickly
• Powered by the automated ML SDK – easily customize advanced settings
• Automate the end-to-end ML workflow from the UI
• Easily explore and monitor all your experiments from a single place
Roadmap

“Day 0” (First time) experience
Create your first experiment with automated machine learning to
produce quality models with zero effort
Automate the process of algorithm selection, hyperparameter tuning, and best model selection
with automated machine learning, and accelerate your productivity. Select your data and let
automated ML do the rest to provide the best model from endless possible options

Create a new automated machine learning experiment
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* Experiment name
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Create a new automated machine learning experiment
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mytestmlexperimentsto… automl-blob-d8b2d4df-…* Storage account * Container

Training job settings:
* Training job type  Classification
* Target column  Freight cost
* Primary metric  Accuracy
Number of iterations: 100

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A side-by-side comparison of capabilities and features
Model deployment options
Scoring interface
provided
Deployment
environments
Scalability of
scoring interface
Scoring
requirements
Model packaging
SQL Server or SQL Database
T-SQL stored procedure
SQL Server 2017 database instance
on-premises or in Azure VM
Need to author Python or R code
within a T-SQL stored procedure that
loads the trained model from a table
where it is stored and applies it in
scoring.
Serialized to table
Limited to capacity of single server
Azure Databricks
Notebook or Job
Load the trained model from storage
and apply to scoring in notebook in
Python, Scala, R, or SQL.
Serialized to storage
Azure Databricks cluster, model export
Can scale across cluster resources
Web service
Create a Docker image that contains
scoring service, model, and
dependencies
Docker image
SQL Server, Hadoop
Scales by deploying more instances
in Azure Container Services
Azure Machine Learning
AKS, ACI edge via AML
IoT, IoT edge via AML
AKS, ACI
IoT, IoT edge
Spark and Batch AI

Machine learning and AI portfolio
What engines do you want to use?
Deployment target
Which experience do you want?
Build your own or consume
pre-trained models?
Microsoft ML &
AI products
Build your own
Azure Machine
Learning
Code first
(On-prem)
ML Server
On-prem
Hadoop
SQL Server
(cloud)
BYOT
SQL Server Hadoop Azure Batch DSVM Spark
Visual tooling
(cloud)
AML Studio
Consume
Cognitive
services, bots
Spark ML,
SparkR, SparklyR
Notebooks Jobs
Azure Databricks
Spark
When to use what

Artificial Intelligence Decision Tree
Big Data Decision Tree v4
Business Intelligence Solutions Decision Tree

Q & A ?
James Serra, Big Data Evangelist
Email me at: jamesserra3@gmail.com
Follow me at: @JamesSerra
Link to me at: www.linkedin.com/in/JamesSerra
Visit my blog at: JamesSerra.com (where this slide deck is posted via the “Presentations” link on the top menu)

Customer
analytics
Financial
modeling
Risk, fraud,
threat detection
Credit
analytics
Marketing
analytics
Faster innovation
for a better
customer experience
Improved consumer
outcomes and
increased revenue
Enhanced customer
experience with
machine learning
Transform growth
with predictive
analytics
Improved customer
engagement with
machine learning
Customer profiles
Credit history
Transactional data
LTV
Loyalty
Customer segmentation
CRM data
Credit data
Market data
CRM
Credit
Risk
Merchant records
Products and services
Clickstream data
Products
Services
Customer service data
Customer 360
degree evaluation
Reduced customer churn
Underwriting, servicing and
delinquency handling
Insights for new products
Commercial/retail banking,
securities, trading and
investment models
Decision science, simulations
and forecasting
Investment recommendations
Real-time anomaly detection
Card monitoring and fraud
detection
Security threat identification
Risk aggregation
Enterprise DataHub
Regulatory and
compliance analysis
Credit risk management
Automated credit analytics
Recommendation engine
Predictive analytics and
targeted advertising
Fast marketing and multi-
channel engagement
Customer sentiment analysis
Transaction data
Demographics
Purchasing history
Trends
Effective customer
engagement
Decision services
management
Risk and revenue
management
Risk and compliance
management
Recommendation
engine

Genomics and
precision medicine
Clinical and
claims data
GPU image
processing
IoT device
analytics
Social
analytics
Faster innovation
for drug
development
Improved outcomes
and increased
revenue
Diagnostics
leveraging
machine learning
transforms quality
of care
Improved patient
communications
and feedback
FAST-Q
BAM
SAM
VCF
Expression
HL7/CCD
837
Pharmacy
Registry
EMR
Readings
Time series
Event data
Social media
Adverse events
Unstructured
Single cell sequencing
Biomarker, genetic, variant
and population analytics
ADAM and HAIL on Databricks
Claims data warehouse
Readmission predictions
Efficacy and comparative
analytics
Prescription adherence
Market access analysis
Graphic intensive workloads
Deep learning using
Tensor Flow
Pattern recognition
Aggregation of
streaming events
Predictive maintenance
Anomaly detection
Real-time patient feedback
via topic modelling
Analytics across
publication data
MRI
X-RAY
CT
Ultrasound
DNA
sequences
Real world
analytics
Image
deep learning
Sensor
data
Social data
listening

Content
personalization
Customer churn
prevention
Recommendation
engine
Predictive
analytics
Sentiment
analysis
Faster innovation
for customer
experience
Improved consumer
outcomes and
increased revenue
Enhance user
experience with
machine learning
Predictive
analytics
transforms growth
Improved consumer
engagement with
machine learning
Customer profiles
Viewing history
Online activity
Content sources
Channels
Customer profiles
Online activity
Content distribution
Services data
Transactions
Subscriptions
Demographics
Credit data
Content metadata
Ratings
Comments
Social media activity
Personalized viewing and
engagement experience
Click-path optimization
Next best content analysis
Improved real time
ad targeting
Quality of service and
operational efficiency
Market basket analysis
Customer behavior analysis
Click-through analysis
Ad effectiveness
Content monetization
Fraud detection
Information-as-a-service
High value user engagement
Predict audience interests
Network performance
and optimization
Pricing predictions
Nielsen ratings and
projections
Mobile spatial analytics
Demand-elasticity
Social network analysis
Promotion events
time-series analysis
Multi-channel marketing
attribution
Consumption logs
Clickstream and devices
Marketing campaign
responses
Personalized
recommendations
Effective
customer retention
Information
optimization
Inventory
allocation
Consumer engagement
analysis

Next best and
personalized offers
Store design and
ergonomics
Data-driven stock,
inventory, ordering
Assortment
optimization
Real-time pricing
optimization
Faster innovation
for customer
experience
Improved consumer
outcomes and
increased revenue
Omni-channel
shopping
experience with
machine learning
Predictive
analytics
transforms growth
Improved consumer
engagement with
machine learning
Customer profiles
Shopping history
Online activity
Social network analysis
Shopping history
Online activity
Floor plans
App data
Demographics
Buyer perception
Consumer research
Market/competitive analysis
Historical sales data
Price scheduling
Segment level price changes
Customer 360/consumer
personalization
Right product, promotion,
at right time
Multi-channel promotion
Path to purchase
In-store experience
Workforce and manpower
optimization
Predict inventory positions
and distribution
Fraud detection
Market basket analysis
Economic modelling
Optimization for foot traffic,
Online interactions
Flat and declining categories
Demand-elasticity
Personal pricing schemes
Promotion events
Multi-channel engagement
Demand plans
Forecasts
Sales history
Trends
Local events/weather
patterns
Recommendation
engine
Effective customer
engagement
Inventory
optimization
Inventory
allocation
Consumer
engagement

Customer value
analytics
Next best and
personalized offers
Risk and fraud
management
Sales and campaign
optimization
Sentiment
analysis
Faster innovation
for customer
growth
Improved outcomes
and increased
revenue
Risk management
with machine
learning
Predictive
analytics
transforms growth
Improved customer
engagement with
machine learning
Customer profiles
Online history
Transaction data
Loyalty
CRM data
Credit data
Market data
CRM
Merchant records
Products
Services
Marketing data
Social media
Online history
Customer service data
Customer 360, segmentation
aggregation and attribution
Audience modelling/index
report
Reduce customer churn
Insights for new products
Historical bid opportunity
as a service
Right product, promotion,
at right time
Real time ad bidding platform
Personalized ad targeting
Ad performance reporting
Fraud prevention
Customer spend and
risk analysis
Data relationship maps
Optimizing return on ad
spend and ad placement
Multi-channel promotion
Ideal customer traits
Optimized ad placement
Opinion mining/social
media analysis
Deeper customer insights
Customer loyalty programs
Shopping cart analysis
Transaction data
Demographics
Purchasing history
Trends
Effective customer
engagement
Recommendation
engine
Risk and fraud
analysis
Campaign reporting
analytics
Brand promotion and
customer experience

Digital oil field/
oil production
Industrial
IoT
Supply-chain
optimization
Safety and
security
Sales and marketing
analytics
Faster innovation
for revenue
growth
Improved outcomes
and increased
revenue
Optimizing supply-
chain with machine
learning
transforms safety
and security
Improved customer
engagement with
machine learning
Field data
Asset data
Demographics
Production data
Sensor stream data
UAVs images
Inventory data
Production data
Sensor stream data
Transport
Retail data
Grid production data
Refinery tuning parameters
Clickstream data
Products
Services
Market data
Competitive data
Demographics
Production optimization
Integrate exploration
and seismic data
Minimize lease
operating expenses
Decline curve analysis
Pipeline monitoring
Preventive maintenance
Smart grids and microgrids
Grid operations, field service
Asset performance
as a service
Trade monitoring,
optimization
Retail mobile applications
Vendor management -
construction, transportation,
truck and delivery
optimization
Industrial safety
Environment health and safety
Fast marketing and
multi-channel engagement
Develop new products and
monitor acceptance of rates
Predictive energy trading
Deep customer insights
Transaction data
Demographics
Purchasing history
Trends
Upstream optimization,
maximize well life
Grid operations, asset
inventory optimization
Supply-chain
optimization
Risk
optimization
Recommendations
engine

Intrusion detection
and predictive
analytics
Security
intelligence
Fraud detection
and prevention
Security compliance
reporting
Fine-grained data
analytics security
Prevent complex
threats with
machine learning
Faster innovation
for threat
prevention
Risk management
with machine
learning
Transform security
with improved
visibility
Limit malicious
insiders to
transform growth
Firewall/network logs
Apps
Data access layers
Network flows
Authentications
Web
Applications
Devices
OS
Files
Tables
Clusters
Reports
Dashboards
Notebooks
Prevention of DDoS attacks
Threat classifications
Data loss/anomaly detection
in streaming
Cybermetrics and changing
use patterns
Real-time data correlation
Anomaly detection
Security context, enrichment
Offence scoring, prioritization
Security orchestration
e-Tailing
Inventory monitoring
Social media monitoring
Phishing scams
Piracy protection
Ad-hoc/historic incident
reports
SOC/NOC dashboards
Deep OS auditing
Data loss detection in IoT
User behavior analytics
Role-based access controls
Auditing and governance
File integrity monitoring
Row level and column level
access permissions
Web/app logs
Social media content
Security controls to leverage
all data
Actionable threat
intelligence
Risk and fraud
analysis
Compliance
management
Identity and access
management for analytics

Machine Learning and AI

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