Certification Study Group - Professional ML Engineer Session 3 (Machine Learning in the Enterprise)
- 1. Session 3 Dr. Ryan Rad Professional Machine Learning Engineer
- 2. Where are we on our journey 1 Session 3 Content Review 2 Extra Information 4 Next Steps 5 Sample Question Review 3 6 Q&A
- 3. Where are we on our journey
- 4. Professional Machine Learning Certification Learning Journey Organized by Google Developer Groups Surrey co hosting with GDG Seattle Session 1 Feb 24, 2024 Virtual Session 2 Mar 2, 2024 Virtual Session 3 Mar 9, 2024 Virtual Session 4 Mar 16, 2024 Virtual Session 5 Mar 23, 2024 Virtual Session 6 Apr 6, 2024 Virtual Review the Professional ML Engineer Exam Guide Review the Professional ML Engineer Sample Questions Go through: Google Cloud Platform Big Data and Machine Learning Fundamentals Hands On Lab Practice: Perform Foundational Data, ML, and AI Tasks in Google Cloud (Skill Badge) - 7hrs Build and Deploy ML Solutions on Vertex AI (Skill Badge) - 8hrs Self study (and potential exam) Lightning talk + Kick-off & Machine Learning Basics + Q&A Lightning talk + GCP- Tensorflow & Feature Engineering + Q&A Lightning talk + Enterprise Machine Learning + Q&A Lightning talk + Production Machine Learning with Google Cloud + Q&A Lightning talk + NLP & Recommendation Systems on GCP + Q&A Lightning talk + MOPs & ML Pipelines on GCP + Q&A Complete course: Introduction to AI and Machine Learning on Google Cloud Launching into Machine Learning Complete course: TensorFlow on Google Cloud Feature Engineering Complete course: Machine Learning in the Enterprise Hands On Lab Practice: Production Machine Learning Systems Computer Vision Fundamentals with Google Cloud Complete course: Natural Language Processing on Google Cloud Recommendation Systems on GCP Complete course: ML Ops - Getting Started ML Pipelines on Google Cloud Check Readiness: Professional ML Engineer Sample Questions
- 5. Session 3 Content Review
- 6. Session 3 Study Group Model Development - Build a model. - Train a model. - Test a model. - Scale model training and serving. - Model Explainability
- 7. ML Processes
- 8. ML Processes
- 10. Model Complexity vs Error
- 11. Model Complexity vs Error Optimal Region
- 12. Model Complexity vs Error Overfitting Underfitting
- 13. Overfitting How to fix/prevent overfitting: ● Add regularization ○ L1/L2 Reg ○ Dropout ● Reduce model complexity
- 14. Underfitting How to fix/prevent underfitting: ● Lower regularization coefficients ● Increase model complexity ● Feature Engineering
- 15. Data Catalog
- 18. Distributed Training Approaches Async Parameter Server vs. Sync AllReduce
- 19. PS0 PS1 Worker 0 Worker 1 Worker 2 Worker 3 Async parameter server
- 20. PS0 PS1 Worker 0 Worker 1 Worker 2 Worker 3 x x x Async parameter server
- 21. PS0 PS1 Worker 0 Worker 1 Worker 2 Worker 3 Async parameter server
- 22. Worker 0 Worker 3 Worker 2 Worker 1 p0, p1.. p0, p1.. p0, p1.. p0, p1.. Sync allreduce architecture
- 23. Consider async parameter server if... Consider sync allreduce if... Many low-power or unreliable workers. Multiple devices on one host. Fast devices with strong links (e.g. TPUs). Better for multiple GPUs. Constrained by compute power. More mature approach. Constrained by I/O. There isn’t one right answer, but here are some considerations
- 25. Gradient-based Attribution attribution for feature Create attribution using gradient of the output wrt each base input feature ● same as feature weights for linear models ● 1st order approximation for non-linear models ● use (normalized) attribution as mask/window over image
- 29. Model complexity often refers to the number of features or terms included in a given predictive model. What happens when the complexity of the model increases? A. Model performance on a test set is going to be poor. B. All of the options are correct. C. Model is more likely to overfit. D. Model will not figure out general relationships in the data.
- 30. Model complexity often refers to the number of features or terms included in a given predictive model. What happens when the complexity of the model increases? A. Model performance on a test set is going to be poor. B. All of the options are correct. C. Model is more likely to overfit. D. Model will not figure out general relationships in the data.
- 31. You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do? A. Train the model for a few iterations, and check for NaN values. B. Train the model for a few iterations, and verify that the loss is constant. C. Train a simple linear model, and determine if the DNN model outperforms it. D. Train the model with no regularization, and verify that the loss function is close to zero.
- 32. You need to write a generic test to verify whether Dense Neural Network (DNN) models automatically released by your team have a sufficient number of parameters to learn the task for which they were built. What should you do? A. Train the model for a few iterations, and check for NaN values. B. Train the model for a few iterations, and verify that the loss is constant. C. Train a simple linear model, and determine if the DNN model outperforms it. D. Train the model with no regularization, and verify that the loss function is close to zero.
- 33. You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier? A. Use K-fold cross validation to understand how the model performs on different test datasets. B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image. C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features. D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.
- 34. You work for a textile manufacturer and have been asked to build a model to detect and classify fabric defects. You trained a machine learning model with high recall based on high resolution images taken at the end of the production line. You want quality control inspectors to gain trust in your model. Which technique should you use to understand the rationale of your classifier? A. Use K-fold cross validation to understand how the model performs on different test datasets. B. Use the Integrated Gradients method to efficiently compute feature attributions for each predicted image. C. Use PCA (Principal Component Analysis) to reduce the original feature set to a smaller set of easily understood features. D. Use k-means clustering to group similar images together, and calculate the Davies-Bouldin index to evaluate the separation between clusters.
- 35. Q&A
- 36. Preview actions for next week
- 37. By our next meeting 1. Complete a. Production Machine Learning Systems b. Computer Vision Fundamentals with Google Cloud
- 38. Link to badge Redeem your participation badge Thank you for joining the event
- 39. Thank you for tuning in! For any operational questions about access to Cloud Skills Boost or the Road to Google Developers Certification program contact: gdg- support@google.com