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ML Platforms

Overview

ML platforms provide end-to-end infrastructure for the machine learning lifecycle — from data management and experiment tracking through model training, deployment, and monitoring. They reduce the engineering burden of building custom MLOps infrastructure, allowing teams to focus on model development.

Platform Landscape

graph TD
    A[ML Platforms] --> B[Open Source]
    A --> C[Cloud Managed]
    A --> D[Specialized]
    B --> B1[MLflow]
    B --> B2[Kubeflow]
    B --> B3[Feast]
    C --> C1[Vertex AI - Google]
    C --> C2[SageMaker - AWS]
    C --> C3[Azure ML]
    D --> D1[Weights & Biases]
    D --> D2[Neptune.ai]
    D --> D3[Comet ML]

Platform Comparison

PlatformProviderStrengthsWeaknesses
MLflowOpen SourceSimple, framework-agnosticLimited serving
KubeflowOpen SourceK8s-native, scalableComplex setup
Vertex AIGoogleFully managed, AutoMLVendor lock-in
SageMakerAWSComprehensive, Studio UIComplex pricing
Azure MLMicrosoftEnterprise, Azure integrationAzure lock-in
W&BSpecializedBest experiment trackingNot full platform

Core Platform Components

graph LR
    A[Data] --> B[Feature Store]
    B --> C[Experiment Tracking]
    C --> D[Training]
    D --> E[Model Registry]
    E --> F[Deployment]
    F --> G[Monitoring]
    G -->|Retrain| C

Interview Questions

  1. Build vs buy an ML platform? — Buy when: small team, standard workflows, need to move fast. Build when: unique requirements, scale demands, engineering capacity. Most start with managed and migrate to custom as they scale.

  2. What are the must-have features of an ML platform? — Experiment tracking, model registry, feature store, training infrastructure, deployment automation, and monitoring.

  3. How do you choose between cloud ML platforms? — Consider existing cloud ecosystem, specific ML needs (AutoML, LLM support), team expertise, and cost. Multi-cloud strategies add complexity.

  4. What is the role of an ML platform team? — Build and maintain the platform, provide tooling and templates, support ML teams, ensure best practices, and manage infrastructure costs.

Summary

ML platforms abstract the complexity of MLOps infrastructure. Open-source options (MLflow, Kubeflow) offer flexibility, cloud platforms (Vertex AI, SageMaker) provide managed convenience, and specialized tools (W&B) excel at specific tasks. The choice depends on team size, existing infrastructure, and specific ML requirements.

Cross-References