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How To Deploy A Machine Learning Model On AWS EC2 | AUG 2021 Updated | ML Model To Flask Website Hi, my name is Nitish Singh, and welcome to my YouTube channel. In this video, we will learn to deploy a machine learning model to AWS EC2. Step Build the model Export the model using Pickle/Joblib Build a Flask website to serve the model Deploy the website on AWS EC2 Create an AWS account Create an EC2 instance Edit the security group Download keygen(pem file) Download and install Putty and WinSCP Upload Flask website to EC2 using WinSCP Install packages on EC2 using Putty Code 🤍 Tags deploy ml model on aws,deploy ml model flask,ml model deployment using flask,ml model deployment,ml aws,aws ec2,flask python,machine learning projects,campusx,Nitish Singh campusx,pickle ml model,joblib,ml tutorial,ml cloud,machine learning on cloud #Campusx #NitishSingh #100DaysOfMachineLearning
Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more 🤍 Hello All, In this video we will see how we can deploy ML Models in AWS EC2 Instance Please download the code from the github url github url :🤍 #AWSEC2MLDEPLOYMENT Support me in Patreon: 🤍 You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: 🤍 Buy the Best book of Machine Learning, Deep Learning with python sklearn and tensorflow from below amazon url: 🤍 Connect with me here: Twitter: 🤍 Facebook: 🤍 instagram: 🤍 Subscribe my unboxing Channel 🤍 Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning! Deep Learning Playlist: 🤍 Data Science Projects playlist: 🤍 NLP playlist: 🤍 Statistics Playlist: 🤍 Feature Engineering playlist: 🤍 Computer Vision playlist: 🤍 Data Science Interview Question playlist: 🤍 You can buy my book on Finance with Machine Learning and Deep Learning from the below url amazon url: 🤍 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 THINGS to support my channel LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL
Hello, Guys, I am Spidy. I am back with another video. In this video, I am showing you how you can deploy Machine Learning Model as API in AWS EC2 Fruit & Vegetable Recognition Project Tutorial - 🤍 - Commands - Commands are based on Amazon Machine Image (AMI) # Installation Commands sudo su apt-install update git clone [YOUR Repository] cd [YOUR Repository] python3 -m pip install -r requirements.txt # Run permanent nohup python3 ec2_api.py [Close Terminal, the app will run as a service for permanent] # Stop Application ps -ef (Get process ID from here) kill [id] Donate►machinelearninghubai🤍okhdfcbank Note: If you want me to solve your errors and make the project run into the system, I will do it using a remote desktop, and it will be paid. You can reach me at kushalbhavsar58🤍gmail.com for your queries. 🔥 Don't forget to Subscribe My GitHub for free projects► 🤍 My store for buying paid projects► 🤍 Facebook► 🤍 Instagram► 🤍 Paypal► 🤍 Buy Coffee for me► 🤍 Playlist that you should check👇 Machine Learning College Projects► 🤍 Python College Projects► 🤍 Android App using Python► 🤍 "The video thumbnails were created using publicly available images from Google images and are used solely for thumbnail purposes. I do not claim ownership of these images. If you are the owner of any copyrighted content used in these thumbnails and want them removed or changed, please contact me and I will comply promptly. Thank you." #aws #awsec2 #awstutorial
🔥Edureka AWS 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: 🤍 🔹Check Edureka's Blog playlist here: 🤍 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: 🤍 Twitter: 🤍 LinkedIn: 🤍 Instagram: 🤍 Facebook: 🤍 SlideShare: 🤍 Castbox: 🤍 Meetup: 🤍 #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 24x7 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 the Course AWS Architect Certification Training from Edureka is designed to provide in depth knowledge about Amazon AWS architectural principles and its components. The sessions will be conducted by Industry practitioners who will train you to leverage AWS services to make the AWS cloud infrastructure scalable, reliable, and highly available. This course is completely aligned to AWS Architect Certification - Associate Level exam conducted by Amazon Web Services. During this AWS Architect Online training, you'll learn: 1. AWS Architecture and different models of Cloud Computing 2. Compute Services: Amazon EC2, Auto Scaling and Load Balancing, AWS Lambda, Elastic Beanstalk 3. Amazon Storage Services : EBS, S3 AWS, Glacier, CloudFront, Snowball, Storage Gateway 4. Database Services: RDS, DynamoDB, ElastiCache, RedShift 5. Security and Identity Services: IAM, KMS 6. Networking Services: Amazon VPC, Route 53, Direct Connect 7. Management Tools: CloudTrail, CloudWatch, CloudFormation, OpsWorks, Trusty Advisor 8. Application Services: SES, SNS, SQS Course Objectives On completion of the AWS Architect Certification training, learner will be able to: 1. Design and deploy scalable, highly available, and fault tolerant systems on AWS 2. Understand lift and shift of an existing on-premises application to AWS 3. Ingress and egress of data to and from AWS 4. Identifying appropriate use of AWS architectural best practices 5. Estimating AWS costs and identifying cost control mechanisms Pre-requisites There are no specific prerequisites for this course. Any professional who has an understanding of IT Service Management can join this training. There is no programming knowledge needed and no prior AWS experience required. If you are looking for live online training, write back to us at sales🤍edureka.in or call us at US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information.
To learn more, please visit: 🤍 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
6 Easy steps to deploy Machine Learning (ML) models in AWS EC2 Instance In this tutorial you will learn how to deploy a Machine Learning Application based out of Flask in AWS EC2 Instance. Keep following my videos to get quality content on Statistics, Machine Learning, Deep Learning, Data Science, AI & various other related topics. Software's required: WinSCP: 🤍 PuTTy: 🤍 Learn about Flask: 🤍 Learn about Heroku Deployment: 🤍 Learn about GCP Deployment: 🤍 Learn about Azure Deployment: 🤍 Few GitHub links: AWS: 🤍 Flask: 🤍 (Flask) Heroku+GCP: 🤍 (Same Flask code used for Heroku, and almost the same code deployed on Google Cloud Platform) Azure: 🤍 Video Editing: Poonam Shenoy (🤍 Interested in a 1-1 guidance? Connect with me!! : Facebook: 🤍 LinkedIn: 🤍 Tags Used machine learning amazon web services cloud computing aws deployment aws satyajit data science deploy ml models deploy ml models aws machine learning models cloud aws cloud practitioner aws sagemaker aws s3 aws machine learning certification aws ec2 ec2 deployment aws ec2 models aws ec2 machine learning satyajit pattnaik cloud models deploy end to end ml projects data science projects public cloud private cloud software defined networking #machinelearning #amazonwebservices #cloudcomputing #awsdeployment #awssatyajit #datascience #deploymlmodels #deploymlmodelsaws #machinelearningmodelscloud #awscloudpractitioner #awssagemaker #awss3 #awsmachinelearningcertification #awsec2 #ec2deployment #awsec2models #awsec2machinelearning #satyajitpattnaik #cloudmodels #deployendtoend #mlprojects #datascienceprojects #publiccloud #privatecloud #softwaredefinednetworking
Learn more about Amazon SageMaker at – 🤍 Amazon SageMaker enables you to quickly and easily deploy your ML models to the most scalable infrastructure. In this video, you will learn deployment options for your ML models with SageMaker
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Code github:🤍 ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! 🤍 All Playlist In My channel Complete ML Playlist :🤍 Complete NLP Playlist:🤍 Docker End To End Implementation: 🤍 Live stream Playlist: 🤍 Machine Learning Pipelines: 🤍 Pytorch Playlist: 🤍 Feature Engineering :🤍 Live Projects :🤍 Kaggle competition :🤍 Mongodb with Python :🤍 MySQL With Python :🤍 Deployment Architectures:🤍 Amazon sagemaker :🤍 Please donate if you want to support the channel through GPay UPID, Gpay: krishnaik06🤍okicici Discord Server Link: 🤍 Telegram link: 🤍 Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more 🤍 Please do subscribe my other channel too 🤍 Connect with me here: Twitter: 🤍 Facebook: 🤍 instagram: 🤍
In this tutorial we will learn how to deploy machine learning model via API to AWS lambda. We will see how to create FastAPI, and then containerize it using docker and upload to Amazon Elastic Container Registry (ECR). Code: 🤍
In this Tutorial you'll learn how to deploy Machine Learning models with FastAPI, Docker, and Heroku. Code: 🤍 Resources: 🤍 🤍 🤍 Get your Free Token for AssemblyAI Speech-To-Text API 👇🤍 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Timeline: 00:00 Introduction 01:08 Model Development 05:00 FastAPI app 08:44 Dockerize the app 14:14 Deployment to Heroku 17:46 Final Testing #MachineLearning #FastAPI
In this videos we will be seeing how we can deploy ml application using CI CD Pipelines in AWs cloud Project Code: 🤍 Join iNeuron's Data Science Masters Course with Job Guaranteed Starting From April 3rd 2023 🤍 Join the PWSKILLS Data Science Masters Course Best Affordable Data Science Course From Pwskills(6-7 Months) Impact Batch:- Data-Science-Masters (Full Stack Data Science) 1. Data Science Masters Hindi: 🤍 (Hindi) 2. Data Science Masters English: 🤍 (English) Join this channel membership to get access to materials: 🤍 check out the end to end project playlist 🤍 Check Out My Other Playlist Python Playlist: Python In English:🤍 Python In Hindi: 🤍 Stats Playlist: English 7 Days Statistics Live Session :🤍 Hindi: Stats Playlist: 🤍 Complete ML Playlist: 🤍 Hindi: ML Playlist: 🤍 5 DaysLive Deep Learning Playlist: 🤍 Complete Deep Learning Playlist: 🤍
Application deployment in AWS Step by Step | AWS deployment tutorial | AWS deployment strategies #unfolddatascience #datascience #datascience Hello , My name is Aman and I am a Data Scientist. All amazing data science courses at most affordable price here: 🤍 Understand all details of application used in the below 2 videos: 🤍 🤍 Complete AWS playlist: 🤍 Find all codes here: 🤍 Topics for the video: Application deployment in AWS Step by Step AWS deployment tutorial AWS deployment strategies AWS deployment in AWS AWS deployment interview questions AWS deployment group AWS deployment tamil AWS deployment telugu AWS deployment hindi About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well. Book recommendation for Data Science: Category 1 - Must Read For Every Data Scientist: The Elements of Statistical Learning by Trevor Hastie - 🤍 Python Data Science Handbook - 🤍 Business Statistics By Ken Black - 🤍 Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron - 🤍 Ctaegory 2 - Overall Data Science: The Art of Data Science By Roger D. Peng - 🤍 Predictive Analytics By By Eric Siegel - 🤍 Data Science for Business By Foster Provost - 🤍 Category 3 - Statistics and Mathematics: Naked Statistics By Charles Wheelan - 🤍 Practical Statistics for Data Scientist By Peter Bruce - 🤍 Category 4 - Machine Learning: Introduction to machine learning by Andreas C Muller - 🤍 The Hundred Page Machine Learning Book by Andriy Burkov - 🤍 Category 5 - Programming: The Pragmatic Programmer by David Thomas - 🤍 Clean Code by Robert C. Martin - 🤍 My Studio Setup: My Camera : 🤍 My Mic : 🤍 My Tripod : 🤍 My Ring Light : 🤍 Join Facebook group : 🤍 Follow on medium : 🤍 Follow on quora: 🤍 Follow on twitter : 🤍unfoldds Get connected on LinkedIn : 🤍 Follow on Instagram : unfolddatascience Watch Introduction to Data Science full playlist here : 🤍 Watch python for data science playlist here: 🤍 Watch statistics and mathematics playlist here : 🤍 Watch End to End Implementation of a simple machine learning model in Python here: 🤍 Learn Ensemble Model, Bagging and Boosting here: 🤍 Build Career in Data Science Playlist: 🤍 Artificial Neural Network and Deep Learning Playlist: 🤍 Natural langugae Processing playlist: 🤍 Understanding and building recommendation system: 🤍 Access all my codes here: 🤍 Have a different question for me? Ask me here : 🤍 My Music: 🤍
Build train and deploy model in sagemaker | sagemaker tutorial | sagemaker pipeline #sagemaker #aws #datascience #machinelearning Hello, My name is Aman and I am a Data Scientist. All amazing data science courses at the most affordable price here: 🤍 Book one on one session here(Note - These supports are chargable): 🤍 Follow on Instagram: unfold_data_science AWS playlist: 🤍 Practice from here : 🤍 About this video: Build train and deploy model in sagemaker sagemaker tutorial sagemaker pipeline sagemaker studio tutorials sagemaker endpoints sagemaker studio lab About Unfold Data science: This channel is to help people understand the basics of data science through simple examples in an easy way. Anybody without prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at a high level through this channel. The videos uploaded will not be very technical in nature and hence can be easily grasped by viewers from different backgrounds as well. Book recommendation for Data Science: Category 1 - Must Read For Every Data Scientist: The Elements of Statistical Learning by Trevor Hastie - 🤍 Python Data Science Handbook - 🤍 Business Statistics By Ken Black - 🤍 Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow by Aurelien Geron - 🤍 Category 2 - Overall Data Science: The Art of Data Science By Roger D. Peng - 🤍 Predictive Analytics By By Eric Siegel - 🤍 Data Science for Business By Foster Provost - 🤍 Category 3 - Statistics and Mathematics: Naked Statistics By Charles Wheelan - 🤍 Practical Statistics for Data Scientist By Peter Bruce - 🤍 Category 4 - Machine Learning: Introduction to machine learning by Andreas C Muller - 🤍 The Hundred Page Machine Learning Book by Andriy Burkov - 🤍 Category 5 - Programming: The Pragmatic Programmer by David Thomas - 🤍 Clean Code by Robert C. Martin - 🤍 My Studio Setup: My Camera: 🤍 My Mic: 🤍 My Tripod: 🤍 My Ring Light: 🤍 Join the Facebook group : 🤍 Follow on medium: 🤍 Follow on quora: 🤍 Follow on Twitter: 🤍unfoldds Watch the Introduction to Data Science full playlist here: 🤍 Watch python for data science playlist here: 🤍 Watch the statistics and mathematics playlist here : 🤍 Watch End to End Implementation of a simple machine-learning model in Python here: 🤍 Learn Ensemble Model, Bagging, and Boosting here: 🤍 Build Career in Data Science Playlist: 🤍 Artificial Neural Network and Deep Learning Playlist: 🤍 Natural language Processing playlist: 🤍 Understanding and building a recommendation system: 🤍 Access all my codes here: 🤍 Have a different question for me? Ask me here : 🤍 My Music: 🤍
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The deployment of ML models in production is a delicate process filled with challenges. You can deploy a model via a REST API, on an edge device, or as as an off-line unit used for batch processing. You can build the deployment pipeline from scratch, or use ML deployment frameworks. In this video, you'll learn about the different strategies to deploy ML in production. I provide a short review of the main ML deployment tools on the market (TensorFlow Serving, MLFlow Model, Seldon Deploy, KServe from Kubeflow). I also present BentoM - the focus of this mini-series - describing its features in detail. = 1st The Sound of AI Hackathon (register here!): 🤍 Join The Sound Of AI Slack community: 🤍 Interested in hiring me as a consultant/freelancer? 🤍 Connect with Valerio on Linkedin: 🤍 Follow Valerio on Facebook: 🤍 Follow Valerio on Twitter: 🤍 = Content: 0:00 Intro 0:36 ML deployment strategies 1:32 Basic ML deployment 3:27 Disadvantages of basic ML deployment 4:57 Overview of ML deployment tools 9:54 BentoML 14:00 What's next?
You've collected, cleaned and prepared your data, chose the right machine learning algorithm, and trained a model. Now you're ready to test your model with real users and will need to decide the best way to deploy it into production. In this episode, Gonzalo covers the basic patterns to deploy your machine learning model into production for real-time predictions. Check out more resources for architecting in the #AWS cloud: 🤍 Additional Resources: 🤍 🤍 #AWS #AmazonWebServices #CloudComputing #BackToBasics #MachineLearning
Talk 🤍 AWS Africa Virtual Day, 9/7/2020. An up to date overview of all SageMaker capabilities, with an end to end demo: building a classifier with XGBoost, using SageMaker Debugger, SageMaker Model Monitor, Real-Time Endpoints, Batch Transform, and Spot Instances in the process. ⭐️⭐️⭐️ Don't forget to subscribe to be notified of future videos ⭐️⭐️⭐️ ⭐️⭐️⭐️ Want to buy me a coffee? I can always use more :) 🤍 ⭐️⭐️⭐️ For more content: * AWS blog: 🤍 * Medium blog: 🤍 * YouTube: 🤍 * Podcast: 🤍 * Twitter 🤍
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Hello, Guys, I am Spidy. I am back with another video. In this video, I am showing you how you can deploy Python Streamlit App on AWS EC2. Deploy the Same App with your Own Custom Domain - 🤍 - Commands - Commands are based on Amazon Machine Image (AMI) # Installation Commands sudo su yum update yum install git git clone [YOUR Repository] cd [YOUR Repository] python3 -m pip install -r requirements.txt # Run commands python3 -m streamlit run filename.py # Run permanent nohup python3 -m streamlit run filename.py [Close Terminal, app will run as a service for permanent] # Stop Application ps -ef (Get process ID from here) kill [id] Donate►machinelearninghubai🤍okhdfcbank Note: If you want me to solve your errors and make the project run into the system, I will do it using a remote desktop, and it will be paid. You can reach me at kushalbhavsar58🤍gmail.com for your queries. 🔥 Don't forget to Subscribe My GitHub for free projects► 🤍 My store for buying paid projects► 🤍 Facebook► 🤍 Instagram► 🤍 Paypal► 🤍 Buy Coffee for me► 🤍 Playlist that you should check👇 Machine Learning College Projects► 🤍 Python College Projects► 🤍 Android App using Python► 🤍 "The video thumbnails were created using publicly available images from Google images and are used solely for thumbnail purposes. I do not claim ownership of these images. If you are the owner of any copyrighted content used in these thumbnails and want them removed or changed, please contact me and I will comply promptly. Thank you." #aws #awsec2 #awstutorial
So you have built your machine learning model, so now what? In this video, I will share to you 4 approaches that you can use for deploying your machine learning model. I also share how I deploy my machine learning models in my own research work. 🌟 Buy me a coffee: 🤍 ⭕ Timeline 1:08 Obtaining the final machine learning model 1:25 Deploying the machine learning (ML) model 1:37 ML model as a data product 1:47 Four approaches to ML model deployment 1:52 Deployment format to use depends on the use case 2:30 Save ML model as objects 2:41 In Python, we can save as a pickle object 2:44 In R, we can save as a RDS object 3:01 Transfer ML-derived rules to a custom function, then apply this to make prediction 3:28 Create API to receive input and make prediction 3:59 Embed ML model inside a web application 4:04 In Python, popular web framework includes: Django, Flask and Dash 4:10 In R we have Dash and Shiny 4:21 Dash and Shiny are suitable for making data-driven dashboard 4:28 Shiny code can be deployed on your own web server or shinyapps.io The idea for this video was suggested in a comment by seshendra vemuri ⭕ Playlist: Check out our other videos in the following playlists. ✅ Data Science 101: 🤍 ✅ Data Science YouTuber Podcast: 🤍 ✅ Data Science Virtual Internship: 🤍 ✅ Bioinformatics: 🤍 ✅ Data Science Toolbox: 🤍 ✅ Streamlit (Web App in Python): 🤍 ✅ Shiny (Web App in R): 🤍 ✅ Google Colab Tips and Tricks: 🤍 ✅ Pandas Tips and Tricks: 🤍 ✅ Python Data Science Project: 🤍 ✅ R Data Science Project: 🤍 ⭕ Subscribe: If you're new here, it would mean the world to me if you would consider subscribing to this channel. ✅ Subscribe: 🤍 ⭕ Recommended Tools: Kite is a FREE AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite and I love it! ✅ Check out Kite: 🤍 ⭕ Recommended Books: ✅ Hands-On Machine Learning with Scikit-Learn : 🤍 ✅ Data Science from Scratch : 🤍 ✅ Python Data Science Handbook : 🤍 ✅ R for Data Science : 🤍 ✅ Artificial Intelligence: The Insights You Need from Harvard Business Review: 🤍 ✅ AI Superpowers: China, Silicon Valley, and the New World Order: 🤍 ⭕ Stock photos, graphics and videos used on this channel: ✅ 🤍 ⭕ Follow us: ✅ Medium: 🤍 ✅ FaceBook: 🤍 ✅ Website: 🤍 (Under construction) ✅ Twitter: 🤍 ✅ Instagram: 🤍 ✅ LinkedIn: 🤍 ✅ GitHub 1: 🤍 ✅ GitHub 2: 🤍 ⭕ Disclaimer: Recommended books and tools are affiliate links that gives me a portion of sales at no cost to you, which will contribute to the improvement of this channel's contents. #dataprofessor #machinelearning #modeldeployment #deployml #deploymachinelearning #datascienceproject #learnr #rprogramming #learnrprogramming #python #learnpython #shiny #dash #datascience #datamining #bigdata #datascienceworkshop #dataminingworkshop #dataminingtutorial #datasciencetutorial #ai #artificialintelligence #r
In this video, I will show you how to deploy machine learning model to production on amazon aws ec2 instance. We will use nginx web server that will server http requests. For AWS EC2 we will use ubuntu server on which we will deploy our web application as well as python flask server. Using nginx reverse proxy /api requests will be routed to python flask server running on same machine. Here is the timeline and list of topics we are covering today, Code: 🤍 Topics - 00:00 introduction 00:47 Technical architecture of project 01:44 Setup nginx on windows (dev environment) 08:54 Chrome javascript debugging tips 10:11 Launch EC2 instance on amazon AWS 14:26 Connect to ec2 instance using ssh (git bash client) 15:57 Copy code to cloud using WinScp 18:05 install nginx on ec2 19:30 nginx setup on ec2 (ubuntu server) 25:49 install python packages for flask server Do you want to learn technology from me? Check 🤍 for my affordable video courses. Website: 🤍 Facebook: 🤍 Twitter: 🤍
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples. Complete Title: AWS re:Invent 2018: [REPEAT] Build, Train, & Deploy ML Models Quickly & Easily with Amazon SageMaker, ft. 21st Century Fox (AIM404-R)
PyData Berlin 2018 Take your machine learning model out of your desk drawer and show its benefit to the world through a simple API using AWS Lambda and API gateway. This tutorial will bridge the gap between having a machine learning model (e.g. in your Jupyter notebooks) and taking it to a level where others can benefit from it (i.e. through an API). Slides: 🤍 - 🤍pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: 🤍
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In this session, explore how state-of-the-art algorithms built into Amazon SageMaker are used to detect declines in machine learning (ML) model quality. One of the big factors that can affect the accuracy of models is the difference in the data used to generate predictions and what was used for training. For example, changing economic conditions could drive new interest rates affecting home purchasing predictions. Amazon SageMaker Model Monitor automatically detects drift in deployed models and provides detailed alerts that help you identify the source of the problem so you can be more confident in your ML applications. Learn more about re:Invent 2020 at 🤍 Subscribe: More AWS videos 🤍 More AWS events videos 🤍 #AWS #AWSEvents
In this video you'll learn how to use AWS Amplify Hosting's drag-and-drop UI to deploy an app to AWS Amplify's global CDN, including over 200 points of presence around the world. Learn more about AWS Amplify at - 🤍 Subscribe: More AWS videos - 🤍 More AWS events videos - 🤍 #AWS #AWSAmplify
Course name: “Machine Learning & Data Science – Beginner to Professional Hands-on Python Course in Hindi” In Student Mark Predictor ML Project Part-3, we have deployed a machine learning model on the Amazone AWS EC2 instance using the Python Flask framework. In the Machine Learning/Data Science End to End Project in Python Tutorial in Hindi, we explained each and every step of the Machine Learning Project / Data Science Project Deployment in detail. Project name: Student Mark Prediction Source code & study material: 🤍 Part-1 Student Mark Predictor ML Project 🤍 Part-2 Student Mark Predictor ML Project Deployment Using Flask on Local Machine: 🤍 Anaconda Navigator, Jupyter Notebook, Spyder Installation: 🤍 ……………………………………………………………………………………………………………………………………………………………. Course Playlists- - Machine Learning Project in Hindi: 🤍 Machine Learning & Data Science - Beginner to Professional Hands-on Python Course in Hindi: 🤍 Python NumPy Tutorial in Hindi: 🤍 Python Pandas Tutorial in Hindi 🤍 Python Matplotlib Tutorial in Hindi: 🤍 Python Seaborn Tutorial in Hindi: 🤍 …………………………………………………………………………………………………………………… For more information: Contact Us: = -Website: 🤍 -Facebook: 🤍 -Instagram: 🤍 -Twitter: 🤍 -LinkedIn: 🤍 …………………………………………………………………………………………………………………… #MachineLearningProjects #DataScienceProject #MLProjectsDeployment #MachineLearningTutorialInHindi #IndianAIProduction
This tutorial shows how to set-up a Machine Learning Architecture in Amazon Web Services by using combination of Amazon SageMaker, AWS Lambda Function, and Amazon API Gateway. The main idea is to build a workflow in cloud where a regular end user from public internet can make requests on pre-trained ML model and get a prediction (or AI-based insights) in a second back. From technical perspective, the backed works in such way: the the end use input his/her data and send it to API Gateway which uses REST API. Once the API service receive this input data, it forward it to pre-defined AWS Lambda function. Here, AWS Lambda function triggers the ML model endpoint which was created after the model training was completed and it's artifact saved in S3 bucket. Finally, the ML endpoint after received user input data, make a prediction using a pre-trained ML model and send back a predicted value in the same way through AWS Lambda to Amazon API Gateway. So, this video provides one of many ways how to deploy a Machine Learning model on the cloud using AWS. In this video, the usage of Amazon API Gateway is demonstrated either by using AWS Console and a custom Python script (where you can flexible leverage the API functionality). For demonstration purposes, in this video as an example I use breast cancer diagnosis prediction Machine Learning problem. The ML model endpoint was created specifically on this use case. You can find this notebook directly from AWS SageMaker Notebook templates. Documentation used in this tutorial, where you can find snippets for IAM role and AWS Lambda code in Python programming language used in this video: 🤍 Read more related to this use case: - Amazon IAM: 🤍 - Amazon SageMaker: 🤍 - AWS Lambda Function: 🤍 - Amazon API Gateway: 🤍 - Also, I highly recommend to refresh you knowledge on IAM (Identity & Access Management), AWS Roles, Policies and Groups with this video: 🤍 The content of the tutorial: 0:00 - Explanation of ML architecture on AWS 2:15 - AWS Hand-on! Create AWS SageMaker Notebook Instance 4:49 - Explanation of Jupyter Notebook and train the ML model 8:17 - Build the SageMaker Endpoint for ML model 11:11 - Set-up AWS Lambda function 16:32 - Set-up Amazon API Gateway 19:05 - Test the ML architecture on AWS I hope this tutorial was useful for you. If need any help, or wanna to share your experience or suggest a topic for the next video, please drop a comment below! - Thank you! #sagemaker #lambdafunction #apigateway Slava Ukraina!
AWS Certified Machine Learning - Specialty validates your expertise in building, training, tuning, and deploying machine learning models on AWS. Find preparation resources or schedule your exam. Learn more about AWS Training and Certification at - 🤍 Subscribe: More AWS videos 🤍 More AWS events videos 🤍 #AWS #AWSCertified
AWS provides a number of different machine learning products (🤍 that you or your business can leverage. Learn about the three layers of the machine learning stack. Experts who are comfortable building and training their own machine learning models can take advantage of AWS’s Framework & Interfaces layer. Developers and data scientists can leverage AWS’s ML Platforms layer using Amazon SageMaker to build, train, and deploy machine learning models without having specialized expertise in ML or having to think about the infrastructure like you would at the Framework & Interface layer. Developers who want to make calls to APIs to add machine learning services to their applications without building and training their own models can take advantage of the Application Services layer. Simply call APIs to perform image processing, voice recognition, video processing, speech synthesis, or other machine learning services. Start taking advantage of machine learning on AWS today like the NFL, Netflix, Zillow, and other large businesses have!
#cartoonify #aws #serverless # Subscribe to my channel on this link 🤍 How to deploy CartoonGAN, the model behind Cartoonify, on a serverless architecture using AWS? In this video, we will: - Look at serverless architectures and understand why they matter and why you should consider them - Use the Serverless framework to define the infrastructure as code - Deploy the model and test the API endpoint from a notebook By the end of this video, you should be able to reuse the code of this project, to deploy your own scalable application. Code: 🤍 Learn about this project through my blog post on Medium: 🤍 Music: 🤍bensound.com Follow me at: - Medium: 🤍 - My blog: 🤍 - Twitter: 🤍 - Github: 🤍 - Linkedin: 🤍
Learn more about Amazon SageMaker at – 🤍 With Amazon SageMaker, you have the flexibility to bring in your own model and leverage the capabilities of the service. In this video, we will dive deep into how you can bring your own model into SageMaker.
In this video lecture we will demonstrate a complete project on how you can apply machine learning on Titanic dataset using the AWS Sagemaker service. The project starts from the basics of loading the dataset from your storag bucket (S3) Importing it to the Jupyter Notebook, performing the exploratory data analysis for data cleaning/feature engineering. Then the project focus on splitting the dataset followed by training the model, deploying it to get the AWS Sagemaker endpoints to perform the survival prediction. This is a project for machine learning on AWS sagemaker. For complete series stay tuned to our youtube channel. This course will be live soon on our website 🤍skillcurb.com and our Udemy institute page. Course Timeline: 00:00 - 02:35 Problem Statement 02:36 - 06:28 Importing Dataset 06:29 - 13:13 Exploratory Data Analysis 13:14 - 19:46 Data Cleaning Part I 19:47 - 26:50 Data Cleaning Part II 26:51 - 29:22 Splitting Dataset into Train and Test 29:23 - 36:07 Training Model in SageMaker 36:08 - 41:06 Deploying Model 41:07 - 44:40 Survival Prediction and Deleting Endpoints #MachineLearning #SageMaker #AWS Please subscribe to our Channel to get Free Content on the Cutting Edge Technology.
Swami Sivasubramanian, VP Machine Learning, Amazon Web Services delivers the first-ever Machine Learning Keynote at re:Invent. Hear how AWS is freeing builders to innovate on machine learning with the latest developments in AWS machine learning, demos of new technology, and insights from customers. Including the launch of Distributed Training on SageMaker, SageMaker Clarify, Deep Profiling for SageMaker Debugger, SageMaker Edge Manager, Amazon Redshift ML, Amazon Neptune ML, Amazon Lookout for Metrics, and Amazon HealthLake. Guest speakers include Jennifer Langton, NFL and Elad Benjamin, Philips with demos and deep dives from AWS speakers including Dr. Nashlie Sephus, Dorothy Li, and Dr. Matt Wood. Launch Announcements: 00:00 Machine Learning Keynote 15:56 Distributed Training on SageMaker 36:16 SageMaker Clarify 43:16 Deep Profiling for SageMaker Debugger 53:29 SageMaker Edge Manager 1:01:58 Amazon Redshift ML 1:04:30 Amazon Neptune ML 1:15:44 Amazon Lookout for Metrics 1:36:40 Amazon HealthLake Demos: 45:54 SageMaker 1:07:18 Quicksight Q 1:19:50 Industrial AI 1:36:40 Amazon HealthLake Guest speakers include: 21:56 Jennifer Langton, NFL 1:41:40 Elad Benjamin, Philips Subscribe: More AWS videos 🤍 More AWS events videos 🤍 #AWS
Learn more at - 🤍 Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? This session demonstrates how cloud-based machine learning frameworks can help turn your data into intelligence. We introduce the general machine learning process and suite of AWS services (Comprehend, Rekognition, Polly, Lex and Transcribe) which reduces the undifferentiated heavy lifting for customers to deploy machine learning solutions. We will also be covering a demo of Amazon SageMaker with a computer vision demo. Presenter: - Jason Hunter, Enterprise Solutions Architect, Amazon Web Services
This AWS SageMaker tutorial demonstrates AWS Sagemaker using the “linear learner” algorithm which you can see here: 🤍 BTW, you’ll see this example using the “mnist.pkl.gz” dataset which is the globally known MNIST dataset. Info about that can be found here: 🤍 The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning.It was created by "re-mixing" the samples from NIST's original datasets. The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. Furthermore, the black and white images from NIST were normalized to fit into a 28x28 pixel bounding box and anti-aliased, which introduced grayscale levels. AWS recommends using this instead of their original ML service (which is not available for new accounts). – Simplified ML service which allows you to build/deploy your ML models (using many different out of the box algorithms) to AWS. The built in algorithms are not pre-trained so we need to format the training data to fit the model input specifications. Sagemaker will save the model parameters to S3 once training is completed. You can set up https end points. – Linear Learner and Factorization Machine algorithms supported for “classification and regression” and Seq2seq for text summarization (speech to text). K-means Clustering for Clustering (logically grouping data) and Principal components analysis (Dimensionality Reduction). Xgboost, DeepAR (Face recognition), etc.. Regression – Output prediction is a continuous real value Classification – Output prediction is a categorical binary value (a vegetable or mineral for example) – Uses services like AWS Glue: AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores. You can use that to move data around from Redshift, Aurora and such as input to your ML models. – Has many built in algorithms so you as the developer don’t have to write any code. Each of the “models” (out of the box) are hosted in Docker containers on AWS. – Uses the open source Jupyter (Python) notebooks which is used by many data scientists to load/train the models with input data. – Once you train your models, you can create “endpoints” where your deployed model can be accessed programmatically by your software, etc.. – To use a built in algorithm 1) Retrieve the training data (Explore and clean the data) 2) Format and serialize the data (put it in the format that the algorithm wants to see) and then upload it to S3 3) Train with the built in algorithm (stored in containers), set up the estimators and train with the input data. 4) Deploy the model which creates an endpoint configuration and endpoint for the prediction responses. 5) Use the endpoint for inference. – boto3 python sdk offers access to other AWS services such as S3 and EC2, etc. #AWS #SageMaker #AWSSageMaker
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