MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows. - Reduce friction between science and engineering - Deploy your models to production faster - Health, diagnostics and governance of ML models - Kubernetes as a core platform for MLOps - Support advanced use-cases like continual learning with MLOps

2. whoami
● Solutions Architect @ cnvrg.io
● = built by data scientists, for data scientists to help teams:
○ Get from data to models to production in the most efficient and fast way
○ Bridge science and engineering
○ Automate MLOps

3. def agenda(30 mins):
● Introduction to MLOps
● What can MLOps do?
● A world without MLOps
● Elements of an MLOps solution
● MLOps with cnvrg.io

4. MLOps.intro()
● Is it a practice? A job? A platform?
● Collaboration & communication between data science & engineering
professionals
● Stabilizes pipeline for entire ML lifecycle
● Create a real Machine Learning Cycle
● Ease friction at every point of the pipeline Research
Data ExplorationDeployment
Training

5. if not MLops:
+ Not all data scientists come from a DevOps background and are not
always following best practices
+ Enterprise ML development is slow & hard to standardize/scale
+ So many Tools & Frameworks
= Many delays in process
= Friction between teams
= Wasted resources
= Tough to iterate on models
= No standardization in industry

6. if MLOps == True:
● Deploy faster, easier and more often
● Centralize model tracking, versioning and monitoring
● Integrate different technologies together
● Reduce friction between science and engineering
○ Enhance collaboration
● Move towards standardization in the field
● Support continual learning with MLOps (CI/CD)
○ Webinar on CI/CD for Machine Learning

7. MLOps.elements()
1. Data management
2. Collaboration & communication
3. Model tracking, version and management
4. One-click experiment execution and model deployment
5. Unifies team no matter what language, framework, or provider
6. Features to assist every role in the team
a. Data Scientist
b. Engineer
c. Business Managers
7. Continual learning - the automatic deployment and retraining of models
IN production

8. MLOps.now()
● Many half-baked solutions
● Very early open-source technologies
● Requires deep know how to even implement

9. cnvrg.demo()

10. webinar.summary()
● Data Science is a quickly advancing field
● The lifecycle is inefficient within most firms
● MLOps is a still developing field that should be quickly adopted
● As a community we need to invest in standardizing MLOps
● You can incorporate MLOps with open source tools or full platform
solutions

11. Thanks!
https://cnvrg.io
info@cnvrg.io
+972506600186

12. Next webinar!