🔥Edureka AWS Training: https://www.edureka.co/aws-certification-training This Edureka video on “Deploy an ML Model using Amazon Sagemaker” discusses what is Amazon Sagemaker and how you can build, train and deploy your Machine Learning Models in Amazon Sagemaker. These are the topics covered in the AWS Machine Learning Tutorial video: 00:00:00 Introduction 00:01:14 What is Amazon Sagemaker? 00:04:21 Create your AWS Account 00:06:46 Create your First Notebook Instance 00:17:39 Train your Model on AWS 00:24:37 Deploy your Model on AWS 00:26:33 Evaluate your Model on AWS 00:29:03 AWS SageMaker Case Study: Grammarly 🔹Check Edureka’s complete DevOps playlist here: http://goo.gl/O2vo13 🔹Check Edureka’s Blog playlist here: https://bit.ly/3gfNuZr ——————————————————————————————– 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ SlideShare: https://www.slideshare.net/EdurekaIN Castbox: https://castbox.fm/networks/505?country=in Meetup: https://www.meetup.com/edureka/ #Edureka #DeployAnMlModelUsingAmazonSagemaker #AWSTutorial #AWSCertification #AWSTraining #AWSMachineLearning #AWSMLDeployment #MachineLearningOnCloud #CloudComputing #AWS ——————————————————————————————– How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 30 hours of assignment, 20 hours of project 3. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! – – – – – – – – – – – – – – About [More]
Data scientists spend a lot of time on data cleaning and munging, so that they can finally start with the fun part of their job: building models. After you have engineered the features and tested different models, you see how the prediction performance improves. However, the job is not done when you have a high performing model. The deployment of your models is a crucial step in the overall workflow and it is the point in time when your models actually become useful to your company. In this session you will learn about various possibilities and best practices to bring machine learning models into production environments. The goal is not only to make live prediction calls or have the models available as REST API, but also what needs to be considered to maintain them. This talk will focus on solutions with Python (flask, Cloud Foundry, Docker, and more) and the well established ML packages such as Spark MLlib, scikit-learn, and xgboost, but the concepts can be easily transferred to other languages and frameworks. Speaker SUMIT GOYAL Software Engineer IBM
To learn more, please visit: https://aws.amazon.com/sagemaker Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this tech talk, we will introduce you to the concepts of Amazon SageMaker including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment of ML models. With zero setup required, Amazon SageMaker significantly decreases your training time and the overall cost of getting ML models from concept to production. Learning Objectives: – Learn the fundamentals of building, training & deploying machine learning models – Learn how Amazon SageMaker provides managed distributed training for machine learning models with a modular architecture – Learn to quickly and easily build, train & deploy machine learning models using Amazon SageMaker
Watch this presentation to learn how to effectively build and deploy TensorFlow based Deep learning models on the mobile platforms. Sample code: https://github.com/AndreaPisoni EVENT: TensorFlow and Deep Learning Singapore 2017 SPEAKER: Andrea Pisoni PERMISSIONS: The original video was published on Engineers.SG YouTube channel with the Creative Commons Attribution license (reuse allowed).