AWS Sagemaker 2018- Fully Managed Machine Learning Service

AWS Sagemaker 2018- Fully Managed Machine Learning Service, available at $19.99, has an average rating of 2.85, with 35 lectures, based on 46 reviews, and has 1313 subscribers.

You will learn about Complete understanding of AWS Sagemaker and way to develop a fully Managed Machine learning Service This course is ideal for individuals who are Anyone specially DataScientist and Developer who want to easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It is particularly useful for Anyone specially DataScientist and Developer who want to easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

Enroll now: AWS Sagemaker 2018- Fully Managed Machine Learning Service

Summary

Title: AWS Sagemaker 2018- Fully Managed Machine Learning Service

Price: $19.99

Average Rating: 2.85

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Complete understanding of AWS Sagemaker and way to develop a fully Managed Machine learning Service

Who Should Attend

Anyone specially DataScientist and Developer who want to easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

Target Audiences

Anyone specially DataScientist and Developer who want to easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don’t have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.

If you want to learn about Amazon SageMaker, I recommend you to go through this course which will cover in detail-

How it works?�This course provides an overview of Amazon SageMaker, explains key concepts, and describes the core components involved in building AI solutions with Amazon SageMaker. We recommend that you read this topic in the order presented.
This course explains how to set up your account and create your first Amazon SageMaker notebook instance.
Try a model training exercise – This course walks you through training your first model. You use training algorithms provided by Amazon SageMaker.�
Explore other topics here– Depending on your needs, the following:
- Submit Python code to train with deep learning frameworks – In Amazon SageMaker, you can use your own TensorFlow or Apache MXNet scripts to train models.�
- Use Amazon SageMaker directly from Apache Spark�
- Use Amazon AI to train and/or deploy your own custom algorithms – Package your custom algorithms with Docker so you can train and/or deploy them in Amazon SageMaker.�
- And a ton, more….is included in this course….

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Amazon Sagemaker & Built in Algorithm

Lecture 1: Training a model with Amazon Sagemaker

Lecture 2: Deploying a model

Lecture 3: Sagemaker Notebook Instance

Lecture 4: Validating ML models

Lecture 5: Setting up a notebook Instance

Lecture 6: Train a model with Built in Algorithm

Lecture 7: Train and Deploy a model

Lecture 8: Built in algorithm

Lecture 9: Built in algorithm-cont

Lecture 10: Linear Learner

Lecture 11: Factorization Machines,XGBoost Algorithm

Lecture 12: Image Classification Algorithm

Lecture 13: sequence2sequence

Lecture 14: Principal Component Analysis

Lecture 15: Neural Topic Model

Lecture 16: Blazing Text

Chapter 3: Using your own Algorithm

Lecture 1: Using your own Algorithm

Lecture 2: Using your own Inference code

Lecture 3: Automaticaly scaling

Lecture 4: Automatic scaling for Variant

Lecture 5: Target tracking scaling policy

Lecture 6: Scaling policy to production variant

Lecture 7: Editing and scaling policy

Lecture 8: config automatic scaling-additional

Lecture 9: Using tensor flow with Amazon Sagemaker

Lecture 10: Apache MXNet 1

Lecture 11: Apache MXNet 2

Lecture 12: AWS Sagemaker training &Inference with Apache Spark

Lecture 13: SM Estimater in a Spark Pipeline

Lecture 14: Understanding Resource Ownership

Lecture 15: IAM Policies

Lecture 16: Roles

Lecture 17: Monitoring Amazon Sagemaker

Lecture 18: Log file Entries

Instructors

XEH Academy
Former Instructor@ Microsoft,Cisco, GE, HP & JP Morgan Chase

Rating Distribution

1 stars: 15 votes
2 stars: 6 votes
3 stars: 5 votes
4 stars: 16 votes
5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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