Overview
An ML platform is the infrastructure layer that enables data scientists and ML engineers to build, deploy, and operate machine learning models efficiently. Unlike individual ML models or applications, an ML platform provides reusable infrastructure: model registries, feature stores, experiment tracking systems, model serving infrastructure, monitoring tools, and CI/CD pipelines for ML artifacts.
Think of it as the difference between building a single house versus building the construction infrastructure that enables building many houses efficiently. An ML platform team builds the tools, systems, and patterns that allow ML practitioners across the organization to ship models faster, more reliably, and at scale.
Companies like Netflix (feature stores and model serving), Uber (Michelangelo platform), and Airbnb (Zipline feature store) have built internal ML platforms that accelerate ML development across hundreds of models. For hiring, prioritize engineers who've operated ML systems in production—they understand the real challenges that platforms must solve.
What Success Looks Like
Before diving into hiring, understand what a successful ML platform initiative achieves. Your platform should deliver measurable improvements within the first 6-12 months—not just infrastructure, but infrastructure that accelerates ML development across your organization.
Characteristics of Successful ML Platforms
1. Self-Service Capabilities
Data scientists and ML engineers can deploy models, access features, and run experiments without waiting on platform engineers. The platform abstracts away infrastructure complexity while maintaining control and governance.
2. Reliable Model Operations
Models deploy consistently, monitor automatically, and fail gracefully. Platform infrastructure handles retraining pipelines, A/B testing, rollbacks, and performance degradation detection without manual intervention.
3. Developer Experience Focus
The platform feels like a product, not infrastructure. Clear documentation, intuitive APIs, helpful error messages, and fast iteration cycles. ML practitioners prefer using your platform over building custom solutions.
4. Observability and Governance
Every model deployment is tracked, every feature is versioned, every experiment is logged. The platform provides visibility into ML operations across the organization and enforces standards without blocking innovation.
5. Cost Efficiency
The platform optimizes compute costs, reduces redundant infrastructure, and enables cost-aware model deployment decisions. ML practitioners understand the cost implications of their choices.
Warning Signs of Struggling ML Platforms
- Data scientists bypass the platform to deploy models manually
- Platform engineers spend most time on support tickets rather than building
- No clear metrics on platform adoption or impact
- Models deployed through the platform fail more often than manual deployments
- Platform APIs are inconsistent or poorly documented
- Infrastructure costs grow faster than model count
- Platform team becomes a bottleneck for ML development velocity
Roles You'll Need
an ML platform requires a specific team composition that differs from both traditional software engineering and ML research teams.Core Team (First 3-5 Engineers)
Senior ML Platform Engineer (1-2)
These engineers set the technical direction and build core platform components. They combine strong software engineering skills with deep understanding of ML systems.
What to look for:
- Experience operating ML systems in production (not just training models)
- Strong software engineering fundamentals (distributed systems, APIs, databases)
- Understanding of ML lifecycle: training, deployment, monitoring, retraining
- Can design systems that abstract complexity while maintaining flexibility
- Product mindset—thinks about developer experience, not just technical elegance
Why this role first: Platform architecture decisions made early have lasting impact. You need senior engineers who can make good trade-offs between flexibility and standardization.
ML Infrastructure Engineer (1-2)
These engineers build and maintain the infrastructure components: model serving systems, feature stores, experiment tracking, and monitoring tools.
What to look for:
- Experience with ML serving frameworks (TensorFlow Serving, TorchServe, Triton)
- Understanding of feature stores and online/offline feature serving
- Experience with experiment tracking tools (MLflow, Weights & Biases, custom)
- Can optimize for performance, cost, and reliability
- Comfortable with Kubernetes, containers, and cloud infrastructure
MLOps Engineer (1)
Focuses on CI/CD for ML, model registry, deployment pipelines, and operational tooling.
What to look for:
- Experience automating ML workflows and pipelines
- Understanding of model versioning and artifact management
- Can build developer tools and CLI interfaces
- Experience with infrastructure as code (Terraform, CloudFormation)
- Strong debugging and troubleshooting skills
Growth Team (Engineers 6-10)
As the platform matures, add specialists:
| Role | When to Add | What They Own |
|---|---|---|
| Feature Store Engineer | When feature reuse becomes critical | Online/offline feature serving, feature discovery, data quality |
| Model Serving Specialist | When serving becomes a bottleneck | Inference optimization, latency reduction, cost optimization |
| Observability Engineer | When monitoring needs scale | ML-specific monitoring, alerting, dashboards, anomaly detection |
| Platform Product Engineer | When adoption needs focus | Developer experience, documentation, SDKs, user support |
| Data Platform Integration | When data pipelines need platform integration | Data pipeline integration, data quality, lineage |
Common Mistake: Hiring Only ML Researchers
Don't hire researchers expecting them to build production infrastructure. ML platform engineering requires software engineering skills first, ML knowledge second. The best platform engineers are software engineers who've specialized in ML systems, not researchers learning to code.
What actually matters:
- Can they build reliable distributed systems?
- Do they understand production operations?
- Have they operated ML systems at scale?
- Can they design APIs and developer experiences?
- Do they think about cost and efficiency?
Platform Architecture Decisions
The architecture choices you make early determine what your platform can become. Understanding these decisions helps you hire engineers who can make them well.
Centralized vs Distributed Platform
Centralized Model
- Single platform team owns all ML infrastructure
- Ensures consistency but can become a bottleneck
- Best for: smaller organizations, early-stage platforms
Federated Model
- Domain teams build on platform primitives
- Platform team provides core infrastructure and standards
- Best for: larger organizations, multiple ML use cases
Hybrid Model
- Platform team owns core infrastructure (feature store, model registry, serving)
- Domain teams own domain-specific tooling
- Best for: growing organizations transitioning to scale
Build vs Buy Decisions
| Component | Build | Buy | Recommendation |
|---|---|---|---|
| Model Registry | Complex but core | MLflow, Weights & Biases | Buy initially, customize later |
| Feature Store | Core differentiation | Feast, Tecton, AWS SageMaker Feature Store | Buy if available, build if unique needs |
| Experiment Tracking | Standardized | MLflow, Weights & Biases, Neptune | Buy — not your core competency |
| Model Serving | Core competency | SageMaker, Vertex AI, custom | Build — serving is platform core |
| Monitoring | Can start simple | Custom + Prometheus/Grafana | Hybrid — buy observability, build ML-specific |
| CI/CD for ML | Core competency | Custom + standard CI/CD | Build — ML-specific needs |
Key Architectural Patterns
Model Registry Pattern
- Centralized storage for model artifacts, metadata, and versions
- Enables model discovery, versioning, and governance
- Critical for organizations with many models
Feature Store Pattern
- Separates feature computation from model training and serving
- Enables feature reuse across models
- Reduces training-serving skew
Experiment Tracking Pattern
- Logs experiments, hyperparameters, metrics, and artifacts
- Enables reproducibility and comparison
- Foundation for model selection and optimization
Model Serving Pattern
- Separates model deployment from model training
- Enables A/B testing, canary deployments, and rollbacks
- Critical for production reliability
Team Structure
How you organize your ML platform team affects how quickly the platform evolves and how well it serves ML practitioners.
Early Stage (2-4 Engineers): Generalist Team
At this stage, everyone works across platform components. The same engineer might build feature store infrastructure in the morning and fix model serving issues in the afternoon.
Advantages:
- Maximum flexibility
- Deep understanding across platform components
- Fast iteration
- No coordination overhead
Risks:
- Platform components can feel inconsistent
- Hard to maintain deep expertise in all areas
- Burnout from breadth
How to manage:
- Rotate ownership of different components
- Ensure every engineer understands the full platform
- Document decisions and patterns early
Growth Stage (5-8 Engineers): Component Teams
As the team grows, organize around platform components while maintaining cross-team collaboration.
Option A: Component-Based Teams
- Model Serving Team: Inference infrastructure, optimization
- Feature Store Team: Online/offline serving, data quality
- MLOps Team: CI/CD, deployment pipelines, tooling
- Observability Team: Monitoring, alerting, dashboards
Option B: Product-Based Teams
- Core Platform Team: Foundation infrastructure
- Developer Experience Team: APIs, SDKs, documentation
- Operations Team: Reliability, on-call, incident response
Option C: Hybrid (Recommended)
- Core Platform Team: Foundation (registry, serving, features)
- Product Team: Developer experience and adoption
- Operations Team: Reliability and support
Why hybrid is usually better: Platform teams need both infrastructure depth and product thinking. Separating these concerns allows specialization while maintaining collaboration.
Coordination Mechanisms
Regardless of structure, ML platforms need coordination:
- Platform Design Reviews: Weekly review of new components and APIs
- User Feedback Sessions: Regular sessions with ML practitioners using the platform
- Adoption Metrics: Track platform usage, deployment velocity, and user satisfaction
- Shared Standards: Consistent APIs, naming conventions, and patterns across components
Technical Considerations
Model Serving Architecture
Real-Time vs Batch Serving
Most organizations need both:
- Real-Time: REST APIs, gRPC endpoints for low-latency predictions
- Batch: Scheduled jobs for bulk predictions, ETL pipelines
Your platform should support both patterns. Engineers need experience with both.
Serving Framework Choices
- TensorFlow Serving: Mature, production-ready, TensorFlow-focused
- TorchServe: PyTorch-native, good for PyTorch models
- Triton Inference Server: Framework-agnostic, GPU-optimized
- Custom: Maximum flexibility, highest maintenance burden
Recommendation: Start with a standard framework (TensorFlow Serving or Triton), customize only when necessary.
Feature Store Architecture
Online vs Offline Stores
- Online Store: Low-latency feature serving for real-time inference (Redis, DynamoDB, custom)
- Offline Store: Historical features for training (data warehouse, data lake)
Most feature stores need both. Engineers should understand the trade-offs.
Feature Store Patterns
- Computed Features: Features computed on-demand vs pre-computed
- Feature Versioning: How to version features without breaking models
- Feature Discovery: How ML practitioners find and reuse features
- Data Quality: Ensuring feature quality across training and serving
Experiment Tracking and Model Registry
What to Track
- Experiments: hyperparameters, metrics, code versions, data versions
- Models: artifacts, metadata, performance metrics, lineage
- Deployments: which models are deployed where, A/B test configurations
Registry Design
- Model versioning strategy (semantic versioning vs incremental)
- Metadata schema (what information to store about models)
- Access control (who can deploy, who can view)
- Lineage tracking (which data and features produced which models)
Monitoring and Observability
ML-Specific Monitoring
Beyond standard application monitoring, ML systems need:
- Model Performance: Prediction accuracy, drift detection
- Data Quality: Feature distributions, missing values, outliers
- Prediction Distributions: How predictions change over time
- Cost Tracking: Compute costs per model, per prediction
- Latency Monitoring: P50, P95, P99 latency for inference
Alerting Strategy
- What to alert on (accuracy degradation, latency spikes, cost anomalies)
- How to alert (thresholds, anomaly detection, manual triggers)
- Who gets alerted (on-call rotation, model owners, platform team)
Common Pitfalls
1. Building Platform Before Understanding Needs
The mistake: Building a comprehensive ML platform before understanding what ML practitioners actually need.
What happens: Platform engineers build features nobody uses. ML practitioners work around the platform. Platform becomes expensive infrastructure with low adoption.
Better approach: Start with one or two critical capabilities (e.g., model serving and experiment tracking). Learn from usage. Expand based on actual needs, not theoretical requirements.
2. Over-Engineering the Platform
The mistake: Building a platform that handles every possible ML use case from day one.
What happens: Platform is too complex, too slow to evolve, and too hard to use. ML practitioners prefer simpler, custom solutions.
Better approach: Build the minimum platform that solves real problems. Add complexity only when necessary. Prefer simple, composable primitives over monolithic solutions.
3. Ignoring Developer Experience
The mistake: Building platform infrastructure without considering how ML practitioners will use it.
What happens: Platform has powerful capabilities but terrible APIs, unclear documentation, and confusing error messages. Low adoption despite technical excellence.
Better approach: Treat platform as a product. Invest in documentation, SDKs, examples, and developer support. Measure developer experience metrics (time to first deployment, support tickets, adoption rates).
4. Underestimating Operational Burden
The mistake: Planning for platform building, not platform operating.
What happens: Platform engineers spend 80% of time on support, debugging, and maintenance. Platform development slows. Team burnout.
Better approach: Budget engineering time for operations: on-call, support, debugging, optimization. A realistic ratio is 60% building, 40% operating for mature platforms. Build self-service capabilities to reduce support burden.
5. Hiring Only Infrastructure Engineers
The mistake: Hiring engineers who understand infrastructure but not ML systems.
What happens: Platform is technically sound but doesn't solve ML-specific problems. ML practitioners can't use it effectively.
Better approach: Hire engineers with ML systems experience. They understand model serving, feature stores, experiment tracking, and ML operations. Infrastructure skills are necessary but not sufficient.
6. Neglecting Cost Management
The mistake: Building platform without considering compute costs.
What happens: Platform enables ML development but costs explode. Models deployed through platform are expensive to run. Cost becomes a blocker for ML adoption.
Better approach: Build cost awareness into platform from day one. Track costs per model, per prediction. Provide cost visibility to ML practitioners. Optimize for cost efficiency, not just functionality.
ML Platform-Specific Interview Questions
When interviewing engineers for ML platform roles, assess their understanding of ML systems and platform thinking:
"How would you design a model serving system that supports A/B testing and canary deployments?"
Good answers consider:
- Model versioning and registry integration
- Traffic routing and gradual rollout
- Metrics collection for comparison
- Rollback mechanisms
- Latency and cost implications
"A data scientist complains that features used in training don't match features available at serving time. How would you solve this?"
Good answers explore:
- Feature store architecture (online vs offline)
- Training-serving skew detection
- Feature versioning strategies
- Data quality monitoring
- Documentation and discovery
"How would you measure the success of an ML platform?"
Good answers mention:
- Developer velocity (time to deploy, deployment frequency)
- Platform adoption (models deployed, active users)
- Reliability (uptime, error rates, rollback frequency)
- Cost efficiency (cost per model, cost per prediction)
- Developer satisfaction (surveys, support ticket volume)
"Walk me through how you'd handle a production model that's experiencing performance degradation."
Good answers cover:
- Monitoring and alerting
- Root cause analysis (data drift, model staleness, infrastructure issues)
- Rollback procedures
- Communication with stakeholders
- Post-incident improvements
"How do you balance platform standardization with ML practitioner flexibility?"
Good answers discuss:
- Core primitives vs custom solutions
- When to enforce standards vs allow exceptions
- Developer experience vs governance
- Platform evolution based on user feedback