Building AI that Doesn’t Break
Skills:
AI Systems Design85%
MLOps Community Mini Summit #11! We talked to Elliot Gunton, Senior Software Engineer @ Pipekit, Qian Li, Co-Founder @ DBOS, Inc., and Alan Nichol, Co-founder & CTO @ Rasa
// Abstract
No YAML? No Problem: Orchestrate Kubernetes Workflows the Easy Way with Python
Sick of writing orchestration logic in YAML? You’re not alone. Discover how Hera, the Python SDK for Argo Workflows, lets you express complex Kubernetes workflows using clean, testable Python code. Keep your business logic and orchestration logic in one place — no indentation nightmares required.
Building Reliable AI Applications with Durable Workflows
Chaining functions together is easy. Keeping AI workflows running when things go sideways? That’s the hard part. This talk introduces durable workflows — systems that checkpoint state, recover automatically, and gracefully handle everything from human delays to API flakiness. You’ll see real examples of AI pipelines that stay resilient in production.
Process Calling: Agentic Tools Need State
Function calling gave LLMs a way to "do" things — but it’s not enough. When you’re building agents for customer-facing use cases, stateless abstractions fall short fast. Learn why the future of agentic tooling is process-based, not function-based, and what it means to build agents that remember, recover, and reliably finish what they start.
// Bio:
Elliot Gunton
Elliot is a passionate maintainer of Hera, the Python SDK for Argo Workflows. At Pipekit, he is helping to bring scalable data pipelines to the Python world, unlocking the full potential of Argo Workflows for data scientists. Previously, at Bloomberg, Elliot supported Machine Learning engineers to accelerate their model retraining with Argo Workflows through Hera, simplifying the authoring of complex workflows.
Qian Li
Qian is a co-founder of DBOS, Inc. She completed her Ph.D. in Computer Science at Stanford University in 2023, where her research focused on abstractions for efficient and reliable cloud
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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Remote Collaboration as a Data Scientist
MLOps.community
MLOps Manifesto with Luke Marsden from Dotscience
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MLOps lifecycle description
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What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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Life purpose and too many spreadsheets
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Explainability, Black boxes and EU white paper on reproducibility
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Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
MLOps.community
Automatically Retrain Machine Learning Models? Are best practices worth it?
MLOps.community
Building an MLOps Team? Key ideas to keep in mind
MLOps.community
Hierarchy of MLOps Needs
MLOps.community
Bare necessities for getting an ML model into production
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MLOps and Monitoring
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How Phil Winder got into Data Science and Software Engineering
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Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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Friction Between Data Scientists and Software Engineers
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MLOps Problems in different size companies
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ML tooling in large companies
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ML Platforms - The build vs buy question
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ML Services Gateway at SurveyMonkey
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Message buses, Async and sync architecture
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MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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Hybrid Data Science Teams @SurveyMonkey
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How do you handle ML version control at SurveyMonkey
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Doing ML with Personal Information
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Evolution of the ML feature store @SurveyMonkey
MLOps.community
Developing a Machine Learning Feature Store
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Auto retrain ML models is not the question
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3 key parts to Machine Learning monitoring
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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps.community
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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MLOps: Airflow Pros and Cons
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Specific challenges in Machine Learning
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Current State Of Machine Learning
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Humans in the Loop are a defining factor in Machine Learning
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Learning from real life Machine Learning failures
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Survivorship Bias in machine learning tutorials
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Swiss Cheese model in Machine Learning
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Resume driven development in Machine learning & software engineering
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Who has the highest standards in ML?
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Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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Speed, Trust, Evolution and Scale in MLOps
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More difficult transition for data scientists to become ML engineers
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How many models in prod til I need a dedicated ML platform?
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Deeper thinking from data scientists around platform blackholes
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Checkpointing, metadata, and confidence in your data
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Adjacent usecases and multistep feature engineering
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Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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Reproducability flaws in end to end Machine Learning debugging
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3rd wave of data scientists
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MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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Are Kubeflow and Airflow complementary?
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Why Kubeflow gained so much traction=open community
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Who decides the dirrection of Kubeflow
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What do Kubeflow and Arrikto do and how do they work together?
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Versioning your ML steps with Kubeflow
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Machine Learning Lifecycles//Perception vs Reality
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Kubeflow vs SageMaker in Machine Learning
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