MLA 020 Kubeflow and ML Pipeline Orchestration on Kubernetes
Machine Learning Guide
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Cloud Computing
Muc definitely a maker's thinga tatters job. Increasingly, i'm trying to do a lot of my development in the cloud. What we often see is that clients come with a direct question and already some sort of an idea what they want. The first step would really be to go back to the d drawing table andnd, have a look at their data. And then as you go through those steps, you just say, but as we're going along, i'm choosing cubi flow the nex sens ye for sure.
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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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