Created by Soledad Galli, Christopher Samiullah | Video: 1280×720 | Audio: AAC 48KHz 2ch | Duration: 08:11 H/M | Lec: 125 | 3.60 GB | Language: English | Sub: English [Auto-generated]

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 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.

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

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

A Python installation
A Jupyter notebook installation
Python coding skills including pandas and scikit-learn
Familiarity with Python environments, OOP and git
Familiarity with Machine Learning algorithms


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