MLOps & Automation Workshop: Bringing ML to Production in a Few Easy Steps

💻 Abstract:
The process of moving from data science research to production pipelines is long and resource-consuming, new practices like MLOps and tools like Kubeflow (ML toolkit and pipeline management over Kubernetes) are emerging to provide the equivalent of CI/CD for data science projects, but this requires dedicated ML engineering teams to translate data-scientists/engineers work to production-ready code. Serverless can simplify data science by automating the process of code to container and enables users to add instrumentation and auto-scaling with minimum overhead. However, serverless has many limitations involving performance, lack of concurrency, lack of GPU support, limited application patterns, and limited debugging possibilities. Yaron Haviv will introduce Kubeflow, and how it works with Nuclio and MLRun, open-source projects enabling serverless data science and full ML lifecycle automation over Kubeflow. Yaron will show real-world examples and a demo and, explain how it can significantly accelerate projects' time to market and save resources.

🔊 Speaker Bio:
CTO and founder, Iguazio
Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI, and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real-time AI. He also initiated and built Nuclio, a leading open-source serverless platform with over 3,400 Github stars and MLRun, Iguazio’s open-source MLOps orchestration framework.
Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development, and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO, and networking company that floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science, and more.

Timestamps:

0:00 Intro
2:50 Getting to know Yaron Haviv
3:14 Coverage of the talk
6:16 General challenge of MLOps
9:11 Did you try running notebooks in production
10:37 Things to do in the transition
15:16 What is DevOps
15:54 What is MLOps
22:02 You can use Separate tools & services, or use Kubernetes as the Baseline
22:43 What is an Automated ML pipeline
23:51 Traditional fraud-detection architecture (Hadoop)
24:20 Real-time fraud prediction & prevention
25:33 How MLOps builds as a stack
28:39 Serverless - moving to micro-services
37:02 ML & Analytics functions architecture
38:13 KubeFlow: Automated ML pipelines & tracking
45:30 Simple, Production-ready development process
48:59 Model deployment & Monitoring in production

Practice and Demo

49:48 Start
1:38:25 Why we need a Feature Store
1:39:40 Unified approach to feature engineering
1:42:09 Driving projects into production
1:45:21 Feature Store Demo

2:02:16 Closing remarks