MLOps Conference

ML workflows and processes are critical for enabling rapid prototyping and deployment of ML/AI models in any organization. This conference is a platform to present and discuss such workflows and patterns that drive data-driven organizations to operate at scale.

We are accepting experiential talks and written content on the following topics:

1. ML development workflows.
2. ML deployment frameworks.
3. Data lineage.
4. Model lineage.
5. Model ethics/bias testing.
6. A/B testing frameworks.
7. Model governance.
8. Explainability/interpretability of models in run-time.
9. Impact of change in MLOps mindset in product organizations.
10. DataOps workflows.
11. DataOps frameworks.
12. Alerting, monitoring and managing models in production.
13. Growing and managing data teams.
14. MLOps in research.
15. Deployment and infrastructure for machine learning.
16. ROI (Return on Investment) for MLOps.

Who should speak?

1. MLOps engineers who build and maintain ML workflows and deploy ML models.
2. Data engineers building production scale data pipelines, feature stores, model dashboards, and model maintenance.
3. Tech leaders/engineers/scientists/product managers of companies who have built tools and products for ML productivity.
4. Tech leaders/engineers/scientists/product managers of companies who have built tools, products, processes for Data Ops to support ML.
5. Tech Leaders/engineers/scientists/product managers who have experience with products that failed to make a mark in the market due to ML failures.
6. Investors who are investing in the space of ML productivity tools, frameworks and landscape.
7. Privacy/ethics stakeholders involved in model governance and testing for ethics/bias.

Content can be submitted in the form of:

• 15 minute talks
• 30 minute talks
• 1,000 word written articles

All content will be peer-reviewed by practitioners from industry.