January 14, 2025 6:15 PM PST
This meeting focused on the fundamentals of Machine Learning Engineering (MLE), discussing the evolving role of MLEs, the transition into ML projects, and the impact of large models on smaller ones. The session also highlighted the importance of understanding the full lifecycle of ML development and provided insights into tools and resources for learning and automation in AI/ML.
Presenter: Coach Denny
Key Topics Discussed
Trends in the MLE Role
- The MLE role is evolving to encompass various aspects of ML projects, similar to software engineering.
- There will be significant overlap between MLE and Software Development Engineering (SDE) roles in the long run.
Transitioning into ML Projects
- Knowledge: Understanding the fundamentals of machine learning.
- Hands-on Experience: Gaining practical experience through projects.
- Finding Opportunities: Seeking roles or projects that allow for involvement in ML.
Large Models vs. Small Models
- Small models will continue to exist alongside large models.
- In specific scenarios, large models may replace small models, especially in:
- Medical image recognition: Better at explaining results.
- Natural language processing: Content generation and industry-specific applications with abundant data.
Overcoming Limitations of Large Models
- Strategies include:
- Overlaying a large model with a smaller model.
- Training a new model to address specific needs.
Full Lifecycle of ML Development
- Most ML-related jobs require understanding how to convert business problems into ML problems.
- Integration skills are essential for successful implementation.
Trends in Tool Usage
- The trend is shifting from building tools to utilizing existing tools.
- Recommended resources for learning AI/ML include:
- AWS Jump Starter
- Paper with Code
- Hugging Face
- Kaggle
Directions for MLE Work
- Model Development
- Pipeline Management
- Monitoring and MLOps
Automation and Integration
- Emphasis on AI/ML tools for automation and their integration with existing pipelines.
- Higher requirements may not be necessary to work with ML models if opportunities are available.
- Reading research papers is beneficial for staying updated in the field.
Conclusion
The session provided valuable insights into the current landscape of Machine Learning Engineering, emphasizing the importance of adaptability, continuous learning, and practical experience in the field.