Accelerating adoption of AI by developers

(consuming models)

Rise of hybrid training and scoring scenarios

Push scoring/inference to the edge, cloud, on-prem

Moving high-end developers into deep learning as

non-traditional path to DS / AI dev

Growth of diverse hardware arms race across all form factors

(CPU / GPU / FPGA / ASIC / device)

Demonstrating success of transfer learning techniques

while reducing dev complexity

Data prep

Model deployment & management

Model lineage & auditing

Explain-ability

D A T A S C I E N C E & A I

C H A L L E N G E SK E Y T R E N D S

Apps + insightsSocial

LOB

Graph

IoT

Image

CRM INGEST STORE PREP & TRAIN MODEL & SERVE

Data orchestration and monitoring

Data lake and storage

Hadoop/Spark/SQL and ML

.

IoT

Azure Machine Learning

T H E A I D E V E L O P M E N T L I F E C Y C L E

Spark

SQL Server

Virtual machines

GPUs

Container services

NotebooksIDEs

Azure Machine Learning Workbench

SQL Server

Machine Learning Server

O N - P R E M I S E S

A Z U R E M A C H I N E L E A R N I N G

E D G E C O M P U T I N G

Azure IoT Edge

Experimentation and

Model Management

A Z U R E M A C H I N E L E A R N I N G S E R V I C E S T R A I N & D E P LO Y O P T I O N S

A Z U R E

H Y P E R S C A L E ,

E N T E R P R I S E - G R A D E

I N F R A S T R U C T U R E

D E V E LO P E R TO O L S &

S E R V I C E S

O P E N P L AT F O R M F O R

D ATA S C I E N C E

Hardware

Storage management

Software

T H E M L & A I P L A T F O R M

AI Applications (1st & 3rd party) Cognitive Services Bot Framework

Spark AI Batch Training DS VM SQL Server ACS

BLOB Cosmos DB SQL DB/DW ADLS

CPUs FPGA GPUs IoT

Azure Machine Learning

Model deployment & management

Machine Learning toolkits

Experimentation management,

data prep, & collaboration

CNTK

Tensorflow

ML Server

Scikit-Learn

Other Libs.

PROSE

Docker

Cloud – Spark, SQL, other engines

ML Server – Spark, SQL, VMs

Edge

Plan for Hackathon

www.aka.ms/aalab

https://docs.microsoft.com/da-dk/azure/machine-learning/preview/quickstart-installation

Plan for Hackathon

Links for Azure Machine Learning Workbench

• https://azure.microsoft.com/en-us/blog/diving-deep-into-what-s-new-with-azure-machine-learning/

• https://docs.microsoft.com/da-dk/azure/machine-learning/preview/quickstart-installation

Begin building now with the tools and platforms you know

Build, deploy, and

manage at scale

Boost productivity with

agile development

Benefit from the fastest AI developer cloud

Build, deploy, and manage at scale

B u i l d an d dep l oy eve r y where – c l ou d , o n -premi ses , edge , an d i n - da t a

Dep l oy i n m i n u tes w i t h da t a d r i ven man agemen t an d re t r a i n i n g of a l l you r mode l s

P ro to t y pe l oca l l y t hen s ca l e u p an d ou t w i t h VMs , S pa r k c l u s te r s , an d GP U s

Rea l t i me , h i gh t h rou gh -pu t i n s i ght s eve r y where , i n c l u d i n g E xce l i n tegr a t i on

Deployment and management of models as HTTP services

Container-based hosting of real time and batch processing

Management and monitoring through Azure

(e.g., AppInsights)

First class support for SparkML, Python, CNTK, TF, TLC, R,

extensible to support others (Caffe, MXnet)

Service authoring in Python and .NET Core

Manage models

DOCKER

Single node deployment (cloud/on-prem)

Azure Container Service

Azure IoT Edge

Spark clusters

Deploy everywhere

Consume Azure Machine Learning models in Excel

Boost productivity with agile development

S pen d more t i me mode l i n g an d l e s s t i me prepp i n g w i t h bu i l t - i n i n te l l i gen t da t a wr an g l i n g

I n c rease co l l abor a t i on an d sha r i n g w i t h n otebook s an d G i t

Avo i d l o s i n g an y t h i n g w i t h ve r s i on con t ro l an d reprodu c i b i l i t y

Kn ow t he bes t pe r fo r mi n g mode l s w i t h met r i c s , l i n eage , r u n h i s to r y, a s se t man agemen t , an d more

Sample, understand, and prep data rapidly

Leverage PROSE SDK and more for intelligent, data prep by example

Extend/customize transform and featurize through Python

Generate Python and PySpark for execution at scale

Prep data

Experiment

Manage job for local and cloud experiments

Find support for Spark + Python + R (roadmap)

Execute jobs locally, on remote VMs (scale up),on Spark clusters (scale out), or SQL on-premises

Create with Git-backed experimentation tracking of code, config, parameters, and data

Discover and compare with detailed historical metadata

Begin building now with the tools and platforms you know

Choose be t ween v i su a l d r ag -an d-drop o r code - f i r s t au t hor i n g

U se you r f avo r i te I DE

Ca l l Azu re Mach i n e Lea r n i n g se r v i ces d i rec t l y i n VS Code*

B u i l d on an y f r amewor k o r l i b r a r y w i t h t he mos t popu l a r l an gu ages

Tr a i n qu i cke r an d eas i e r w i t h i n du s t r y -l ead i n g S par k an d GP Us

VISUAL DRAG -AND-DROP CODE-F IRST

Build as you like

Use your favorite IDE

Leverage all types of data

Use what you want

U S E T H E M O S T P O P U L A R I N N O VAT I O N S

U S E A N Y TO O L

U S E A N Y F R A M E W O R K O R L I B R A R Y

Integrated into your IDE

Visual Studio Code extension (more IDEs and notebooks on roadmap)

Begin building with your IDE - no extra tool needed

Integrated rich authoring for machine learning and deep learning

Call Azure Machine Learning services straight from your IDE or notebook

Plan for Hackathon

www.aka.ms/aalab

https://docs.microsoft.com/da-dk/azure/machine-learning/preview/quickstart-installation

Plan for Hackathon

Links for Azure Machine Learning Workbench

• https://azure.microsoft.com/en-us/blog/diving-deep-into-what-s-new-with-azure-machine-learning/

• https://docs.microsoft.com/da-dk/azure/machine-learning/preview/quickstart-installation

Try it for free

Lea r n more

St a r t n ow