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Improving Machine Learning
 Workflows: Training, Packaging and Serving.


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As machine learning practitioners, we know how hard it can be to have a smooth process around training and serving production-ready models. Processing the data, saving all the relevant artefacts to make experiments reproducible, packaging and serving the models; all these individual components can be a nightmare to implement and manage. MLflow - a new platform for managing the ML life cycle.

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Improving Machine Learning
 Workflows: Training, Packaging and Serving.

  1. 1. Improving Machine Learning
 Workflows Training, Packaging and Serving.
  2. 2. We build speech and NLP models 
 to help call centre agents to better assist customers in real time.
  3. 3. Struggles
  4. 4. Manual Work Struggles Copying data from server to server Training & Experimentation Keeping track of experiments Keeping track of artefacts Reproducibility Packaging & versioning models Manual deployments No CI/CD Longer release cycles Slow model / code integration No transparency Serving Versioning Model encapsulation
  5. 5. - Machine Learning Lifecycle. - Building Blocks: 
 mlflow & TensorFlow Serving. - Infrastructure: 
 Putting all components together. - CI/CD for ML models. Agenda
  6. 6. Building blocks: 
 mlflow & TensorFlow Serving
  7. 7. Tracking models with mlflow
  8. 8. Tracking models with mlflow
  9. 9. Tracking models with mlflow
  10. 10. Tracking models with mlflow
  11. 11. Tracking models with mlflow
  12. 12. Serving models with TensorFlow
  13. 13. Serving models with TensorFlow
  14. 14. Infrastructure Before After
  15. 15. CI/CD for ML models
  16. 16. Tracking models with mlflow
  17. 17. Aigent’s ML lifecycle
  18. 18. Solutions Manual Work Training & Experimentation No CI/CD Serving
  19. 19. Wilder Rodrigues @wilderrodrigues - Software & Artificial Intelligence Engineer - Apache™ Committer & PMC member - Keras contributor - School of AI Utrecht Dean - Amsterdam AI Ambassador - Public Speaker