12. Scalability & Systems
Scalability in machine learning emphasizes the importance of designing systems that can handle increasing complexity and data sizes effectively. The chapter discusses various architectural strategies, including distributed computing, parallel processing, and efficient data storage, as well as online learning and system deployment techniques. Key challenges such as memory limitations and communication overhead are addressed, showing how modern systems can adapt to the growing demands of machine learning applications.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Scalability is crucial for handling large datasets and complex models.
- Different scaling methodologies exist, such as horizontal and vertical scaling.
- Advanced frameworks like MapReduce and Apache Spark can efficiently process large data.
- Distributed training methods, such as data and model parallelism, allow efficient model training across multiple nodes.
- Effective deployment strategies include model serving architectures, load balancing, and A/B testing to ensure scalable ML systems.
Key Concepts
- -- Scalability
- The ability of a system to handle increased workload by adding resources.
- -- MapReduce
- A programming model for processing large datasets with a distributed algorithm.
- -- Data Parallelism
- A method where data is split across multiple nodes, allowing simultaneous processing of mini-batches.
- -- Federated Learning
- A training approach where model training occurs on devices while keeping data decentralized.
- -- Model Serving
- Methods for deploying machine learning models to provide predictions in production environments.
Additional Learning Materials
Supplementary resources to enhance your learning experience.