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1
Intro
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The Story
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Why Azure Cognitive Services
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programming
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Building a Baseline with AutoML
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NLP-Architect Aspect Based Sentiment Analysis
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Managing Tailwind Traders ML Challenges
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Challenges of distributed training
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Azure Machine Learning Typical end to end ML process Prepare
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Create Compute
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Create an experiment
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Create a training file
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Create an estimator
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Submit the experiment to the cluster
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Register the model
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AMLS to deploy
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Inference config inference_config - InferenceConfig
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Deployment using AML
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Deploy to ACI
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Typical 'manual' approach to hyperparameter tu
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Automated Hyperparameter Tuning Manage Active Jobs
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Wrap up
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Learning Resources Get started with Azure ML Services and the Python SDK aka.ms/AA3dzht
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the integration of text analysis intelligent services into business processes using Azure Machine Learning in this conference talk from NDC Sydney 2020. Learn about pre-built cognitive services models and progress to training custom neural models for Aspect-Based Sentiment Analysis using Intel NLP Architect. Discover when custom models are necessary and how to create them quickly with AutoML. Gain insights into fine-tuning model hyperparameters using HyperDrive. Follow the journey of Tailwind Traders as they implement these technologies, addressing challenges in distributed training and managing machine learning processes. Dive into the typical end-to-end ML process, including preparation, computation, experimentation, model registration, and deployment. Understand automated hyperparameter tuning and active job management. Conclude with valuable learning resources to further your Azure ML Services and Python SDK knowledge.

Taking Models to the Next Level with Azure Machine Learning

NDC Conferences
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