Overview
Syllabus
Intro
The Story
Why Azure Cognitive Services
programming
Building a Baseline with AutoML
NLP-Architect Aspect Based Sentiment Analysis
Managing Tailwind Traders ML Challenges
Challenges of distributed training
Azure Machine Learning Typical end to end ML process Prepare
Create Compute
Create an experiment
Create a training file
Create an estimator
Submit the experiment to the cluster
Register the model
AMLS to deploy
Inference config inference_config - InferenceConfig
Deployment using AML
Deploy to ACI
Typical 'manual' approach to hyperparameter tu
Automated Hyperparameter Tuning Manage Active Jobs
Wrap up
Learning Resources Get started with Azure ML Services and the Python SDK aka.ms/AA3dzht
Taught by
NDC Conferences