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  2. MACHINE LEARNING PLATFORM ARCHITECTURE Models: AKS Batch predictions: SQL DB Analytics Power BI Synapse Business Apps Responsible ML tools Seamless studio experience Notebooks Designer Comprehensive MLOps Unified management across clouds and on-premises Serverless Compute Managed Kubernetes Azure Edge & Hybrid Azure Arc-enabled Kubernetes Edge/IoT Devices Reproducibility Automation Deployment Re-training Security and Governance Automated ML Azure ML Structured Data Unstructured Data All other ML scenarios; NLP, Vision, IoT, etc. Open Datasets Prepare Data Build & Train Manage & Monitor Deploy Data Bricks
  3. Generative AI Prompt: Write a tagline for an ice cream shop. Response: We serve up smiles with every scoop! Prompt: Table customers, columns = [CustomerId, FirstName, LastName, Company, Address, City, State, Country, PostalCode] Create a SQL query for all customers in Texas named Jane query = Response: SELECT * FROM customers WHERE State = 'TX' AND FirstName = 'Jane' Prompt: A white Siamese cat Response: GPT-3 Codex DALL·E
  4. Inferencing timeCost Capability Ada • Simple classification • Parsing and formatting text Curie • Answering questions • Complex, nuanced classification Davinci • Summarizing for specific audience • Generating creative content Babbage • Semantic search ranking • Moderately complex classification Azure OpenAI Service models Cushman-codex Davinci-codex Capability Codex GPT-3
  5. OPENAI Azure Cognitive Services – Speech & OpenAI Intelligent Transcription WebApp Azure OpenAI Service Audio Files
  6. Q&A
  7. DEMO
  8. DEMO

Notas do Editor

  1. AZURE ML can help us implement a Responsible MLOps process, for our entire ML lifecycle regardless of where our compute is running.  With built-in integration with Azure DevOps, developers can ensure model reproducibility, validation, deployment, and retraining.  Here, we can see that the platform architecture includes tools and services across the lifecycle – from data preparation to utilization that can be realized through deep integration with other Azure Data Services.   When the key components of a platform are Data, Model Building, Model Governance and Model Operations. Actually, Azure Machine Learning Platform empowers data scientists and developers with a wide range of capabilities to help with building, training, and deployment of machine learning models securely and at scale.  
  2. When GPT-3 is for text completion, and it is a set of models that can understand and generate natural language. it can also do interesting things with Classification and summarization, and we will get to some of these examples later.  The second is Codex that is actually a descendant of GPT-3 that can understand and generate code, including translating natural language to code. And there is a lot of interesting innovation in this space, for example take a given code and translate it to my SQL instead of SQL.  And DALL-E that is different from GPT 3 and Codex that can generate or manipulate images from natural language. And this becomes an interesting use especially for generating images on the fly. So instead of spending an hour to find prefect image for our power point presentation for example, we can generate image and do manipulation of images by providing further instructions for example to  change the background.