4. "More and more of ASP.NET is open source. We want to
make ASP.NET more pluggable, more open, more fun."
"We've got big things planned - some that will surprise you."
February 25, 2012
by Scott Hanselman
5. One ASP.NET ~ Katana Project
Katana Project
Helios
OWIN
6. .NET オープンソースの道のり
.NET
2001
ECMA 335
(CLI)
2002
.NET 1.0 for
Windows released.
Mono project
begins
2008
ASP.NET MVC
(web platform)
open source
April 2014
.NET Compiler
Platform (“Roslyn”)
open source
.NET Foundation
founded
Nov. 2014
.NET Core
(cross-platform)
project begins
2016
Mono project joins
.NET Foundation
Aug. 2017
.NET Core 2.0
released
Dec. 2018
.NET Core 2.2
released
.NET Core 3.0
preview
WinForms
and WPF go
open source
Fall 2019
.NET Core 3.0
17. 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 1.3
オープンソース & クロスプラットフォーム 機械学習フレームワーク
18. 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.
github.com/dotnet/machinelearning-samples