MLOps community meetup #36! This week we talk to David Hershey Solutions Engineer at Determined AI, about Moving Deep Learning from Research to Production with Determined and Kubeflow. 

// Key takeaways:
What components are needed to do inference in ML
How to structure models for ML inference
How a model registry helps organize your models for easy consumption
How you can set up reusable and easy-to-upgrade inference pipelines

// Abstract:
Translating the research that goes into creating a great deep learning model into a production application is a mess without the right tools. ML models have a lot of moving pieces, and on top of that models are constantly evolving as new data arrives or the model is tweaked. In this talk, we'll show how you can find order in that chaos by using the Determined Model Registry along with Kubeflow Pipelines.

// Bio:
David Hershey is a solutions engineer for Determined AI. David has a passion for machine learning infrastructure, in particular systems that enable data scientists to spend more time innovating and changing the world with ML. Previously, David worked at Ford Motor Company as an ML Engineer where he led the development of Ford's ML platform. He received his MS in Computer Science from Stanford University, where he focused on Artificial Intelligence and Machine Learning.

// Relevant Links
www.determined.ai
https://github.com/determined-ai/determined
https://determined.ai/blog/production-training-pipelines-with-determined-and-kubeflow/ 

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https://www.linkedin.com/in/david-hershey-458ab081/

Timestamps:
0:00 - Intros
4:15 - The structure of the chat
5:20 - What is DeterminedAI?
7:20 - How is DeterminedAI different than other more standard artifact storage solutions?
9:25 - Where are the boundaries between what your tool determined AI does really well, and where it works smoothly with other things around it?
11:48 - Is Kubeflow dying?
13:54 - How do you see DeterminedAI and Kubeflow becoming more solidified?
15:55 - How does DeterminedAI interact with Kubeflow at the moment?
18:01 - What type of models they are, is the Kubeflow metadata?
19:18 - What a model registry is and why it's so important to have that?
23:16 - Can you give us the quick demo real fast?
30:52 - Which orchestration tool to use?
32:04 - When using Kubeflow are determined how can you deploy the model through CD tools like Jenkins?
33:40 - How is determined connected to Kubeflow?
36:09 - What components you feel are needed to do inference in machine learning? And how can we structure different models for that machine learning inference?
40:04 - Are they the same one when we talk about ML researchers?
42:14 - How can we better be ready for when we do want to get into the production?
44:59 - In this pipeline, Where do you normally see people getting stopped?
47:05 - What are things that you've been seen pop up that you're not necessarily thinking about in those first phases?
50:17 - What are the most underrated topic regarding deploying machine learning models in production? 
52:44 - How do you see the adoption of tools such as Determined and Kubeflow by Data scientists?
54:40 - Can you explain the Determined open source components?