Implementing Machine Learning in the Real World
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This document outlines 15 lessons learned from building large-scale machine learning systems in the real world. Some key challenges discussed include data scientists not being well-suited for engineering work, traditional development methodologies not working for machine learning, the difficulty of data labeling and feature extraction, and the complexities of training, executing, operationalizing, and securing machine learning models at scale. The document provides ideas to address these challenges such as establishing separate data science and engineering teams, implementing automated data labeling strategies, leveraging centralized feature stores, and adopting techniques like transfer learning and continual learning.
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Implementing Machine Learning in the Real World
- 1. Some Things I Wish I Had Known Before Scaling Machine Learning Solutions Invector Labs
- 3. Agenda • Myths and realities of machine learning solutions in the real world • 15 Lessons I learned when building large scale machine learning systems • Challenge • What we learned? • Solution
- 5. We can discuss the theoretical definitions or, instead, focus on the pragmatic one…
- 8. But the reality remains that building machine learning solutions remains brutally difficult
- 9. But not just because of the obvious reasons…
- 10. Challenges of Machine Learning in the Real World High Technological Barrier Limited Talent Availability Labeled Datasets Cost …
- 12. We are dealing with a new app lifecycle… Traditional App Lifecycle Machine Learning App Lifecycle Experimentation Model Creation Training Testing Regularization Deployment Monitoring Optimization Design Implementation Deployment Management/ Monitoring
- 14. The Aspects of a Machine Learning Solution that will Drive You Crazy Strategy & Processes Data Engineering Experimentation Model Training Model Operationalization Runtime Execution Security Lifecycle Management Optimization …
- 15. Lessons learned when building high scale machine learning solutions…
- 18. Challenges Data scientists are great at experimentation Not so much at writing high quality code Experimentation deep learning frameworks don’t necessarily make great production frameworks, ex: PyTorch vs. TensorFlow
- 19. Some Ideas to Consider •Write notebooks and experimentation models Data Science Team •Refactor or rewrite models for production environments •Automate training and optimization jobs Engineering Team •Deploy models •Monitor, retrain, and optimize models DevOps Teams • Divide data science and data engineering teams
- 20. Lesson #2 Neither Agile nor Waterfall Methodologies Work in Machine Learning
- 21. Challenges Waterfall methods don’t work because you rarely know what machine learning methods are going to work for a specific problem Agile methods don’t work because you need very specific requirements
- 22. Some Ideas to Consider Agile Waterfall Agile • Split the development lifecycle into agile and waterfall iterations
- 24. Lesson # 3 : Feature extraction can become a reusability nightmare…
- 25. Challenges Different models require the same features from a dataset Feature extraction jobs are computationally expensive Different teams create proprietary ways to capture and store feature information
- 26. Some Ideas to Consider Dataset Preparation Job1 Dataset Preparation Job2 Dataset Preparation JobN Representation Learning Task1 Representation Learning Task1 Representation Learning Task1 Feature Store Model 1 Model N Implement a centralized feature store Leverage representation learning to extract relevant features from a dataset Look for reference architectures: ex: Uber’s Michelangelo
- 27. Lesson #4 : Data labeling is so easy to underestimate
- 28. Challenges Data experts spend a lot of time labeling datasets The logic for data labeling is often not reusable Subjective data labeling strategy fail to differentiate between useful and useless features
- 29. Some Ideas to Consider Implement an automated data labeling strategy Generative learning can help to structure more effective labels Project Snorkel is one of the leading automated data labeling frameworks in the market
- 31. Lesson #5: The single machine learning framework fallacy
- 32. Challenges Enterprises like to standardize on a single machine learning framework Different teams have different technology preferences Providing a consistent machine learning platform across different machine learning frameworks is no easy task
- 33. Some Ideas to Consider Experimentation Framework Intermediate Representation Production Framework Optimize for productivity, not consistency Enable enough flexibility to leverage different frameworks for experimentation and production ONNX is a great solution for intermediate representations
- 34. Lesson #6: Too much time going from notebooks to production programs
- 35. Challenges Notebooks are ideal for model experimentation and testing Notebooks typically have performance challenges when executed at scale Scaling Notebook environments can be challenging Parametrizing Notebook executions is far from trivial
- 36. Some Ideas To Consider • Jupyter, Zeppelin Model Experimentation • Papermill • Netflix’s Meson Scheduling Notebooks • Docker Containers • Kubernetes Running Complex Workflows Enable an infrastructure to operationalize data science notebooks Use containers for the most complex machine learning workflows
- 37. Lesson #7: Model selection can be a machine learning problem
- 38. Challenges Data scientists make very subjective decisions when comes to model selection The same problem can be solved using different machine learning models Very often is almost impossible to differentiate between similar models
- 39. Some Ideas To Consider Represent machine learning requirements as a dataset with an objective attribute Leverage AutoML-based techniques for model selection Problem Dataset AutoML Proposed Models
- 42. Challenges The No Free Lunch Theorem Trained models can perform poorly against new datasets New engineers and DevOps need to understand how to re-train existing models
- 43. Some Ideas to Consider DataLake Data Outcomes/Feature Store Training Job1 Training Job2 Training JobN Automate Training Jobs Orchestrate scheduled execution of training jobs
- 45. Challenges Training machine learning models can be computationally expensive Most machine learning models need to be retrained entirely based on the arrival of new data Its nearly impossible to quantify the impact that new datasets have in the performance of a model
- 46. Some Ideas to Consider Implement continual learning models Consider transfer learning as a fundamental enabler
- 47. Lesson #10: Training a model requires as much coding as creating it…
- 48. Challenges Data engineers spend a lot of time writing training routines for machine learning models Comparing the performance of different models on the same datasets remains tricky Changes on a training dataset often imply changes on the training code
- 49. Some Ideas to Consider Explore a configuration- driven training process Uber’s Ludwig is an innovative, no-code framework for training machine learning models
- 50. Executing Machine Learning Models…
- 51. Lesson #11: Different models require different execution patterns…
- 52. Challenges Not all models can be executed via APIs Some models take a long time to run In some scenarios, different models need to be executed at the same time based on a specific condition
- 53. Some Ideas to Consider Scheduled Activation Model Model Pub-Sub Activation Model Model On-Demand Activation Model Model Model API Gateway Event Gateway Enable different execution modes based on client’s requirements
- 54. Lesson #12: Mobile deep learning is more complicated than you think
- 55. Challenges Centralized cloud deep learning models don’t scale On-device deep learning models are hard to distribute and train Tons of privacy challenges
- 56. Some Ideas to Consider Consider using federated learning or similar patterns for mobile based machine learning
- 59. Challenges The accuracy-interpretability friction The unpredictability factor Limited toolset
- 60. Some Ideas to Consider •Use tools like TensorBoard to visualize the structure of neural networks Visualize the Network and its Results •High training error is a sign of underfitting •High test error and low training error is a sign of overfitting Compare Training and Test Errors •Helps to determine whether the error is in the code or in the data Test with Small Datasets •Monitor the number of activations in hidden units Monitor Activations and Gradient Values Understanding How Nodes are Activated Understanding what Hidden Layers Do Understanding How Concepts are Formed Interpretability Establish systematic practices to debug machine learning models Onboard modeling visualization and interpretability tools
- 61. Security…
- 62. Lesson #14: Machine learning models are so easy to hack
- 63. Challenges Most neural networks are vulnerable to adversarial attacks Attackers don’t need access to the models but can simply manipulate input datasets Most of the times adversarial attacks go undetected
- 64. Some Ideas to Consider Test your neural networks for adversarial robustness IBM’s adversarial robustness toolbox is one of the leading stacks in neural network security
- 65. Lesson # 15: Data privacy is the elephant in the machine learning room
- 66. Challenges Machine learning models intrinsically build knowledge about private datasets Most machine learning techniques require clear access to data which, in many cases, contains sensitive information There are no established techniques to evaluating the privacy robustness of machine learning models
- 67. Some Ideas to Consider Private machine learning is an emerging area of research Leverage techniques such as secured multi-party computations or zero- knowledge-proofs to obfuscate training datasets PySyft is an emerging framework to enable privacy in machine learning models
- 68. Some not-well-known, reference architectures that might help…
- 69. DAWN Project from Stanford University Michelangelo from Uber MLFlow from DataBricks FBLearner from Facebook TFX from Google
- 70. The challenges go beyond the obvious…
- 71. Three Foundational Challenges for the Mainstream Adoption of Machine Learning Lowering the Technological Entry Point • Can mainstream developers embrace machine learning stacks? Talent Availability • Can companies and governments nurture local data science talent? Data Democratization • Can rich datasets stop being a privilege of large corporations and governments ?
- 72. Some Initiatives to Consider Lowering the Technological Entry Point • AutoML, low-code machine learning frameworks Talent Availability • Google AI Academy, Coursera, Udacity… Data Democratization • Decentralized AI platforms
- 73. Summary • Implementing machine learning solutions in the real world remains incredibly challenging • There is a large gap between the advancements in AI research and the practical viability of those techniques • Machine learning applications require a new lifecycle different from traditional software models • Each aspect of that lifecycle brings a unique set of challenges • Start small, iterate…