Build Machine Learning Model APIs
What you'll learn
- Deploy machine learning models into the cloud
- Build a 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
- This is an intermediate level course (see description)
Learn how to put your machine learning models into production.
What is model deployment?
Deployment of machine learning models, or merely, putting models into production, suggests making your models readily available to your other business systems. By deploying models, other systems can send out data to them and get their predictions, which remain in turn populated back into the company systems. Through machine learning model deployment, you and your business can start to make the most of the model you created.
When we believe about data science, we believe about how to create machine knowing models, we believe about which algorithm will be more predictive, how to engineer our features, and which variables to utilize to make the models more precise. Only when a model is totally incorporated with the business systems, we can draw out the genuine worth from its predictions.
Why take this course?
In this Deployment of Machine Learning Models course, you will learn every element of how to put your models in production. Throughout this course, you will learn all the procedures and facilities needed to deploy machine knowing models expertly.
In this Deployment of Machine Learning Models course, you will have at your fingertips, the series of procedures that you require to follow to deploy a machine learning model, plus a project template with complete code, that you can adjust to deploy your own models.
What is the course structure?
Part 1: The Research Environment
The Deployment of Machine Learning Models course starts from the most typical beginning point for most of data scientists: a Jupyter notebook with a machine learning model trained in it.
Part 2: Understanding Machine Learning Systems
A summary of essential architecture and style factors to consider for various kinds of machine learning models. This part sets the theoretical foundation for the useful part of the Deployment of Machine Learning Models course.
Part 3: From Research to Production Code
A hands-on project with total source code, which takes you through the procedure of transforming your notebooks into production prepared code.
Part 4: Deployment Tooling
Continuing with the hands-on project, this area takes you through the required tools for real production deployments, like CI/CD, screening, model cloud storage and more.
Part 5: Deployments
In this area, you will deploy models to both cloud platforms (Heroku) and cloud facilities (AWS).
Part 6: Bonus areas
In addition, there are devoted areas which go over managing huge data, deep knowing and typical concerns came across when deploying models to production.
This Deployment of Machine Learning Models course will assist you take the primary steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a totally deployed machine learning model, thinking about CI/CD, and deploying to cloud platforms and facilities.
There is a lot more to model deployment, like model tracking, advanced deployment orchestration with Kubernetes, and arranged workflows with Airflow, as well as numerous screening paradigms such as shadow deployments that are not covered in this Deployment of Machine Learning Models course.
Who are the trainers?
We have actually collected a great group to teach this Deployment of Machine Learning Models course. Sole is a leading data scientist in financing and insurance coverage, with 3+ years of experience in building and carrying out machine learning models in the field, and several IT awards and elections. Chris is an AI software application engineer with massive experience in building APIs and deploying machine learning models, permitting business to extract complete gain from their application and choices.
Who is this course for?
This Deployment of Machine Learning Models course appropriates for data scientists wanting to deploy their very first machine learning model, and software application developers aiming to shift into AI software application engineering. Deployment of machine learning models is an extremely advanced subject in the data science course so the course will likewise appropriate for intermediate and sophisticated data scientists.
How sophisticated is this course?
This is an intermediate level course, and it needs you to have experience with Python programming and git. It depends on how much time you would like to set aside to go ahead and learn those ideas that are brand-new to you.
We have actually included in-depth lecture notes and references, so we do think novices can take the Deployment of Machine Learning Models course, however keep in mind that you will require to put in the hours to check out up on unknown ideas. On this point, the course gradually increases in intricacy, so you can see how we pass, slowly, from the familiar Jupyter notebook, to the less familiar production code, utilizing a project-based technique which we think is optimum for learning.
Still uncertain if this is the best course for you?
Here are some rough standards:
Never ever composed a line of code prior to: This course disagrees
Never ever composed a line of Python prior to: This course disagrees
Never ever trained a machine learning model prior to: This course disagrees. Preferably, you have actually currently developed a couple of machine learning models, either at work, or for competitors or as a hobby.
Have actually only ever run in the research study environment: This course will be difficult, however if you are ready to research a few of the principles we will reveal you, the Deployment of Machine Learning Models course will use you a lot of value.
Have a little experience composing production code: There might be some unknown tools which we will reveal you, however typically you must get a lot from the course.
Non-technical: You might get a lot from simply the theoretical area (area 3) so that you get a feel for the possible architectures and difficulties of ML deployments. The remainder of the Deployment of Machine Learning Models course will be a stretch.
With more than 50 lectures and 8 hours of video (aff), this thorough course covers every element of model deployment. Throughout the Deployment of Machine Learning Models course, you will utilize Python as your primary language and other open source innovations that will permit you to host and make calls to your machine learning models.
We hope you enjoy it and we anticipate seeing you on board!
Who this course is for:
- Data scientists who wish to deploy their very first machine learning models
- Data scientists who wish to learn the finest practices around model deployment
- Software application developers who wish to shift into an expert system
- Intermediate and advanced data scientists who wish to level up their abilities
- Data engineers who develop data pipelines to productionise machine learning models
- Enthusiasts of coding and open source
Download The Deployment of Machine Learning Models
Created by Soledad Gallil, Christopher Samiullah
Last updated 9/2020
Size: 3.65 GB