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Testing and Monitoring Machine Learning Model Deployments
ML testing strategies, shadow deployments, production model monitoring and more
Created by Christopher Samiullah, offered on Udemy
To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 4294 students which left 326 reviews at an average rating of 4.07, which is average.
We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 8 hours 19 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.
This course currently has a bestcourses score of 5.6/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.
Learn how to test & monitor production machine learning models.
What is model testing?
You’ve taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren’t any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.
What is model monitoring?
You’ve deployed your model to production. OK now what? Is it working as you expect? How do you know? By monitoring models, we can check for unexpected changes in:
When we think about data science, we think about how to build machine learning models, 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 test & monitor these models in a production system is often neglected, . Only when we can effectively monitor our production models can we determine if they are performing as we expect.
Why take this course?
This is the first and only online course where you can learn how to test & monitor machine learning models. The course is comprehensive, and yet easy to follow. Throughout this course you will learn all the steps and techniques required to effectively test & monitor machine learning models professionally.
In this course, you will have at your fingertips the sequence of steps that you need to follow to test & monitor a machine learning model, plus a project template with full code, that you can adapt to your own models.
What is the course structure?
Part 1: Testing
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. We gradually build up the complexity, testing the model first in the Juyter notebook and then in a realistic production code base. Hands-on exercises are interspaced with relevant and actionable theory.
Part 2: Shadow Mode
We explain the theory & purpose of deploying a model in shadow mode to minimize your risk, and walk you through an example project setup.
Part 3: Monitoring
We take you through the theory & practical application of monitoring metrics & logs for ML systems.
This course does not cover model deployment (we have a separate course dedicated to that topic)
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 a tech lead & ML 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?
Data Scientists who want to know how to test & monitor their models beyond in production
Software engineers who want to learn about Machine Learning engineering
Machine Learning engineers who want to improve their testing & monitoring skills
Data Engineers looking to transition to ML engineering
Lovers of open source technologies
How advanced is this course?
This is an advanced level course, and it requires you to have experience with Python programming and git. How much experience? It depends on how much time you would like to set aside to go ahead and learn those concepts that are new to you. To give you an example, we will work with Python environments, we will work with object oriented programming, we will work with the command line to run our scripts, and we will checkout code at different stages with git. You don’t need to be an expert in all of these topics, but you need a reasonable working knowledge. We also work with Docker a lot, though we will provide a recap of this tool.
For those relatively new to software engineering, the course will be challenging. We have added detailed lecture notes and references, so we believe that those missing some of the prerequisites can take the course, but keep in mind that you will need to put in the hours to read up on unfamiliar concepts. On this point, the course slowly increases in complexity, so you can see how we pass, gradually, from the familiar Jupyter notebook, to the less familiar production code, using a project-based approach which we believe is optimal for learning. It is important that you follow the code, as we gradually build it up.
Still not sure if this is the right course for you?
Here are some rough guidelines:
Never written a line of code before: This course is unsuitable
Never written a line of Python before: This course is unsuitable
Never trained a machine learning model before: This course is unsuitable. Ideally, you have already built a few machine learning models, either at work, or for competitions or as a hobby.
Never used docker before: The second part of the course will be very challenging. You need to be ready to read up on lecture notes & references.
Have only ever operated in the research environment: This course will be challenging, but if you are ready to read up on some of the concepts we will show you, the course will offer you a great deal of value.
Have a little experience writing production code: There may be some unfamiliar tools which we will show you, but generally you should get a lot from the course.
Non-technical: You may get a lot from just the theory lectures, so that you get a feel for the challenges of ML testing & monitoring, as well as the lifecycle of ML models. The rest of the course will be a stretch.
To sum up:
With more than 70 lectures and 8 hours of video this comprehensive course covers every aspect of model testing & monitoring. 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.
We hope you enjoy it and we look forward to seeing you on board!
What you will learn
- Machine Learning System Unit Testing
- Machine Learning System Integration Testing
- Machine Learning System Differential Testing
- Shadow Deployments (also known as Dark/Decoy launches)
- Statistical Techniques for Assessing Shadow Deployments
- Monitoring ML System with Metrics (Prometheus & Grafana)
- Monitoring ML Systems with Logs (Kibana & the Elastic Stack)
- The Theory Around Continuous Delivery for Machine Learning
- Comfortable with Python
- Familiar with Scikit-Learn, Pandas, Numpy
- Comfortable with Data Science Fundamentals
- Can use Git version control
- Basic knowledge of Docker
- This is an advanced course