Guests Patrick Barker, Founder / CTO of Kentauros AI and Farhood Etaati, Software Engineer at Yektanet, discuss the challenges and skepticism surrounding MLOps and its relation to previous ML models. They explore the potential future developments, the significance of knowledge transfer, and the emergence of persona-specific tools. The speakers also mention building a tool for TypeScript developers and discuss the challenges faced by MLOps engineers.
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Quick takeaways
The podcast explores the challenges of adopting newer specialized tools in MLOps and discusses the sustainability of the field in general.
The episode highlights the potential of agents in automating various ML Ops tasks and shaping the future of ML Ops.
Deep dives
ML Ops and the Evolution of MLOps
The podcast episode delves into the discussion of MLOps and its evolution. Two opinionated individuals share their insights on the subject, offering contrasting viewpoints. The episode highlights the differences between MLOps and the emerging field of LLMOps, emphasizing the specific user profiles and needs of each. The conversation touches on the challenges of data engineering in the MLOps space and the potential impact of generative AI for ML Ops. The guests also explore the ever-growing influence of large language models and their potential to render traditional ML 1.0 approaches less relevant. While acknowledging the hype and noise surrounding the field, the podcast ultimately recognizes the value of exploring new directions and pushing the boundaries of AI and ML operations.
The Role of Agents in ML Ops
The episode delves into the role of agents in ML Ops and their potential to transform the field. The guests discuss the power of agents and how they can be applied to automate various ML Ops tasks, including training, evaluating, and serving models. The conversation highlights the promise of training agents to handle specific use cases, as well as their potential to accelerate the delivery of ML solutions. The guests touch on the challenges of data modeling and the need for more efficient and scalable models. While recognizing the ongoing research efforts in the field, they explore the potential for agents to play an integral role in shaping the future of ML Ops.
Navigating the MLOps Landscape
The podcast episode addresses the current landscape of MLOps and the challenges faced by practitioners in the field. The guests discuss the differences between ML 1.0 and LLMOps, highlighting the distinct user profiles and needs of each. They explore the hype and noise surrounding the field, including the use of buzzwords and the potential for overpromising and underdelivering. The episode emphasizes the importance of a holistic approach to MLOps, focusing on mindset, automation, and specific challenges such as data engineering. The guests discuss the need for reliable blueprints and the importance of continually evolving the field to meet the demands of future problem solvers.
MLOps, LLMOps, and the Future of AI
The podcast episode delves into the future of AI operations, discussing the potential impact of MLOps and LLMOps. The guests explore the challenges and opportunities presented by ML 1.0 and LLMOps, highlighting areas such as automation, scalability, and energy consumption. They discuss the need for efficient models and the importance of making foundational models more accessible and eco-friendly. The conversation addresses the evolving needs of developers and the potential of generative AI for ML Ops. The guests reflect on the ongoing progress in the field and the crucial role of ML Ops engineers in navigating complex paradigms and shaping the future of AI.
Farhood Etaati is a Software Engineer at Yektanet.
MLOps podcast #204 with Patrick Barker, CTO of Kentauros AI and Farhood Etaati, MLOps/Platform Team Lead at AIMedic, MLOps at the Crossroads.
// Abstract
MLOps is at a crossroads. The ever-increasing excitement for LLMs' ability to solve some interesting real-world problems has made many people interested in applying these models in new applications which comes with its own challenges, that have upstarted the term "LLMLOps". But how much of those challenges are not a newer representation of what older-gen ML models had to deal with in the production, and the question arises whether developing "new" specialized tools to address these applications actually provides any substantial value for the sustainability of the field in general. Tools are coming and going at a rate that makes many technical people skeptical of adopting newer tools. What can we do as a community to alleviate these issues? Why OSS MLOps is lacking behind and how VC money is contributing to that?
// Bio
Farhood Etaati
MLOps engineer at AIMedic. Studied EE at Uni of Tehran, started out as a data scientist, and pivoted to software engineering. Currently working on on-premise MLOps platform development suitable for Iran's infrastructure.
Patrick Barker
When Patrick is not occupied with building his AI company, he enjoys spending time with his wonderful kids or exploring the hills of Boulder.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Websites: https://github.com/pbarker
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Patrick on LinkedIn: https://www.linkedin.com/in/patrickbarkerco/
Connect with Farhood on LinkedIn: www.linkedin.com/in/farhood-etaati
Timestamps:
[00:00] Farhood's and Patrick's preferred coffee
[01:13] Takeaways
[04:00] Please like, share, and subscribe to our MLOps channels!
[05:26] Strong feelings
[10:21] MLOps vs DevOps Challenges
[13:44] Medical setting, ML tools, NLP, model building
[16:23] MLOps vs Data Engineering
[20:45] MLOps Boosts LLM Development
[23:54] Longtail Use Cases
[31:00] Tech Roles Distinctions
[34:42] Did He Say That?
[37:04] Fine-tuning AI Models
[38:57] ML 2.0 Advancements Explained
[41:11] Generative AI in MLOps
[45:04] ML Reproducibility Challenges
[48:03] Wrap up
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