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What startups need to know about NLP, AI, & ML on the cloud.
- 9. Cognitive Services Intelligent APIs Data for inference Developer Tools Frameworks Extensive training data Pre-Trained Models with Transfer Learning Some training data Enhanced AIPre-Trained AI Bespoke AI Stages of AI
- 14. Sophisticated pretrained models To simplify solution development Azure Databricks Machine Learning VMs Popular frameworks To build advanced deep learning solutions TensorFlow KerasPytorch Onnx Azure Machine Learning LanguageSpeech … Azure Search Vision On-premises Cloud Edge Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Flexible deployment To deploy and manage models on intelligent cloud and edge Cognitive Services
- 16. Prepare Data Train & Test Model … Build model (your favorite IDE) Prepare Experiment
- 17. Prepare Data Register and Manage Model Train & Test Model Build Image … Build model (your favorite IDE) Deploy Service Monitor Model Prepare Experiment Deploy
- 24. Enter data Define goals Apply constraints Input Intelligently test multiple models in parallel Optimized model
- 27. My Computer Data Store Azure ML Workspace Compute Target Docker Image
- 45. Azure Text Analytics Azure Language Understanding Service Azure Machine Learning Workspace Azure Machine Learning Visual Interface Azure Machine Learning AutoML Azure Machine Learning Notebooks Azure Machine Learning Service API Azure Machine Learning Deployment Azure Machine Learning BERT Demo Beyond Word Embeddings NLP Tutorial
Editor's Notes
- There are three main solutions you can build with Azure AI. 1. Build apps that have the ability to interact with users naturally, with pre-trained AI models. 2. Build your own machine learning and deep learning solutions 3. Unlock insights lying latent in various types of documents.
- There are three main solutions you can build with Azure AI. 1. Build apps that have the ability to interact with users naturally, with pre-trained AI models. 2. Build your own machine learning and deep learning solutions 3. Unlock insights lying latent in various types of documents.
- There are three main solutions you can build with Azure AI. 1. Build apps that have the ability to interact with users naturally, with pre-trained AI models. 2. Build your own machine learning and deep learning solutions 3. Unlock insights lying latent in various types of documents.
- Microsoft Envision 2016
- We’ll mainly be using the Azure Machine Learning service in this talk.
- Experiments – this is where you try stuff / will have a history / track code through snapshots for every run / keep sanity (metadata) Data Stores – where does your data live – MNIST is fine until it doesn’t fit on your hard drive, you also need to share with others, and you spilt your coffee once on your machine and lost everything Compute – the metal where the experiments run (physical run on compute) Models – output (just the good ones – you specify what to keep and how to version) Images – package model with inference code and everything you need for it to run properly Deployments – actual running models with ip addresses and everything
- Compute – the actual machines where these things run (I only want these things to run when I have jobs!!) Storage – literal azure storage account where we store: data use it like a scratch space or local drive (you can add more!! Using datastores from before – this is basically the default datastore) Registry – where we store the images we talked about in the last slide (but also for running all of the experiments! – you can add your own – and run your experiments from there) Key vault – storing secrets that we need to attach a compute storage, dsvm, we don’t want people to have your passwords – how would they want us to word this? App Insights – this is for deployed models and their telemetry (you can also customize what you collect – but we won’t talk about that here) TODO: Need to discuss issue of cost TODO: Discuss that this is the baseline and does not include any compute resources that might be added (or external data sources)
- Our approach to ML frameworks is simple. We give customers the flexibility to choose their deep learning framework, without getting locked one framework. To help with this we’ve created a community project in partnership with Facebook that allows customers to train in one framework and use another one for inference Now, let me move to the ML services on Azure
- DANIEL slide Explain the flow step by step
- Use following slides as a recap of the demo you just did.
- TODO: maybe have YOLO output tensor diagram
- TODO: Use a screenshot from the YOLO deployment, not MNIST.