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AI with Azure Machine Learning services: Simplifying the data science process

AI with Azure Machine Learning services: Simplifying the data science process. Daniel Schneider Principal Program Manager Seth Juarez Loud Talker. BRK2304 . agenda. building your first machine learning model working well with others

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AI with Azure Machine Learning services: Simplifying the data science process

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  1. AI with Azure Machine Learning services: Simplifying the data science process Daniel Schneider Principal Program Manager Seth Juarez Loud Talker BRK2304 

  2. agenda building your first machine learning model working well with others showing your work while taking advantage of cloud resources optimizing your model choosing the right model (BONUS TRACK) deploying your model

  3. Azure AI Machine learning AI apps & agents Knowledge mining Azure Databricks Azure Machine Learning Azure Bot Service Azure Cognitive Services Azure Cognitive Search

  4. Azure AI Machine learning AI apps & agents Knowledge mining Azure Databricks Azure Machine Learning Azure Bot Service Azure Cognitive Services Azure Cognitive Search

  5. Machine Learning on Azure Sophisticated pretrained models To simplify solution development Cognitive Services … Vision Speech Language Azure Search Popular frameworks To build advanced deep learning solutions FPGA CPU GPU Pytorch TensorFlow Keras Onnx Productive services To empower data science and development teams Powerful infrastructure To accelerate deep learning Azure Databricks Azure Machine Learning Machine LearningVMs Flexible deployment To deploy and manage models on intelligent cloud and edge Cloud Edge On-premises

  6. Your first ML model

  7. Making a machine learning model Daniel Schneider

  8. programming algorithm answer input

  9. machine learning answer algorithm input

  10. machine learning science-y things model data

  11. science

  12. science

  13. working with others

  14. the team workspace - logical

  15. the team workspace - physical

  16. workspace, data stores, and compute Daniel Schneider

  17. the team workspace

  18. showing your work

  19. running experiments Daniel Schneider

  20. the team workspace

  21. Azure ML Workspace 1. Snapshot folder and send to experiment 2. create docker image 6. Stream stdout, logs, metrics Docker Image 5. Launch script 3. Deploy docker and snapshot to compute 6. Stream stdout, logs, metrics 7. Copy over outputs 4. Mount datastore to compute Compute Target Data Store My Computer

  22. optimizing your model

  23. hyperdrive in action Daniel Schneider

  24. choosing the right model

  25. automatic machine learning Daniel Schneider

  26. (BONUS TRACK) deploying your model

  27. Prepare Deploy Experiment … Build model (your favorite IDE) Register and Manage Model Deploy Service Monitor Model Prepare Data Train & Test Model Build Image

  28. register model

  29. register model

  30. Create images + Scoring file.py + Python Environment

  31. Scoring File

  32. Environment File

  33. Create the Image

  34. Images

  35. Deploy image + Scoring file.py + + Python Environment Azure Kubernetes Service (AKS) or Azure Container Instance

  36. Deploy image

  37. Deploy image

  38. Deploy image + Scoring file.py + + Python Environment Azure Kubernetes Service (AKS) or Azure Container Instance

  39. review building your first machine learning model downloaded standard model working well with others workspaces, data stores, compute, experiments showing your work while taking advantage of cloud resources data stores, compute, and experiments in action (with logging) optimizing your model hyperdrive choosing the right model automatic machine learning (BONUS TRACK) deploying your model model management and deployment

  40. the team workspace

  41. Other session on Azure ML Learn more: http://aka.ms/azureml-docs Start now: https://azure.microsoft.com/en-us/free/ Try it for free

  42. Learn more: http://aka.ms/azureml-docs Start now: https://azure.microsoft.com/en-us/free/ Try it for free Daniel Schneider Daniel.Schneider@microsoft.com Seth Juarez @sethjuarez

  43. Please evaluate this sessionYour feedback is important to us! Please evaluate this session through MyEvaluations on the mobile appor website. Download the app:https://aka.ms/ignite.mobileApp Go to the website: https://myignite.techcommunity.microsoft.com/evaluations

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