ラスベガスで行われた初回のAzure AI カンファレンスのレポート

1. MIC R OSOFT A ZU R E A I C ON FER EN C E
O s a m u M a s u t a n i
D e c e m b e r 2 0 1 8
19/12/2018
1

2. EVENT
2
_ 1st conference
Half annual special conference for Azure and AI
_ December 3-6, 2018
_ Las Vegas
_ Co-located
Dev Intersection https://www.devintersection.com/
SQL Intersection https://www.devintersection.com/#!/?track=sql
_ Speakers
Microsoft Employees : product manager, architect, evangelist, researcher
Partner : consultant, architect

3. AZURE AI CONFERENCE STRUCTURE
3
_ Key notes
Vice president, Architect,
Researcher
Visual Studio, Azure, AI
_ Sessions
6 parallel
Azure (infra/dev) side + AI side

4. DEV INTERSECTION
4
_ Key notes
Most are shared with Azure
_ Sessions
6 Parallel
Visual Studio, C#, .NET, AI
dev, Web, UX, mobile, Java
Script, design, devops …

5. SQL INTERSECTION
5
_ Key notes
Most are shared with Azure
_ Sessions
4 Parallel
SQL server, DBA, data
management, data analytics,
hadoop, big data …

6. KEY NOTES/SESSIONS
6

7. K E Y N O T E :
S TAY P R O D U C T I V E W I T H AZ U R E AN D V I S U AL S T U D I O
7
_ New Visual Studio 2019
Live Share
Preview release on Windows and MacOS
AI trained IntelliCode (type ahead editor feature) : JavaScript ,
C++ etc… added
_ MS platform
WPF, WInForms, WinUI are now open source
Public preview of .NET Core 3.0 (open cross platform version
of .NET)
Xamarin Essential 1.0 GA
C# 9.0
_ Git
CEO appears
Integration of github and Azure Board (CI/CD)
Github CEOScott Gu
Live Share between windows and linux

8. K E Y N O T E :
F I N D Y O U R PAT H TO H AP P Y AN D P R O D U C T I V E D E V E L O P M E N T
8
_ New in Azure
Python , Linus support on Azure Functions (serverless)
CosmosDB access tool for Visual Studio Code
Virtual Kubelet donation to CNCF
Stream Analytics on IoT Edge
CNAB （Cloud Native Application Bundle） released
AKS support for serverless
_ AI
Azure Machine Learning now GA
ML.NET

9. DEEP LEARNING RELOADED
9
_ Explanatory session for deep learning history and what’s new
_ Azure data bricks support DL
Keras, tensor flow
GPU
_ Image processing demo
Matrix conversion
Image, similarity (KDTree), Convolution
Training
AutoEncoder : self train target data same with source (image) , Max pooling
Optimization
Optimal epocks , minimum loss function
_ Current stage of DN
# of neurons is around bee or frog
Latest (blue brain project) : 10000 neuron , 30 million interconnection
Human brain : 100 billion, 10000 trillion synaptic
ML process is often opaque -> robust

10. ARE YOU READY FOR A DATA LAKE
10
_ Explanatory session for how to use data lake
_ What is Data Lake ?
DWH replacement ?
DataLake technology storate
“Centralized repostiroy of data sotred as bobs or file” (James Dixon, Pentaho CTO)
natural forms, large, unstructured
raw and transformed data
metadata layer supporting search
_ DWH vs data lake
processed,refined vs all dadta
transactional daat,business metics vs all data source types raw
batch process(ETL) vs streaming/batch(ELT)
rigit structure requires time to change vs data is always available
Optionally limit data (hot data) vs optionally archive adata (cold data)
_ Azure Data Lake, SQL server 2019 big data cluster

11. QUANTUM COMPUTING AND THE FUTURE OF
SOFTWARE DEVELOPMENT
11
_ Sessions for quantum computing introduction and development
_ Introduction
History of computer (from Alan turing, Moores law, Ada)
Target problem : Airbus wall
Quantum tunnel Qubit : 4 qubit = 64bit, 16 Qubit = 1M bit
_ State of the art
Niche
Qunatum annealing : DWAVE 2048 qubits, google 72
ION Trap NIST 53 qubits MIT&Harvard 51 qubits
Uniersal
Superconducting 72qubits by google, 50qubits by IBM, 8 qubits by rigetti
Quntim dots intel 2 quits
Topological qubits Microsoft ...
_ Redefine softwar edevelopment
Software language, pattern, document, ALM will be elimitated
Quntum tunneling, superposition.teleportation,entanglement,hamiltonians,boolean oracles
Microsoft introduced Q#
Airbus wall

12. AI EVERYWHERE OPEN AND INTEROPERABLE
PLATFORM FOR AI WITH ONNX
12
_ ONNX ecosystem and Microsoft strategy
_ Introduction
ONNX model zoo : pretrained model : github.com/onnx/models
Custom vision service : customvision.ai (Azure)
Convert existing models : load existing model, convert, save onnx
_ Development of ONNX
Keras from keras.odels, import onnxmltools
Train models in Azure Machine Learning Services
_ Deployment of ONNX
Windows ML (desktop), other OS(mobile,IoT etc…)
Harware abstraction via DirectML, Virtualization ready
Deploy Azure Machine Learning
Model management services
Deploy as web service to ACI or AKS
Deploy on Azure Web services
_ ONNX Runtime (Open source , cross platform)
gihutb / microsoft /onnxruntie
Python, C#, C
_ Intel AI

13. BUILDING VERSATILE REAL-TIME AND BATCH DATA
PIPELINES FOR AI
13
_ Data Pipeline for AI
Ingest and prepare data
Gathering ,Store in a common place
Transform anlyze and publish
Multibple data source,Help Extract value,AI can help reveal insights
Resiliency
Design for failure, Dependency will fail, Never expect your code to be perfect
Cold and hot path
Speed layer
Batch layer(master data)-> serving layer(batch view)
_ On Azure
Azure Functions + ADF for orchestration
ADF : time trigger, tumbling window, event-based, no retry logic
Azure Functions : time trigger, event grid integrtion pipleine run eligibility, retry logic
Data rtransformation with Databricks
Databirkcs integration with ADF, JAR for processing data
Log analytics, Power BI, SQL Server
Monitoring and logging

14. PYTHON AND AI IN VISUAL STUDIO CODE, AZURE
NOTEBOOKS AND AZURE
14
_ Why AI , pet detector 2 tools ,
_ John Lam , Azure Notebook program manager
_ Tela Andrej Karpathy Software 2.0
_ Alpha go Deepmind Alpha Fold
_ Class for depp learning by Jeremy
_
_ Connect petdetector
_ github batdge Azure Notebook
_
_ Azure notebook runs on NC node
_
_ https://visionaidevkit.com/

15. REAL-TIME AI WITH AZURE ML, PROJECT BRAINWAVE,
AND INTEL FPGAS
15
_ Brainware
_ Ted Way
_ AI
_ Faster AI
_ CPU, GPU, FPGA, ASIC GPU drawback is batch size
_ Performance, flexibility , sclae
_ Training german shepard
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_ Tensor flowで書くだけで勝手にFPGAへコンパイル
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_ FPGA で8ms / image 程度に高速化
_

16. K E Y N O T E :
A I IN TER A C TIVE K EYN OTE B Y STEVE “ GU GGS ” GU GGEN H EIMER
16
_ Infusing AI
_
_ Windows, Visual Studio, Office
_ AI Pattern
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_ Pretrained to Custom AI
_ Depth of AI x customization axis
_
_ Data foundation
_ creating AI based solutions (Dynamics265 AI(
_
_ Humanities 1 of disiplin
_
_ AI journey
_ AI4HA
_ .net journty, internet journey