AWS Machine Learning Master Class | NetCom Learning

AWS Machine Learning Master Class Brent VishalDawson Nigam NetCom Learning

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AGENDA Introduction to AWS Machine Learning Strategies for achieving Machine Learning at scale Overview of Data Strategy Defining the right use case Steps on first Proof-of-Concept (POC) Best practices for MLOps

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WHAT IS AMAZON MACHINE LEARNING? • AWS now provides a robust, cloud-based service — Amazon SageMaker — so that developers of all skill levels can use machine learning technology.

• SageMaker is a fully managed machine learning service that helps you create powerful machine learning models. With SageMaker, data scientists and developers can build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

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AMAZON MACHINE LEARNING KEY CONCEPTS • Datasources contain metadata associated with data inputs to Amazon ML

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WHAT IS AMAZON MACHINE LEARNING? • AWS now provides a robust, cloud-based service — Amazon SageMaker — so that developers of all skill levels can use machine learning technology.

• SageMaker is a fully managed machine learning service that helps you create powerful machine learning models. With SageMaker, data scientists and developers can build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

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Amazon Machine Learning Key Concepts

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•Datasources contain metadata associated with data inputs to Amazon ML

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• ML Models generate predictions using the patterns extracted from the input data

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• Evaluations measure the quality of ML models

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• Batch Predictions asynchronously generate predictions for multiple input data observations

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• Real-time Predictions synchronously generate predictions for individual data observations

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• Real-time Predictions synchronously generate predictions for individual data observations

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MACHINE LEARNING CONCEPTS Machine learning (ML) can help you use historical data to make better business decisions. ML algorithms discover patterns in data and construct mathematical models using these discoveries.

• Solving Business Problems with Amazon Machine Learning • When to Use Machine Learning • Building a Machine Learning Application • The Amazon Machine Learning Process

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TRAINING ML MODELS • • • •

Types of ML Models Training Process Training Parameters Creating an ML Model

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DATA TRANSFORMATIONS FOR MACHINE LEARNING • • • • • •

Importance of Feature Transformation Feature Transformations with Data Recipes Recipe Format Reference Suggested Recipes Data Transformations Reference Data Rearrangement

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EVALUATING ML MODELS • • • • • • •

ML Model Insights Binary Model Insights Multiclass Model Insights Regression Model Insights Preventing Overfitting Cross-Validation Evaluation Alerts

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RECORDED WEBINAR VIDEO To watch the recorded webinar video for live demos, please access the link: https://bit.ly/3tPfqdR

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RECOMMENDED COURSES AND MARKETING ASSETS NetCom Learning offers a comprehensive portfolio for Data & AI » DEEP LEARNING ON AWS - Class scheduled on Sep 27 » PRACTICAL DATA SCIENCE WITH AMAZON SAGEMAKER- Class scheduled on Oct 04 » THE MACHINE LEARNING PIPELINE ON AWS - Class scheduled on Dec 06 » FREE AWS DIGITAL TRAINING You can also access the below Marketing Assets » Free Training - AWS Discovery Day - An official introduction to the core concepts of cloud and AWS » Free 1Hr Training - Architecting on AWS: Master Best Practices in 30 Minutes » Free On-Demand Training - Getting Started with Big Data on AWS » Blog - Top 10 Artificial Intelligence Trends For 2019

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