The document outlines the role of MLOps in improving the efficiency of machine learning processes, emphasizing collaboration between data science and engineering teams. It highlights necessary elements of an MLOps solution, including data management, model tracking, and continuous learning. The call to action is for the community to adopt and standardize MLOps practices using various tools and platforms.

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!