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.
“Deployment of Machine Learning Models” 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.
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.
Deployment of Machine Learning Models – Course Content
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.
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.
The course comprises 121 lectures organized into the following sections:
- Machine Learning Pipeline – Research Environment
- Machine Learning System Architecture
- Building a Reproducible Machine Learning Pipeline
- Course Setup and Key Tools
- Creating a Machine Learning Pipeline Application
- Serving the model via REST API
- Continuous Integration and Deployment Pipelines
- Differential Testing
- Deploying to a PaaS (Heroku) without Containers
- Running Apps with Containers (Docker)
- Deploying to IaaS (AWS ECS)
- A Deep Learning Model with Big Data
- Common Issues found during deployment
- Final Section
- A Python installation
- A Jupyter notebook installation
- Python coding skills including pandas and sci-kit-learn
- Familiarity with Machine Learning algorithms
- Familiarity with git
Summary of Course Main Features
- Soledad Galli – Lead Data Scientist
- Christopher Samiullah – Machine Learning Engineer
- Lectures: 145
- On-demand video: 9 hours
- Articles: 30
- Downloadable resources: 67
- 30-Day Money-Back Guarantee
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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