ML System Design, a systems approach to MLOps
Are you a machine learning practitioner, who struggles with designing, reasoning and communicating about larger ML systems?
Then this training is for you!
With the industry moving towards end-to-end ML teams to enable them to implement MLOps practices, it is paramount for you to understand ML from a systems perspective. In this course, you will gain a thorough understanding of the technical intricacies of designing valuable, reliable and scalable ML systems. The session enables you to identify trade-offs and bottlenecks in a system. Plus, you’ll learn how to effectively communicate and collaborate with other people and departments.
The training will contain a mix of theory and practice. As we will share common methodologies and frameworks and apply them straight away to a real business case.
Get ready to develop your skills and knowledge in this exciting field! Be sure to bring your own business case to get the most out of the training.
This training is for you if…
You want to improve your understanding of what makes a scalable machine learning application
You want to make more conscious decisions when working on machine learning applications
You want to be better at identifying trade-offs when making design decisions for your machine learning application
You want to improve communicating your design decisions
This training is not for you if…
You only want to write code. In that case, the MLOps training provides hands-on coding for the implementation of ML infra.
You don’t have experience with running a machine learning model in production. In that case, the Production Ready Machine Learning is maybe more for you.
You think building an ML model is your sole responsibility and do not care to communicate your design, not even to your stakeholders.
Clients we've helped
What you'll learn
Advanced ML System Design
- Techniques for designing valuable, reliable, and scalable ML systems
- Strategies for identifying and mitigating trade-offs and bottlenecks in ML systems
- Best practices to effectively communicate and collaborate during system designs
- Hands-on experience in designing a machine learning system by applying the learned theory to a real business use case
- Introduction to system design and why it’s important
- ML System Design as part of the ML model life cycle
- Requirements engineering
- Walkthrough Step-by-step ML System Design framework
- Design real world ML use case
- Design docs
- C4 modelling
- Design your own case
All are directly applied to a business use case.
After the training you will be able to:
Assess the requirements for ML systems by applying the Reliable Scalable Maintainable en Adaptable framework.
Evaluate technical bottlenecks and trade-offs in design proposals by using the ML System Design Canvas.
Communicate designs clearly and make the proposed solution measurably work by writing proposals following the Software Design Doc template.
Illustrate your designs and provide context by creating hierarchical architecture diagrams.
Machine Learning Engineering Learning Journey
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We believe that our courses empower people to be more effective with data and tech so they can better help colleagues and delight customers. Attending one of our training is also a great way to expand your network, increase your employability, and to command bigger salaries. If you already have a well-paying job, our prices are really affordable. Not everyone is so lucky though. So if you wish to attend one of our most popular courses, but require financial support, we'd love to hear from you.
Roy van SantenMachine Learning Engineer
Roy is a Machine Learning Engineer at Xebia Data. He holds a MSc degree in Chemical Engineering and decided to fully switch to software engineering in 2016.
He is a pragmatic engineer interested in the latest technologies that will always choose the tool that best suits the job. He has a passion for building high-quality and highly maintainable software systems. To achieve this he puts great effort in CI/CD, layered testing, monitoring/alerting and special attention to documentation. He takes an agile and incremental approach to software. He goes by the motto: “if your model is not running in production, it does not exist”. It is better to put a model in production that delivers some value, than having none at all.
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.