Machine Learning System Design
5 February, 2024 – Virtual
Are you a machine learning practitioner who struggles with designing, reasoning, and communicating about larger ML systems? Then this training is for you! 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.
09:00 – 17:00
What will you learn?
With the industry moving toward end-to-end Machine Learning teams to enable them to implement MLOPs practices, it is paramount for you to understand ML from a systems perspective.
After training, you will be able to:
Assess the requirements for ML systems by applying the ‘Reliable, Scalable, Maintainable, and 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.
- Design Docs
- C4 Modeling
- Requirements Engineering
- Reliable, Scalable, Maintainable, and Adaptable (RSMA) framework
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. You will also learn to communicate and collaborate effectively with others and departments.
The training will contain a mix of theory and practice; we will share standard methodologies and frameworks and apply them immediately to a real business 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
Who is it for?
This training is perfect for anyone wishing to improve their understanding of what is essential for a scalable machine learning application and the ability to identify trade-offs when making ML design decisions. Want to make more conscious ML decisions when working on NL applications and improve the communication of these decisions? Then this course is for you!
For this course, you will need experience running machine learning models in production.
No experience yet? Then, the Production Ready Machine Learning course might be a better fit for you.
If you only want to learn how to write code for ML, check out the MLOps training, which provides hands-on coding for implementing ML infrastructure.
Why should I
follow this training?
Learn from the best
This training offers the unique opportunity to learn from industry experts who will provide in-depth conceptual explanations and practical exercises. Their expertise ensures you gain valuable insights and real-world knowledge about designing ML systems.
Comprehensive Learning Experience
Over the course of two days, you will delve into the essential aspects of ML system design, including critical considerations, standard problem-solving solutions, best practices, design patterns, and the avoidance of common pitfalls (anti-patterns). This comprehensive curriculum equips you with a well-rounded understanding of ML system design.
Interactive and Thought-Provoking
The training’s interactive setting fosters active engagement, encouraging participants to think critically about the complexities of ML system design. It provides a platform for hands-on learning and reflective exercises, empowering you to make informed decisions and enhance your ability to create effective and robust machine learning solutions.
should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements and share literature if you need to prepare.
See you soon!
All literature and course materials are included in the price.
After registering for this course, you will receive a confirmation email with practical information.
What does it look like?
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