January 29, 2023 7:00 PM PST
This meeting discusses the architecture and development cycle of machine learning systems. It covers the importance of understanding both software engineering and machine learning engineering, the challenges faced in the field, and the future of machine learning and AI.
Presenter: Coach He, Machine Learning Director
Meeting Summary
Machine Learning Architecture and Development Cycle
- Purpose: To develop a comprehensive understanding of machine learning systems, including tools, workflows, hardware, and architecture.
- Key Topics:
- What is machine learning?
- Basic components and architecture of ML systems.
- Comparison of model development cycle vs. software engineering development cycle.
- Future trends in machine learning and AI.
- Learning new topics related to distributed computing and machine learning.
Common Issues
- Lack of overall knowledge of architecture.
- Difficulty in connecting theoretical knowledge to practical applications.
- Misconceptions about machine learning processes.
Key Challenges in Machine Learning
- Increasing data sizes and the need for systems to process large volumes of data.
- Complexity of distributed systems and the need for efficient architecture.
- Managing system architecture with software rather than manual processes.
- Issues with server management, data partitioning, and replication.
Machine Learning Applications
- Predicting behaviors and patterns, such as:
- Recidivism rates.
- Buying patterns and contract adherence.
- Viewing behaviors and recommendations.
Basic Steps for ML System Development
- Modeling:
- Collect and clean historical data.
- Develop and train models.
- Validate and evaluate models.
- Deployment:
- Deploy models to production.
- Monitor and update models and data.
Challenges from ML Systems
- Accessing diverse data sources.
- Vague requirements leading to assumptions and failures.
- Performance issues post-deployment.
- Aligning team values and goals across different departments.
Solutions to ML System Challenges
- Addressing technical debt in machine learning.
- Ensuring correctness and data access.
- Improving prediction speed and managing changes in underlying data.
Data Infrastructure
- Importance of data lakes and data warehouses.
- Questions to consider regarding data management and governance.
Development Lifecycle
- CI/CD processes for machine learning.
- Importance of testing ML models in production environments.
- Versioning and rollback strategies for models.
Future of Machine Learning
- Growth potential in the ML field.
- Importance of specialization and technical trends.
- Recommendations for resources and training in machine learning.
Recommended Resources
- Books and courses on machine learning, including:
- Full Stack Deep Learning
- Udacity: MLE - nanodegree
- Various YouTube channels and online courses for deeper understanding of concepts.