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![MLOps – the term
• Production ML has been done
for many years in large web
companies and others
• First MLOps Platform for
enterprises – from ParallelM in
2018
• Inspired by Database practices
and DBAs, Software lifecycle
• Focus on full lifecycle tooling
combined with Best Practices
• https://www.kdnuggets.com/2
018/04/operational-machine-
learning-successful-mlops.html
MLOps (a compound of “machine learning” and
“operations”) is a practice for collaboration and
communication between data scientists and operations
professionals to help manage production ML (or deep
learning) lifecycle.[1] Similar to
the DevOps or DataOps approaches, MLOps looks to
increase automation and improve the quality of production
ML while also focusing on business and regulatory
requirements. While MLOps also started as a set of best
practices, it is slowly evolving into an independent
approach to ML lifecycle management. MLOps applies to
the entire lifecycle - from integrating with model
generation (software development lifecycle, continuous
integration/continuous delivery), orchestration, and
deployment, to health, diagnostics, governance, and
business metrics.
https://www.kdnuggets.com/2018/04/operational-machine-
learning-successful-mlops.html](https://image.slidesharecdn.com/mlopspastpresentfuture-200824234353/85/Ml-ops-past_present_future-7-320.jpg)
Uploaded byNisha Talagala
Ml ops past_present_future
MLOps, a practice combining machine learning and operations, focuses on collaboration to manage the production ML lifecycle, evolving from simple best practices into a comprehensive approach. Despite the availability of sophisticated AI technologies and cloud services, adoption remains minimal due to the complexity of integration among multiple roles and tools. The future of MLOps will likely expand to include aspects like validation, security, adaptability, and scalability.
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Ml ops past_present_future
- 1. MLOps – Past,Present and Future Nisha Talagala CEO, Pyxeda AI
- 2. In this talk •What is “MLOps” • MLOps Areas and Evolution • Two MLOps topics • Cloud - End to end pipelines in the Cloud • Drift – suddenly everywhere • MLOps Future
- 3. Sophisticated AI technologies, more availableevery day Each logo is a (separate) service offered by GCP, AWS or Azure for part of an AI workflow
- 4.
- 5. Why? • Flow iscomplex and multi- faceted • Tools are at different levels and tackle subsets of workflow • Multiple roles collaborate Data Train Model(s) Develop Model(s) Test Model(s) Deploy Model(s) Connect to Business app App developers Data Scientists ML Engineers Operations Business Need Monitor and Optimize Many levels of complexity
- 6. A typical flow •Use case definition • Data prep • Modeling • Training • Deploy • Integrate • Monitor/Optimize • Iterate Data Train Model(s) Develop Model(s) Test Model(s) Deploy Model(s) Connect to Business app App developers Data Scientists ML Engineers Operations Business Need Monitor and Optimize
- 7. MLOps – theterm • Production ML has been done for many years in large web companies and others • First MLOps Platform for enterprises – from ParallelM in 2018 • Inspired by Database practices and DBAs, Software lifecycle • Focus on full lifecycle tooling combined with Best Practices • https://www.kdnuggets.com/2 018/04/operational-machine- learning-successful-mlops.html MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle.[1] Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. While MLOps also started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. https://www.kdnuggets.com/2018/04/operational-machine- learning-successful-mlops.html
- 8. MLOps Evolution Taken from: https://www.kdnuggets.com/2018/0 4/operational-machine-learning- successful-mlops.html •Deployment and Serving • Many tools but also startups • Reproducibility • Many open source tools • Containerization, Orchestration • Established patterns • Governance • Emerging rules and tools • Monitoring, Health Diagnostics • Many startups • Lifecycle - Emerging 2018 2020
- 9. In this talk •Origin of “MLOps” • MLOps Areas and Evolution • Two MLOps topics • End to end pipelines in the Cloud • Drift – suddenly everywhere • MLOps Future
- 10. MLOps in Today’sContext – AWS Example Substantial tooling available for each stage from multiple cloud vendors Labeling Data Prep and Visualization Modeling and Deployment Marketplaces Service APIs Manipulate raw data Build, tune or deploy your own models Buy a model or algorithm Use a pre-built AI (example voice to text, etc.) Infrastructure: Compute, Authentication, Data source, Logs etc. Where your AI runs and what monitors it
- 11. S3 A Sample MLLifecycle in AWS Dataset prep and transform AWS Sagemaker Sagemaker endpoint + Marketplace inference code External endpoint (basic Lambda) Dataset Model Artifact Postman Request Prediction AWS Marketplace Modified Dataset API Gateway Custom Docker Containers
- 12. In this talk •Origin of “MLOps” • MLOps Areas and Evolution • Two MLOps topics • End to end pipelines in the Cloud • ML Health and Drift – suddenly everywhere • MLOps Future
- 13. MLOps and COVID Whatis Drift? https://www.weforum.org/agenda/2020/ 05/here-s-how-to-check-in-on-your-ai- system-as-covid-19-plays-havoc/ May 22, 2020
- 14. Drift •Types of Drift •Concept Drift • Data Drift • Prediction inputs • Training vs Prediction
- 15. Data Drift Example- Gradual change in distribution Training Data Production Data: 1-500 Production Data: 500-1000 Production Data: 1000-1500 Bank churn data from Kaggle – Drift Test Generator by Pyxeda (contact info@pyxeda.ai)
- 16. How can thistype of shift in distribution be detected Simple rules like • monitoring the mean (for continuous vaiables) • RMSE (for categorical variables) Other techniques to measure distribution divergence in one dimension • Kolmogorov-Smirnov test (continuous) • Bhattacharyya distance (categorical) • Earth movers distance (categorical)
- 17. How can thistype of shift in distribution be detected Relational Drift • Drift can happen in relationship between two variables Techniques for detecting multi-dimensional drift • Not much literature yet
- 18. In this talk •Origin of “MLOps” • MLOps Areas and Evolution • Two MLOps topics • End to end pipelines in the Cloud • ML Health and Drift – suddenly everywhere • MLOps Future
- 19. Model Governance –Current and Emerging • The process of ensuring that Model creation meets an organization or industry’s compliance and other requirements • For heavily regulated industries, this is a legal issue • For example – Finance has model governance rules that MUST be followed before models are deployed • For other industries, it is a risk mitigation issue • For example - If someone complains of bias – what model was used and how did it come about? • New laws – EU GDPR, CA CCPA, etc. apply across industries Governance is a combination of Compliance, Reproducibility, Security and Integrity
- 20. Model Security –On the Horizon • Models can be hacked • The more advanced the AI, the more likely it can be hacked • For example – if the AI is self adapting (such as online algorithms), they can “drift” and a well placed attack can control the direction of drift • Other attacks can “probe” the model, understand its behavior and then exploit it • Corrupting data is another way to corrupt the (consequent) model Model Security is an emerging topic. Overview of security and relationship to integrity - see https://www.forbes.com/sites/cognitiveworld/2019/01/29/ml-integrity- four-production-pillars-for-trustworthy-ai/#279ee9ec5e6f
- 21. Summary MLOps is hereto stay. One startup in 2018 to 10s or more now. Started with Deployment, now encompasses the whole lifecycle Can also be seen in MLOps Engineer, Full Stack Data Scientist and Applied ML Engineer roles and teams Expect future to include Validation, Security, Adaptability and Scale