Why data science is the new frontier in software development

Transcription

1 Why data science is the new frontier in software development And why every developer should care Jeff

2 Assertion #1 Being a programmer is like being the god of your own universe

3 Assertion #2 Your universe is smaller than you think

4 Assertion #3 AI and ML let you do things that can't be done algorithmically Identify spam s Identify objects in images Translate speech in real time Detect faces in images Identify people in images Analyze sentiment in tweets Convert handwriting to text Translate text between languages Detect credit-card fraud in real time Filter adult content from images Filter profanity from text

5 A Brief History of AI Symbolic AI (e.g., chess) AI winter Present Boom driven by affordable GPUs, more data, and algorithmic advances

6 AI Taxonomy Machine Learning Artificial Intelligence Supervised Learning Deep Learning Unsupervised Learning Reinforcement Learning

7 Machine Learning Data Rules Traditional Programming Answers Data Answers Machine Learning Rules

8 Building an ML Model Regression Classification Support Vector Machines (SVM) Decision Trees and Random Forests Neural Networks Learning Algorithm Tune or replace algorithm Import Data Clean and Prepare Data Train Model Score and Evaluate Model

9 ML Tools and Libraries Scikit-learn and Spark MLlib Azure ML Studio and Amazon ML TensorFlow, Caffe, Keras, and CNTK* MATLAB, Torch, and many more * Primarily used for deep learning

10 scikit-learn

11 Azure ML Studio Visual ML modeling (no code) Drag-and-drop modules for cleaning, learning, scoring, tuning, and more Customizable with R and Python Easy operationalization

12 Azure ML Studio

13 Deep Learning The magic behind computer vision, speech translation, and much more Convolutional Neural Network Recurrent Neural Network Generative Adversarial Network

14 ConvNets Input Image Convolution Pooling Fully Connected Layers 24x24 6x6 26x26 24x24 6x6 24x24 6x6

15 ConvNets in Action

16 Transfer Learning Leverages existing DNNs to achieve acceptable accuracy with exponentially less data and training time Adds domain-specific layer(s) to layers already present in pretrained model Some libraries (e.g., Keras) now include popular pretrained DNNs

17 Pretrained ConvNets ResNet VGGNet MobileNet Inception/Xception And many more "Imagine a deep CNN architecture. Take that, double the number of layers, add a couple more, and it still probably isn t as deep as the ResNet architecture that Microsoft Research Asia came up with in late ResNet is a new 152 layer network architecture that set new records in classification, detection, and localization through one incredible architecture. Adit Deshpande, UCLA

18 ONNX Open Neural Network Exchange Format for interchangeable AI models developed by Microsoft, Amazon, and Facebook Backed by Intel, AMD, NVIDIA, and others

19 TensorFlow

20 Intelligence as a Service Azure Cognitive Services Amazon Cognitive Services Google Cloud AI IBM Watson

21 Azure Cognitive Services Vision Computer Vision API Content Moderator Custom Vision Service Emotion API Face API Video Indexer Speech Speech API Custom Speech Service Speaker Recognition API Translator Speech API Knowledge QnA Maker Custom Decision Service Language Bing Spell Check API Language Understanding (LUIS) Linguistic Analysis API Text Analytics API Translator Text API Web Language Model API Search Bing Autosuggest API Bing Custom Search API Bing Image Search API Bing News Search API Bing Video Search API Bing WebSearch API

22 Computer Vision API

23 Captioning a Photo POST /vision/v1.0/analyze?visualfeatures=description HTTP/1.1 Content-Type: application/json Content-Length: Host: westus.api.cognitive.microsoft.com:443 Ocp-Apim-Subscription-Key: {"url":"

24 JSON Result { } "description": { "tags": [ "man", "dune", "riding", "board", "hill", "sand" ], "captions": [ { "text": "a man riding a skateboard in the sand", "confidence": } ] }, "requestid": "03501f93-a0ba-4205-a778-6e02cce2b509", "metadata": { "width": 3072, "height": 2304, "format": "Jpeg" }

25 Custom Vision Service Build sophisticated image-classification models backed by deep neural networks Get acceptable accuracy with 50 to 100 images Build intelligent apps that invoke models using REST API Or export to ios (CoreML), Android (TensorFlow), or Windows (ONNX) and run locally

26 Custom Vision

27 Local Intelligence New libraries allow ML models to run on-device ML.NET (Microsoft) ML Kit (Google) Windows ML Faster, cheaper, and works without a connection

28 Are you a hot dog? Or not a hot dog?