- A Python installation
- A Jupyter notebook installation
- Python coding skills including pandas and scikit-learn
- Familiarity with Machine Learning algorithms
- Familiarity with git
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
Created by Soledad Galli, Christopher Samiullah
Last updated 2/2019
Size: 3.64 GB