README.md

Advanced Topics in Software Systems (SYS4BIGML)

Fall 2020 - Schedule and Content

• Responsible teacher: Hong-Linh Truong
• Other teachers/Assistants: Phuong Pham and Tri Nguyen

Lectures

• Lecture 1:
  • Slides: Robustness, Reliability, Resilience and Elasticity (R3E) for Big Data/Machine Learning Systems
  • Key reading 1: R3E -An Approach to Robustness, Reliability, Resilience and Elasticity Engineering for End-to-End Machine Learning Systems
  • Key reading 2: The New Frontier of Machine Learning Systems
• Lecture 2
  • Slides: Benchmarking, Monitoring, Validation and Experimenting for Big Data and Machine Learning Systems
  • Key reading 1: Benchmarking big data systems: A survey
  • Key reading 2: MLPERF Training Benchmark
  • Key reading 3: Data Validation for Machine Learning
  • Key reading 4: Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle and ModelDB: a system for machine learning model management
  • Key reading 5: Putting Machine Learning into Production Systems
  • Site 1: AI Matrix
• Lecture 3
  • Slides: Coordination Models and Techniques for Big Data and Machine Learning Systems
  • Key reading 1: Cirrus: a Serverless Framework for End-to-end ML Workflows
  • Key reading 2: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
  • Key reading 3: Orchestrating Big Data Analysis Workflows in the Cloud: Research Challenges, Survey, and Future Directions
  • Key reading 4: KeystoneML: Optimizing Pipelines for Large-ScaleAdvanced Analytics
  • Key reading 5: Jeff Smith. 2018. Machine Learning Systems: Designs that scale (1st. ed.). Manning Publications Co., USA.
  • Key reading 6: Prediction-Serving Systems
• Lecture 4
  • Slides: Machine Learning with Edge Systems
  • Key reading 1: Serving deep neural networks at the cloud edge for vision applications on mobile platforms
  • Key reading 2:From the Edge to the Cloud: Model Serving in ML.NET
  • Key reading 3: Machine Learning at Facebook:Understanding Inference at the Edge
  • Key reading 4: Distributing Deep Neural Networks with Containerized Partitions at the Edge

If you need the sources of slides for your teaching, pls. contact Linh Truong

Hands-on tutorials

• Machine Learning experiment management
• Observability and Monitoring
• Machine Learning Serving
• Edge ML Pipeline
• Qualty of Analytics for ML
• Common tasks with Edge ML

Project ideas presentations

• Students will propose the project idea. This is an important aspect of research-oriented course. If a student cannot propose an idea, the teacher will suggest some concrete ideas for students.

Final project demonstration

• The final project demonstration should be organized like an "event" where all students can demonstrate their work and students can discuss experiences in their projects.
• Demos:
  • Chaos Engineering Data Validation
  • Elastic Image Search

Guides

• How to write study/learning logs

Reading list

• Interesting and relevant papers and sites