The document reviews key announcements and developments from Build 2019, including updates on Windows Subsystem for Linux (WSL 2), developer tools like .NET Core 3.0, and advancements in AI and machine learning. It emphasizes new features in the Windows terminal and highlights integration between development tools such as Azure DevOps and Azure Machine Learning. Additionally, it provides links to resources and sessions for further exploration of these topics.

1. Build 2019 Recap
Eran Stiller
Chief Technology Officer, CodeValue
erans@codevalue.net
@eranstiller
https://stiller.blog
https://codevalue.net

2. Agenda
 Book of News
 (My) Top Announcements
 Windows
 Developer Tools
 AI & ML
 And more…
 Personal Viewing Recommendation
2

3. Book of News
 Centralized view of all announcements
 https://aka.ms/Build2019BookofNews
4

4. Windows
5

5. Windows Subsystem for Linux (WSL)
 Top Customer Asks
6

6. Windows Subsystem for Linux 2 (WSL 2)
7

7. Windows Subsystem for Linux 2 (WSL 2)
8

8. Windows Subsystem for Linux 2 (WSL 2)
 WSL 2 speed comparison test WSL 1 on a Surface laptop
9
2.5x 4.7x 3.1x

9. VS Code Remote
 Build, run, and debug your Linux applications
 Directly from VS Code
 Using WSL as the backend
10

10. Windows Subsystem for Linux 2 (WSL 2)
 What’s new with the Windows Command Line
 https://mybuild.techcommunity.microsoft.com/sessions/77293
 The new Windows subsystem for Linux architecture: a deep dive
 https://mybuild.techcommunity.microsoft.com/sessions/77003
11

11. The New Windows Terminal
12
https://www.youtube.com/watch?v=8gw0rXPMMPE

12. The New Windows Terminal
 Multiple improvements
 Tab support
 Multiple shells
 Windows Command Line, PowerShell, Bash, and others…
 Modern design
 New font, Emoji support, Smooth zooming
 Extensible
 Completely open source
 https://github.com/microsoft/Terminal
 Watch session
 https://mybuild.techcommunity.microsoft.com/sessions/77004
13

13. React Native for Windows
 Build native Windows apps with React
 Open Source
 https://github.com/Microsoft/react-native-windows/tree/master/vnext
 Session
 https://mybuild.techcommunity.microsoft.com/sessions/77007
14

14. Developer Tools
15

15. .NET Core 3.0 Preview 5
 A lot of talk on:
 Desktop support on .NET Core 3
 gRPC support in ASP.NET Core 3
 Blazor
 https://dot.net/get-core3
16

16. 2014
Many
.NETs
.NET FRAMEWORK .NET CORE XAMARIN / MONO

17. 2014 2016
Many
.NETs
.NET
standard
.NET FRAMEWORK .NET CORE XAMARIN / MONO

18. XAMARIN / MONO.NET FRAMEWORK .NET
2014 Next2016
Many
.NETs
.NET
standard
.NET
.NET CORE
.NET STANDARD

19. .NET 5
INFRASTRUCTURE
.NET STANDARD
.NET—A unified platform
DESKTOP WEB CLOUD MOBILE GAMING IoT AI
WPF
Windows forms
UWP
ASP.NET Xamarin UnityAzure ARM32
ARM64
ML.NET
.NET for
Apache Spark

20. Distributed analytics engine for large datasets typically terabytes or petabytes
Currently available with Scala, Java, Python and R…
…but not .NET

21. dot.net/spark
Spark SQL + DataFrames Streaming & interactive Speed & productivityMachine Learning

22. Total execution time (seconds) for all 22 queries in the TPC-H benchmark (lower is better).
Data sourced from an internal run of the TPC-H benchmark, using warm execution on Ubuntu 16.04.
.NET for Apache Spark is designed for high
performance and performs better than python on
the TPC-H benchmark tpc.org/tpch.
The TPC-H benchmark consists of a suite of
business-oriented queries.
Learn more: dot.net/spark
406 433
375
.NET PYTHON SCALA

23. Custom ML made
easy with AutoML
Model Builder (a simple UI
tool) and CLI make it super
easy to build custom ML
Models.
Built for .NET
developers
Create custom ML models
using C# or F# without
having to leave the .NET
ecosystem.
Extended with
TensorFlow & more
Leverage other popular ML
frameworks (TensorFlow,
ONNX, Infer.NET, and more).
Trusted &
proven at scale
Use the same ML
framework which powers
Microsoft Office, Windows
and Azure
dot.net/ml
ML.NET 1.0
An open source and cross-platform machine learning framework

24. dot.net/ml
Product recommendation
Recommend products based on purchase history
using a matrix factorization algorithm.
Sentiment analysis
Analyze the sentiment of customer reviews
using a binary classification algorithm.
Price prediction
Predict taxi fares based on distance traveled
etc. using a regression algorithm.
Customer segmentation
Identify groups of customers with similar
profiles using a clustering algorithm.
Spam detection
Flag text messages as spam using a binary
classification algorithm.
Image classification
Classify images (e.g. broccoli vs pizza) using
a TensorFlow deep learning algorithm.
Sales forecasting
Forecast future sales for products using a
regression algorithm.
GitHub labeler
Suggest the GitHub label for new issues
using a multi-class classification algorithm.
Fraud detection
Detect fraudulent credit card transactions
using a binary classification algorithm.

25. dot.net/ml

26. Developer Tools
 .NET Platform Overview and Roadmap
 https://mybuild.techcommunity.microsoft.com/sessions/77031
 All the Developer Things with Hanselman and Friends
 https://mybuild.techcommunity.microsoft.com/sessions/77123
27

27. AI & ML
28

28. Automated ML
29

30. Text features
- Word ngrams
- unigrams
- bigrams
- trigrams …
- Character ngrams
- unigram …
- vocabulary size, idf, stop-words,
casing
- word embedding
- pretrained word
embeddings
- language?
- pretrained corpus?
- dimension?
- text similarity
- embedding-based
- ngram-based
Numerical features
- Discretization:
- k-means clustering
- n_clusters (2, 3, 4,..?)
- Equal sized bins
- n_bins (2, 3, 4,..?)
- Target encoding on bin-
categories.
- Scaling
- Normalization, percentile-based,
…
- Outlier removal
Categorical features
- one hot encoding
- Target encoding
- Cross-validation
- Regularization params
- Categoricals for trees
Time series forecasting features:
- lagged features
- Frequency detection
Timestamp features
- Day of week
- Day of month
- Day of year
- Month
- Hour
- Minute
- Holiday
- Quarter

31. Text features
- Word ngrams
- unigrams
- bigrams
- trigrams …
- Character ngrams
- unigram …
- vocabulary size, idf, stop-words,
casing
- word embedding
- pretrained word
embeddings
- language?
- pretrained corpus?
- dimension?
- text similarity
- embedding-based
- ngram-based
Numerical features
- Discretization:
- k-means clustering
- n_clusters (2, 3, 4,..?)
- Equal sized bins
- n_bins (2, 3, 4,..?)
- Target encoding on bin-
categories.
- Scaling
- Normalization, percentile-based,
…
- Outlier removal
Categorical features
- one hot encoding
- Target encoding
- Cross-validation
- Regularization params
- Categoricals for trees
Time series forecasting features:
- y- lagged features
- Frequency detection
Timestamp features
- Day of week
- Day of month
- Day of year
- Month
- Hour
- Minute
- Holiday
- Quarter
Without automated ML: hard, combinatorial explosion
With automated ML: easy, tailored to your dataset

32. xgboost alone: ~ 10^10 possible parameter configurations
Compute cost > 10^5 years

34. Datasets
yours perform well with X ML learner

35. ML Ops
36

36. MLOps = ML + DEV + OPS
Experiment
Data Acquisition
Business Understanding
Initial Modeling
Develop
Modeling
Operate
Continuous Delivery
Data Feedback Loop
System + Model Monitoring
ML
+ Testing
Continuous Integration
Continuous Deployment

37. App developer
using Azure DevOps
Build appCollaborate Test app Release app Monitor app
Model reproducibility Model retrainingModel deploymentModel validation
Data scientist using
Azure Machine Learning

38. Code, dataset, and
environment versioning
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning

39. Train model Validate
model
Deploy
model
Monitor
model
Retrain model
Model reproducibility Model retrainingModel deploymentModel validation
Build appCollaborate Test app Release app Monitor app
Azure DevOps integration
App developer
using Azure DevOps
Data scientist using
Azure Machine Learning

40. Sessions
 From Zero to AI Hero–Automatically generate ML models using Azure
Machine Learning service, Automated ML
 https://mybuild.techcommunity.microsoft.com/sessions/76975
 Breaking the Wall between Data Scientists and App Developers with MLOps
 https://mybuild.techcommunity.microsoft.com/sessions/76973
41

41. Small Bits 
42

43. Azure Blockchain Service
 Adir Ron is up next 
44

44. GitHub
 Azure Active Directory Support in GitHub Enterprise
 GitHub Identity Support for Azure
45

45. Resources
 All session videos and slide decks available here
 https://mybuild.techcommunity.microsoft.com/sessions
46

46. Personal Recommendation
 Inside Azure datacenter architecture with Mark Russinovich
 My personal favorite
 https://mybuild.techcommunity.microsoft.com/sessions/77002
47

47. Eran Stiller
Chief Technology Officer, CodeValue
erans@codevalue.net
@eranstiller
https://stiller.blog
https://codevalue.net