MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine Learning Guide
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CubeFlow vs Cloud Native Machine Learning?
In the last episode with gerwat, we were kind of comparing cube flow with a w sec s service, elastic container service. In sage maker land, the way they do pipe lines is wt. There's a lot of overlap with cube flows offerings, ubernetties offten. And then e c s is the equivalent of cubernets on a w s, a container orchestrator platform. But it's all close source. We don't have visibility how any of the stuff works. Why not get open source if you're goingto be getting the same thing in the first place on aw s?
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Machine learning pipeline orchestration tools, such as SageMaker and Kubeflow, streamline the end-to-end process of data ingestion, model training, deployment, and monitoring, with Kubeflow providing an open-source, cross-cloud platform built atop Kubernetes. Organizations typically choose between cloud-native managed services and open-source solutions based on required flexibility, scalability, integration with existing cloud environments, and vendor lock-in considerations.
Links- Notes and resources at ocdevel.com/mlg/mla-20
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Dirk-Jan Verdoorn - Data Scientist at Dept Agency
Managed vs. Open-Source ML Pipeline Orchestration- Cloud providers such as AWS, Google Cloud, and Azure offer managed machine learning orchestration solutions, including SageMaker (AWS) and Vertex AI (GCP).
- Managed services provide integrated environments that are easier to set up and operate but often result in vendor lock-in, limiting portability across cloud platforms.
- Open-source tools like Kubeflow extend Kubernetes to support end-to-end machine learning pipelines, enabling portability across AWS, GCP, Azure, or on-premises environments.
- Kubeflow is an open-source project aimed at making machine learning workflow deployment on Kubernetes simple, portable, and scalable.
- Kubeflow enables data scientists and ML engineers to build, orchestrate, and monitor pipelines using popular frameworks such as TensorFlow, scikit-learn, and PyTorch.
- Kubeflow can integrate with TensorFlow Extended (TFX) for complete end-to-end ML pipelines, covering data ingestion, preprocessing, model training, evaluation, and deployment.
- Production machine learning systems involve not just model training but also complex pipelines for data ingestion, feature engineering, validation, retraining, and monitoring.
- Pipelines automate retraining based on model performance drift or updated data, supporting continuous improvement and adaptation to changing data patterns.
- Scalable, orchestrated pipelines reduce manual overhead, improve reproducibility, and ensure that models remain accurate as underlying business conditions evolve.
- ML pipeline orchestration tools in machine learning fulfill a role similar to continuous integration and continuous deployment (CI/CD) in traditional software engineering.
- Pipelines enable automated retraining, modularization of pipeline steps (such as ingestion, feature transformation, and deployment), and robust monitoring.
- Adopting pipeline orchestrators, rather than maintaining standalone models, helps organizations handle multiple models and varied business use cases efficiently.
- Managed services (e.g., SageMaker, Vertex AI) offer streamlined user experiences and seamless integration but restrict cross-cloud flexibility.
- Kubeflow, as an open-source platform on Kubernetes, enables cross-platform deployment, integration with multiple ML frameworks, and minimizes dependency on a single cloud provider.
- The complexity of Kubernetes and Kubeflow setup is offset by significant flexibility and community-driven improvements.
- Kubeflow operates on any Kubernetes environment including AWS EKS, GCP GKE, and Azure AKS, as well as on-premises or local clusters.
- Local and cross-cloud development are facilitated in Kubeflow, while managed services like SageMaker and Vertex AI are better suited to cloud-native workflows.
- Debugging and development workflows can be challenging in highly secured cloud environments; Kubeflow’s local deployment flexibility addresses these hurdles.
- TensorFlow Extended (TFX) is an end-to-end platform for creating production ML pipelines, tightly integrated with Kubeflow for deployment and execution.
- While Kubeflow originally focused on TensorFlow, it has grown to support PyTorch, scikit-learn, and other major ML frameworks, offering wider applicability.
- TFX provides modular pipeline components (data ingestion, transformation, validation, model training, evaluation, and deployment) that execute within Kubeflow’s orchestration platform.
- Airflow is a general-purpose workflow orchestrator using DAGs, suited for data engineering and automation, but less resource-capable for heavy ML training within the pipeline.
- Airflow often submits jobs to external compute resources (e.g., AI Platform) for resource-intensive workloads.
- In organizations using both Kubeflow and Airflow, Airflow may handle data workflows, while Kubeflow is reserved for ML pipelines.
- MLflow and other solutions also exist, each with unique integrations and strengths; their adoption depends on use case requirements.
- The optimal choice of cloud platform and orchestration tool is typically guided by client needs, existing integrations (e.g., organizational use of Google or Microsoft solutions), and team expertise.
- Agencies with diverse client portfolios often benefit from open-source, cross-cloud tools like Kubeflow to maximize flexibility and knowledge sharing across projects.
- Users entrenched in a single cloud provider may prefer managed offerings for ease of use and integration, while those prioritizing portability and flexibility often choose open-source solutions.
- Both AWS and GCP offer cost-saving compute options for training, such as spot instances (AWS) and preemptible instances (GCP), which are suitable for non-production, batch training jobs.
- Production workloads that require high uptime and reliability do not typically utilize cost-saving transient compute resources, as these can be interrupted.
- Project initiation begins with data discovery and validation of the client’s requirements against available data.
- Cloud environment selection is influenced by client infrastructure, business applications, and platform integrations rather than solely by technical features.
- Data cleaning, exploratory analysis, model prototyping, advanced model refinement, and deployment are handled collaboratively with data engineering and machine learning teams.
- The pipeline is gradually constructed in modular steps, facilitating scalable, automated retraining and integration with business applications.
- Advanced mathematics or statistics education provides a strong foundation for work in data science and machine learning.
- Master’s degrees in data science add the most value for candidates from non-technical undergraduate backgrounds; those with backgrounds in statistics, mathematics, or computer science may benefit more from self-study or targeted upskilling.
- When evaluating online or accelerated degree programs, candidates should scrutinize the curriculum, instructor engagement, and peer interaction to ensure comprehensive learning.
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