MLOps Tools for AI Deployment: From Training to Production

Building AI models is only half the battle – deploying and managing them in production is where the real challenges begin. The MLOps ecosystem has matured significantly, offering robust open source tools for every stage of the deployment lifecycle.

What is MLOps?

MLOps (Machine Learning Operations) brings DevOps practices to machine learning:

• Version Control: Track models, data, and experiments
• CI/CD: Automate testing and deployment
• Monitoring: Track model performance in production
• Scaling: Handle varying inference loads
• Governance: Ensure compliance and reproducibility

The ML Lifecycle

┌──────────────────────────────────────────────────┐
│              ML Lifecycle                        │
├──────────────────────────────────────────────────┤
│  Development                                     │
│  ├── Experiment tracking                         │
│  ├── Model training                              │
│  └── Evaluation                                  │
├──────────────────────────────────────────────────┤
│  Deployment                                      │
│  ├── Model packaging                             │
│  ├── Serving infrastructure                      │
│  └── API endpoints                               │
├──────────────────────────────────────────────────┤
│  Operations                                      │
│  ├── Monitoring                                  │
│  ├── Scaling                                     │
│  └── Updates                                     │
└──────────────────────────────────────────────────┘

Experiment Tracking

MLflow

Key Features:

• Experiment tracking
• Model registry
• Deployment tools
• Project packaging

Weights & Biases

Key Features:

• Rich visualizations
• Team collaboration
• Hyperparameter sweeps
• Report generation

Data Version Control

DVC

Key Features:

• Data versioning
• Pipeline tracking
• Remote storage
• Git integration

Model Serving

vLLM

Performance:

• 24x higher throughput than naive serving
• Efficient memory management
• OpenAI-compatible API

Triton Inference Server

Key Features:

• Multi-framework support
• Dynamic batching
• Model ensembles
• GPU optimization

LocalAI

Best for: Drop-in OpenAI replacement

BentoML

Key Features:

• Model packaging
• API generation
• Containerization
• Scaling

LLM-Specific Operations

LiteLLM

Use Cases:

• Multi-provider routing
• Cost optimization
• Fallback handling

Axolotl

Unsloth

Distributed Training

Ray

Components:

• Ray Train: Distributed training
• Ray Serve: Model serving
• Ray Data: Data processing

DeepSpeed

Capabilities:

• Memory optimization
• Pipeline parallelism
• Mixture of Experts
• Inference optimization

Application Platforms

Gradio

Streamlit

Data Labeling

Label Studio

Supported Types:

• Images
• Text
• Audio
• Video
• Time series

Deployment Patterns

Pattern 1: Direct Serving

Simple models served via REST API:

Client → Load Balancer → Model Server → Response

Pattern 2: Queue-Based

Async processing for heavy workloads:

Client → Queue → Workers → Results Store → Client

Pattern 3: Streaming

Real-time token generation:

Client ← SSE/WebSocket ← Model Server

Pattern 4: Batch

Periodic processing of accumulated requests:

Data → Scheduler → Batch Job → Results

Best Practices

1. Containerize Everything

Use Docker for consistent environments across development and production.

2. Monitor Proactively

Track latency, throughput, errors, and model-specific metrics.

3. Plan for Rollback

Keep previous model versions deployable at all times.

4. Implement Canary Deployments

Test new models on a subset of traffic before full rollout.

5. Cache Strategically

Cache embeddings, frequent queries, and static computations.

Cost Optimization

• Right-size instances: Match GPU to model requirements
• Spot instances: Use for training and batch inference
• Quantization: Deploy INT8 or INT4 models when possible
• Batching: Maximize GPU utilization with smart batching
• Autoscaling: Scale down during low-traffic periods

Conclusion

MLOps tooling has evolved to handle the unique challenges of AI systems. From experiment tracking with MLflow to high-performance serving with vLLM, open source solutions now cover the entire lifecycle.

Explore our MLOps & Infrastructure category to discover more tools for deploying and managing AI at scale.