Lilly Tech Systems
Intermediate
ML Platforms Comparison
Evaluate the major machine learning platforms for custom model training, deployment, and management. Understand how each platform fits different organizational needs and technical requirements.
Platform Overview
| Platform | Provider | Strengths | Best For |
|---|---|---|---|
| SageMaker | AWS | Breadth of features, scale, AWS ecosystem | AWS-native organizations |
| Azure ML | Microsoft | Enterprise integration, OpenAI models | Microsoft/Azure shops |
| Vertex AI | Gemini integration, AutoML, BigQuery | Google Cloud users, data-heavy orgs | |
| Databricks | Databricks | Unified data + ML, lakehouse, MLflow | Data engineering teams |
| Hugging Face | Hugging Face | Open-source models, community, ease of use | Research, open-source-first teams |
AWS SageMaker
The most comprehensive ML platform with features spanning the entire ML lifecycle:
- Training: Managed training jobs with distributed computing, spot instances, and automatic model tuning
- Deployment: Real-time endpoints, batch transform, serverless inference, and multi-model endpoints
- MLOps: Model registry, pipelines, monitoring, and feature store
- LLM support: Amazon Bedrock for managed LLM access alongside custom model training
- Consideration: Steep learning curve, can be complex for simple use cases
Azure Machine Learning
Deep integration with the Microsoft ecosystem makes it a natural choice for enterprise:
- Training: Managed compute clusters, automated ML, designer (drag-and-drop)
- Deployment: Managed online/batch endpoints, AKS integration
- MLOps: MLflow integration, pipelines, responsible AI dashboard
- LLM support: Azure OpenAI Service provides enterprise-grade access to OpenAI models
- Consideration: Best value when already invested in Azure and Microsoft 365
Google Vertex AI
Strong integration with Google's data infrastructure and native access to Gemini models:
- Training: AutoML, custom training with TPU/GPU, hyperparameter tuning
- Deployment: Prediction endpoints, batch prediction, edge deployment
- MLOps: Vertex AI Pipelines, model monitoring, feature store, experiments
- LLM support: Native Gemini access, Model Garden with 100+ models
- Consideration: Excellent for teams using BigQuery and Google Cloud data tools
Choosing a Platform
Decision guide:
- Already on AWS? SageMaker + Bedrock is the path of least resistance
- Microsoft shop? Azure ML + Azure OpenAI integrates with your existing tools
- Data in BigQuery? Vertex AI keeps everything in one ecosystem
- Multi-cloud or cloud-agnostic? Databricks or open-source tools like MLflow
- Startup or small team? Hugging Face or managed inference services minimize operational burden
Build vs Buy Considerations
Not every organization needs a full ML platform. Consider these factors:
- Using LLMs via API only? You may not need an ML platform at all. Direct API integration might suffice.
- Training custom models? A managed platform saves significant infrastructure engineering time.
- Team size matters: Small teams benefit from managed services. Large ML teams may prefer more control.
- Regulatory requirements: Some industries require on-premise or specific cloud deployments.