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Kei Nemoto- Gentle introduction to scaling up ML service with Kubernetes + Mlflow | PyData NYC 2022

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Tubidy MP3 & MP4

Kei Nemoto- Gentle introduction to scaling up ML service with Kubernetes + Mlflow | PyData NYC 2022

Type

Size

İlgili Videolar

Kei Nemoto- Gentle introduction to scaling up ML service with Kubernetes + Mlflow | PyData NYC 2022

Kshetrajna Raghavan- Using Interconnected ML Models to Tackle Retail Challenges | PyData NYC 2022

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Brian Bush: Practical MLOps- Do we need all the things | PD NYC 2022

Martin Hirzel- Fairness for Scikit-Learn Pipelines with Lale | PyData NYC 2022

Diego Torres Quintanilla - How we upstreamed our internal goals to JupyterLab 4 | PyData NYC 2022

Using Coresets to Develop and Maintain Better ML | Eitan Netzer (DataHeroes)

Dealing with the versioning of production-ready models - Corné Vriends | PyData Eindhoven 2021

Managing large-scale ML pipelines with MLflow and serverless computing. - ilyas chaoua

Mary Grace Moesta - MLOps Deployment Patterns with Delta Lake and MLflow | PyData Seattle 2023

Thomas Caswell - From Science to Open Source and Back Again | PyData NYC 2022

Efficient AutoML with Ludwig, Ray, and Nodeless Kubernetes - Anne Marie Holler + Travis Addair

Next-Generation Data Science Workflows Using Ray - Erik Erlandson, Red Hat

Jarek Potiuk - Managing Python Dependencies at scale | PyData Global 2022

Tobias Sterbak: Introduction to MLOps with MLflow

Concurrent-AI: A Parallelized AI Execution Engine for MLFlow and Kubernetes (Kubeflow Summit 2022)

Jeff Hale - Supercharge your Python code with Blocks | PyData NYC 2022

Laszlo Sragner - Clean Architecture: How to Structure Your ML Projects to Reduce Technical Debt

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Tubidy MP3 & MP4

Kei Nemoto- Gentle introduction to scaling up ML service with Kubernetes + Mlflow | PyData NYC 2022

Type

Size

İlgili Videolar

Kei Nemoto- Gentle introduction to scaling up ML service with Kubernetes + Mlflow | PyData NYC 2022

Kshetrajna Raghavan- Using Interconnected ML Models to Tackle Retail Challenges | PyData NYC 2022

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Brian Bush: Practical MLOps- Do we need all the things | PD NYC 2022

Martin Hirzel- Fairness for Scikit-Learn Pipelines with Lale | PyData NYC 2022

Diego Torres Quintanilla - How we upstreamed our internal goals to JupyterLab 4 | PyData NYC 2022

Using Coresets to Develop and Maintain Better ML | Eitan Netzer (DataHeroes)

Dealing with the versioning of production-ready models - Corné Vriends | PyData Eindhoven 2021

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Mary Grace Moesta - MLOps Deployment Patterns with Delta Lake and MLflow | PyData Seattle 2023

Thomas Caswell - From Science to Open Source and Back Again | PyData NYC 2022

Efficient AutoML with Ludwig, Ray, and Nodeless Kubernetes - Anne Marie Holler + Travis Addair

Next-Generation Data Science Workflows Using Ray - Erik Erlandson, Red Hat

Jarek Potiuk - Managing Python Dependencies at scale | PyData Global 2022

Tobias Sterbak: Introduction to MLOps with MLflow

Concurrent-AI: A Parallelized AI Execution Engine for MLFlow and Kubernetes (Kubeflow Summit 2022)

Jeff Hale - Supercharge your Python code with Blocks | PyData NYC 2022

Laszlo Sragner - Clean Architecture: How to Structure Your ML Projects to Reduce Technical Debt

Nidhin Pattaniyil - Building a Search Engine | PyData Seattle 2023

PyData Zurich July 2023: ONNX/Spox and BigCode

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