17 OCT 2024 - Welcome Back to TorrentFunk!
Get your pirate hat back out. Streaming is dying and torrents are the new trend.
Account Registration works again and so do Torrent Uploads.
We invite you all to start uploading torrents again!

TORRENT DETAILS

[FTUForum.com] [UDEMY] Deployment Of Machine Learning Models [FTU]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	3b823b10b12df325cf7a086be6f52a79802fd8c0
Size:	3.6 GB in 99 files
Release Date(s):	Torrent Release: 10-03-2019 by user
Swarm:	0 Seeds & 0 Peers
Vote:	No votes yet.

DESCRIPTION

Build Machine Learning Model APIs

Created by: Soledad Galli

Last updated: 2/2019

Language: English

Caption (CC): Included

Torrent Contains: 304 Files, 15 Folders

Course Source: https://www.udemy.com/deployment-of-machine-learning-models/

What you'll learn

• Deploy machine learning models into the cloud

• Build machine learning model APIs

• Send and receive requests from deployed machine learning models

• Design testable, version controlled and reproducible production code for model deployment

• Build reproducible machine learning pipelines

• Understand the optimal machine learning architecture

• Create continuous and automated integrations to deploy your models

• Understand the different resources available to you to productionise your models

Requirements

• A Python installation

• A Jupyter notebook installation

• Python coding skills including pandas and scikit-learn

• Familiarity with Machine Learning algorithms

• Familiarity with git

Description

Learn how to put your machine learning models into production.

What is model deployment?

Deployment of Machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built.

When we think about data science, we think about how to build machine learning models. We think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. However, how we are going to actually use those models is often neglected. And yet this is the most important step in the machine learning pipeline. Only when a model is fully integrated with the business systems, we can extract real value from its predictions.

Why take this course?

This is the first and only online course where you can learn how to deploy machine learning models. In this course, you will learn every aspect of how to put your models in production. The course is both comprehensive, and yet easy to follow. Throughout this course you will learn all the steps and infrastructure required to deploy machine learning models professionally.

In this course, you will have at your fingertips, the sequence of steps that you need to follow to deploy a machine learning model, plus a project template with full code, that you can adapt to deploy your own models.

What is the course structure?

The course begins from the most common starting point for the majority of data scientists: a jupyter notebook with a machine learning model trained in it. The course will take you through all the necessary steps and infrastructure required to take that model into the cloud, where it can be called from the other systems in the business.

The lectures include an explanation of the systems and architecture required to put models into production, followed by presentations on best coding practices for building reproducible pipelines and testable, versioned, error free production code. The lectures include videos that cover the different scripts required for model deployment.

Who are the instructors?

We have gathered a fantastic team to teach this course. Sole is a leading data scientist in finance and insurance, with 3+ years of experience in building and implementing machine learning models in the field, and multiple IT awards and nominations. Chris is an AI software engineer with enormous experience in building APIs and deploying machine learning models, allowing business to extract full benefit from their implementation and decisions.

Who is this course for?

This course is suitable for data scientists looking to deploy their first machine learning model, and software developers looking to transition into AI software engineering. Deployment of machine learning models is a very advanced topic in the data science path so the course will also be suitable for intermediate and advanced data scientists.

To sum up:

With more than 50 lectures and 8 hours of video this comprehensive course covers every aspect of model deployment. Throughout the course you will use python as your main language and other open source technologies that will allow you to host and make calls to your machine learning models.

Who this course is for:

• Data scientists who want to deploy their first machine learning models

• Data scientists who want to learn best practices around model deployment

• Software developers who want to transition into artificial intelligence

• Intermediate and advanced data scientists who want to level up their skills

• Data engineers who build data pipelines to productionise machine learning models

• Lovers of coding and open source.

For More Udemy Free Courses >>> http://www.freetutorials.eu

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.freetutorials.eu/

VISITOR COMMENTS (0

)

FILE LIST

Filename		Size
	10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.mp4	26.9 MB
	10. Deploying to a PaaS (Heroku) without Containers/1. 10.1 - Introduction.vtt	4.4 KB
	10. Deploying to a PaaS (Heroku) without Containers/1.1 Section10.1_Notes.pdf.pdf	76.7 KB
	10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.mp4	20.9 MB
	10. Deploying to a PaaS (Heroku) without Containers/2. 10.2 - Heroku Account Creation.vtt	2.3 KB
	10. Deploying to a PaaS (Heroku) without Containers/2.1 Section10.2_Notes.pdf.pdf	61.4 KB
	10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.mp4	32.2 MB
	10. Deploying to a PaaS (Heroku) without Containers/3. 10.3 - Heroku Config.vtt	4.8 KB
	10. Deploying to a PaaS (Heroku) without Containers/3.1 Section10.3_Notes.pdf.pdf	69 KB
	10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.mp4	12.3 MB
	10. Deploying to a PaaS (Heroku) without Containers/4. 10.4 - Testing the Deployment Manually.vtt	1.6 KB
	10. Deploying to a PaaS (Heroku) without Containers/4.1 Section10.4_Notes.pdf.pdf	69.8 KB
	10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.mp4	29.1 MB
	10. Deploying to a PaaS (Heroku) without Containers/5. 10.5 - Deploying to Heroku via CI.vtt	3.7 KB
	10. Deploying to a PaaS (Heroku) without Containers/5.1 Section10.5_Notes.pdf.pdf	67.9 KB
	10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.mp4	13.5 MB
	10. Deploying to a PaaS (Heroku) without Containers/6. 10.6 - Wrap Up.vtt	2.1 KB
	10. Deploying to a PaaS (Heroku) without Containers/6.1 Section10.6_Notes.pdf.pdf	63.9 KB
	11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.mp4	31.5 MB
	11. Running Apps with Containers (Docker)/1. 11.1 Introduction to Containers and Docker.vtt	4.4 KB
	11. Running Apps with Containers (Docker)/1.1 Section11.1_Notes.pdf.pdf	70.2 KB
	11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.mp4	26.7 MB
	11. Running Apps with Containers (Docker)/2. 11.2 Installing Docker.vtt	2.4 KB
	11. Running Apps with Containers (Docker)/2.1 Section11.2_Notes.pdf.pdf	77.8 KB
	11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.mp4	21.6 MB
	11. Running Apps with Containers (Docker)/3. 11.3 Creating Our API App Dockerfile.vtt	2.9 KB
	11. Running Apps with Containers (Docker)/3.1 Section11.3_Notes.pdf.pdf	58.5 KB
	11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.mp4	26.7 MB
	11. Running Apps with Containers (Docker)/4. 11.4 Building and Running the Docker Container.vtt	3.6 KB
	11. Running Apps with Containers (Docker)/4.1 Section11.4_Notes.pdf.pdf	94.1 KB
	11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.mp4	46.9 MB
	11. Running Apps with Containers (Docker)/5. 11.5 Releasing to Heroku with Docker.vtt	5.2 KB
	11. Running Apps with Containers (Docker)/5.1 Section11.5_Notes.pdf.pdf	56.6 KB
	11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.mp4	7.7 MB
	11. Running Apps with Containers (Docker)/6. 11.6 - Wrap Up.vtt	1.6 KB
	11. Running Apps with Containers (Docker)/6.1 Section11.6_Notes.pdf.pdf	82.4 KB
	12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.mp4	18.7 MB
	12. Deploying to IaaS (AWS ECS)/1. 12.1 - Introduction to AWS.vtt	3.2 KB
	12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).mp4	49.8 MB
	12. Deploying to IaaS (AWS ECS)/10. 12.9 - Uploading Images to the Elastic Container Registry (ECR).vtt	4.2 KB
	12. Deploying to IaaS (AWS ECS)/10.1 Section12.9_Notes.pdf.pdf	72.4 KB
	12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.mp4	38.1 MB
	12. Deploying to IaaS (AWS ECS)/11. 12.10 - Creating the ECS Cluster with Fargate Launch Method.vtt	3.8 KB
	12. Deploying to IaaS (AWS ECS)/11.1 Section12.10_Notes.pdf.pdf	56.9 KB
	12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.mp4	59.9 MB
	12. Deploying to IaaS (AWS ECS)/12. 12.11 - Creating the ECS Cluster with the EC2 Launch Method.vtt	6.4 KB
	12. Deploying to IaaS (AWS ECS)/12.1 Section12.11_Notes.pdf.pdf	57.5 KB
	12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.mp4	31 MB
	12. Deploying to IaaS (AWS ECS)/13. 12.12 - Updating the Cluster Containers.vtt	3.5 KB
	12. Deploying to IaaS (AWS ECS)/13.1 Section12.12_Notes.pdf.pdf	67.5 KB
	12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.mp4	7 MB
	12. Deploying to IaaS (AWS ECS)/14. 12.13 - Tearing down the ECS Cluster.vtt	740 B
	12. Deploying to IaaS (AWS ECS)/14.1 Section12.13_Notes.pdf.pdf	60.1 KB
	12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.mp4	23.5 MB
	12. Deploying to IaaS (AWS ECS)/15. 12.14 - Deploying to ECS via the CI pipeline.vtt	2.6 KB
	12. Deploying to IaaS (AWS ECS)/15.1 Section12.14_Notes.pdf.pdf	64.2 KB
	12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.mp4	9 MB
	12. Deploying to IaaS (AWS ECS)/16. 12.15 - Wrap Up.vtt	1.9 KB
	12. Deploying to IaaS (AWS ECS)/16.1 Section12.15_Notes.pdf.pdf	56.6 KB
	12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.mp4	25.5 MB
	12. Deploying to IaaS (AWS ECS)/2. 12.2 - AWS Costs and Caution.vtt	2.4 KB
	12. Deploying to IaaS (AWS ECS)/2.1 Section12.2_Notes.pdf.pdf	74.7 KB
	12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.mp4	22.8 MB
	12. Deploying to IaaS (AWS ECS)/3. 12.3a - Intro to AWS ECS.vtt	4.5 KB
	12. Deploying to IaaS (AWS ECS)/3.1 Section12.3_Notes.pdf.pdf	55.1 KB
	12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.mp4	23.8 MB
	12. Deploying to IaaS (AWS ECS)/4. 12.3b - Container Orchestration Options Kubernetes, ECS, Docker Swarm.vtt	3.6 KB
	12. Deploying to IaaS (AWS ECS)/4.1 Section12.3_Notes.pdf.pdf	55.1 KB
	12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.mp4	4.9 MB
	12. Deploying to IaaS (AWS ECS)/5. 12.4 - Create an AWS Account.vtt	603 B
	12. Deploying to IaaS (AWS ECS)/5.1 Section12.4_Notes.pdf.pdf	53.5 KB
	12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.mp4	23.2 MB
	12. Deploying to IaaS (AWS ECS)/6. 12.5 - Setting Permissions with IAM.vtt	3 KB
	12. Deploying to IaaS (AWS ECS)/6.1 Section12.5_Notes.pdf.pdf	57.9 KB
	12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.mp4	28.9 MB
	12. Deploying to IaaS (AWS ECS)/7. 12.6 - Installing the AWS CLI.vtt	2.3 KB
	12. Deploying to IaaS (AWS ECS)/7.1 Section12.6_Notes.pdf.pdf	56.9 KB
	12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.mp4	20.8 MB
	12. Deploying to IaaS (AWS ECS)/8. 12.7 - Configuring the AWS CLI.vtt	2.8 KB
	12. Deploying to IaaS (AWS ECS)/8.1 Section12.7_Notes.pdf.pdf	83 KB
	12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).mp4	9.4 MB
	12. Deploying to IaaS (AWS ECS)/9. 12.8 - Intro the Elastic Container Registry (ECR).vtt	1.2 KB
	12. Deploying to IaaS (AWS ECS)/9.1 Section12.8_Notes.pdf.pdf	55.3 KB
	13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.mp4	15.3 MB
	13. A Deep Learning Model with Big Data/1. Challenges of using Big Data in Machine Learning.vtt	2.4 KB
	13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.mp4	20.8 MB
	13. A Deep Learning Model with Big Data/10. 13.10 - Additional Considerations and Wrap Up.vtt	3.1 KB
	13. A Deep Learning Model with Big Data/10.1 Section13.10_Notes.pdf.pdf	60 KB
	13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.mp4	16.7 MB
	13. A Deep Learning Model with Big Data/2. Introduction to a Large Dataset - Plant Seedlings Images.vtt	1.9 KB
	13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.mp4	88.8 MB
	13. A Deep Learning Model with Big Data/3. Building a CNN in the Research Environment.vtt	10.3 KB
	13. A Deep Learning Model with Big Data/3.1 CNN_Analysis_and Model.zip.zip	1.5 MB
	13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.mp4	79.5 MB
	13. A Deep Learning Model with Big Data/4. Production Code for a CNN Learning Pipeline.vtt	9.1 KB
	13. A Deep Learning Model with Big Data/4.1 CNNProdCode.zip.zip	4.7 KB
	13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.mp4	17 MB
	13. A Deep Learning Model with Big Data/5. Reproducibility in Neural Networks.vtt	3.7 KB
	13. A Deep Learning Model with Big Data/6. Setting the Seed for Keras.html	3.6 KB

RELATED DOWNLOADS

Torrent Name

Added

Size

Seeds

Peers

Uploader

Health