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Classroom Contents
Taking Models to the Next Level with Azure Machine Learning
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- 1 Intro
- 2 The Story
- 3 Why Azure Cognitive Services
- 4 programming
- 5 Building a Baseline with AutoML
- 6 NLP-Architect Aspect Based Sentiment Analysis
- 7 Managing Tailwind Traders ML Challenges
- 8 Challenges of distributed training
- 9 Azure Machine Learning Typical end to end ML process Prepare
- 10 Create Compute
- 11 Create an experiment
- 12 Create a training file
- 13 Create an estimator
- 14 Submit the experiment to the cluster
- 15 Register the model
- 16 AMLS to deploy
- 17 Inference config inference_config - InferenceConfig
- 18 Deployment using AML
- 19 Deploy to ACI
- 20 Typical 'manual' approach to hyperparameter tu
- 21 Automated Hyperparameter Tuning Manage Active Jobs
- 22 Wrap up
- 23 Learning Resources Get started with Azure ML Services and the Python SDK aka.ms/AA3dzht
