Showing posts with label MLOps. Show all posts
Showing posts with label MLOps. Show all posts

Sunday, July 21, 2024

Overcoming the Hurdles of Deploying Platforms

Modern applications require modern platforms and team collaboration to deliver velocity, stability and security. So how do we overcome the hurdles to making platform adoption work? 

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WHY DO PLATFORMS MAKE SENSE FOR MODERN APPLICATIONS?

  • Reduce the cognitive load on application teams
  • Deliver self-service capabilities where it makes sense
  • Deliver consistent services across teams, with efficient operations
  • Reduce costs, reduce snowflakes, etc.

WHAT ARE THE PLATFORM HURDLES, AND HOW TO OVERCOME THEM?

  • The jump from initial projects to scalable delivery across apps and teams
  • Application teams wanting snowflake environments
  • Buying and budgeting centers not aligning to platform delivery
  • Getting application team buy-in to platform services
  • Getting cross-functional team alignment and coordination
  • Balancing stability and pace of innovation

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Sunday, June 23, 2024

DevSecFinPlatMLOps

As we look at the continued expansion of the CNCF Landscape and the end of Cloud 1.0 era, are there any trends or patterns that identify which technology or categories will succeed vs fail? 

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CAN TECHNOLOGY OVERCOME OUR DESIRE TO NOT COLLABORATE?

  • Should technology cross functional areas? 
  • Can technology be successful when crossing functional areas? 

CAN CROSS-FUNCTIONAL TECHNOLOGIES SUCCEED? 

  • Service Mesh
  • PaaS / Developer Portals
  • GitOps
  • FinOps
  • XX as code

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Sunday, March 24, 2024

Cloud Fundamentals needed for AI

If you’re planning to deploy AI for your business, here’s 5 important capabilities your business needs from the cloud era in order to be successful. 

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IF YOU WANT TO DO AI WELL, YOU NEED TO HAVE DONE CLOUD WELL

  • When we first started doing cloud, the smart people would say, “If you don’t do IT well today, you won’t do cloud well in the future”.
  • The same pattern will repeat itself with AI

5 IMPORTANT CLOUD CAPABILITIES NEEDED TO SUCCEED WITH AI

  1. Automate everything - Make automation mission-critical
  2. Build the right abstractions and flexibilities (e.g. sharing GPUs, Devtools, etc.)
  3. Leverage platforms to bring together Data Science, MLOps and AppDev
  4. Know how much things cost (e.g. FinOps)
  5. Socialize success, and socialize learnings across teams

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Wednesday, June 15, 2022

Observability for Machine Learning

Alessya Visnjic (CEO, WhyLabs) talks about MLOps, the concept of ML Observability and why AI models can fail. Alyessa talks about the differences between data health and model health and why post production analysis of ML is so important.

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Topic 1 - Welcome Alessya! You are what is known in the AI/ML spaces as a veteran. For those who aren’t familiar with your previous work, how about a quick introduction and background.

Topic 2 - Give everyone a background in MLOps as it is still an emerging market.  We are seeing an emerging trend of trust in data to train models. How did we get to this problem? Is this a transparency and observability problem once in production?

Topic 3 -  How is model health different from data health? Post deployment of models can actually be a factor, things like data drift over time…

Topic 4 - What does a typical tool chain look like? Under the covers is this a logging platform to provide visibility into the model behavior to ensure accuracy over time? I would think every model is different, how do you “standardize/rationalize” the data to detect anomalies and incorrect results?

Topic 5 - Every new category of tools has leading use cases. Where are you seeing the most traction today and how can you best help practitioners? 

Topic 6 - How can folks get started if they are interested?

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Wednesday, February 3, 2021

Automating Analytics Teams

Derek Knudsen (@dsknudsen, CTO at @Alteryx) talks about the differences between analytics and data science teams, critical analytics workflows, aligning culture and technologies, and best practices in presenting data. 

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Topic 1 - Welcome to the show. Tell us a little bit about your background, and what makes you passionate about helping analytics teams improve their businesses? 

Topic 2 - Can we start by talking about how you think about Analytics teams vs. Data Science teams vs. AI/ML teams? Are these different only in name, or are their functional/skill differences, or places where one group is more appropriate than others? 

Topic 3 - Let’s talk about Analytics in the context of workflows. Are you seeing it still be mostly a business analyst “offline” function, or are more workflows and applications introducing more “real-time” analytics capabilities? 

Topic 4 - We talk a lot on this show about DevOps and Developer Productivity, in the context of more frequently changing applications. How does that apply to Analytics groups? Where do they have bottlenecks today? How do they get around those bottlenecks?

Topic 5 - How do platforms like the Alteryx Analytics Platform help teams improve their analytics velocity and productivity? And how much do you find that the right tools help improve how teams organize, or do they need to be well organized to best take advantage of the right tools? 

Topic 6 - Can you give us some examples of the types of results that companies often achieve when they better align their analytics teams to self-service and automated environments?


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Wednesday, June 17, 2020

MLOps, GPUs and AI Developers

Dillon Erb (@dlnrb, CEO @HelloPaperSpace) talks about what exactly is MLOps, Serverless AI platforms, and how developers can utilize GPUs for AI/ML.

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Topic 1 - Dillon, welcome to the show, tell us a little bit about yourself and how you got involved in this space?

Topic 2 - I’ve had a running joke on the show that a market doesn’t exist until you attach Ops to it. Today we’ll talk about MLOps. Give everyone an introduction for those not familiar.

Topic 3 - What exactly is a Serverless AI Platform? How does this differ from traditional CI/CD platforms that our listeners would be used too? Is this abstracting away the infrastructure layer for MLOps teams?

Topic 3a - Switching gears from Ops to Developers, what do you mean when you say that you make it easy for developers to use GPUs? What do developers need to know about hardware-level stuff like GPUs that they didn’t need to know with CPUs?

Topic 4 - As with all things emerging tech, the use cases are constantly evolving. What are the early initial use cases that you are seeing? Are there unique things that emerge for gaming or media applications?

Topic 5 - How does access to data models fit into all of this?

Topic 6 - I noticed your company did some articles on Covid-19, can you explain what is going on there?

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