The document discusses improving machine learning workflows by addressing struggles with manual processes, tracking experiments, packaging and versioning models, and model serving. It proposes using mlflow for tracking models and TensorFlow Serving for serving models. This will help establish continuous integration and delivery (CI/CD) of machine learning models to solve problems around reproducibility, deployment speed, and transparency.

1. Improving
Machine Learning
Workflows
Training, Packaging and Serving.

2. We build speech and NLP models
to help call centre agents
to better assist customers in real time.

3. Struggles

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. - Machine Learning Lifecycle.
- Building Blocks:
mlflow & TensorFlow Serving.
- Infrastructure:
Putting all components
together.
- CI/CD for ML models.
Agenda

7. Building blocks:
mlflow & TensorFlow Serving

8. Tracking models with mlflow

9. Tracking models with mlflow

10. Tracking models with mlflow

11. Tracking models with mlflow

12. Tracking models with mlflow

13. Serving models with TensorFlow

14. Serving models with TensorFlow

15. Infrastructure
Before After

16. CI/CD for ML models

20. Tracking models with mlflow

21. Aigent’s ML lifecycle

22. Solutions
Manual Work
Training &
Experimentation
No CI/CD Serving

23. Wilder Rodrigues
@wilderrodrigues
medium.com/@wilder.rodrigues
- Software & Artificial Intelligence Engineer
- Apache™ Committer & PMC member
- Keras contributor
- School of AI Utrecht Dean
- Amsterdam AI Ambassador
- Public Speaker