ML engineering skill for productionizing models

Senior ML Engineer

Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration.

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Table of Contents

• Model Deployment Workflow
• MLOps Pipeline Setup
• LLM Integration Workflow
• RAG System Implementation
• Model Monitoring
• Reference Documentation
• Tools

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Model Deployment Workflow

Deploy a trained model to production with monitoring:

1. Export model to standardized format (ONNX, TorchScript, SavedModel)
2. Package model with dependencies in Docker container
3. Deploy to staging environment
4. Run integration tests against staging
5. Deploy canary (5% traffic) to production
6. Monitor latency and error rates for 1 hour
7. Promote to full production if metrics pass
8. Validation: p95 latency < 100ms, error rate < 0.1%

Container Template

FROM python:3.11-slim

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY model/ /app/model/
COPY src/ /app/src/

HEALTHCHECK CMD curl -f http://localhost:8080/health || exit 1

EXPOSE 8080
CMD ["uvicorn", "src.server:app", "--host", "0.0.0.0", "--port", "8080"]

Serving Options

Option	Latency	Throughput	Use Case
FastAPI + Uvicorn	Low	Medium	REST APIs, small models
Triton Inference Server	Very Low	Very High	GPU inference, batching
TensorFlow Serving	Low	High	TensorFlow models
TorchServe	Low	High	PyTorch models
Ray Serve	Medium	High	Complex pipelines, multi-model

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MLOps Pipeline Setup

Establish automated training and deployment:

1. Configure feature store (Feast, Tecton) for training data
2. Set up experiment tracking (MLflow, Weights & Biases)
3. Create training pipeline with hyperparameter logging
4. Register model in model registry with version metadata
5. Configure staging deployment triggered by registry events
6. Set up A/B testing infrastructure for model comparison
7. Enable drift monitoring with alerting
8. Validation: New models automatically evaluated against baseline

Feature Store Pattern

from feast import Entity, Feature, FeatureView, FileSource

user = Entity(name="user_id", value_type=ValueType.INT64)

user_features = FeatureView(
    name="user_features",
    entities=["user_id"],
    ttl=timedelta(days=1),
    features=[
        Feature(name="purchase_count_30d", dtype=ValueType.INT64),
        Feature(name="avg_order_value", dtype=ValueType.FLOAT),
    ],
    online=True,
    source=FileSource(path="data/user_features.parquet"),
)

Retraining Triggers

Trigger	Detection	Action
Scheduled	Cron (weekly/monthly)	Full retrain
Performance drop	Accuracy < threshold	Immediate retrain
Data drift	PSI > 0.2	Evaluate, then retrain
New data volume	X new samples	Incremental update

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LLM Integration Workflow

Integrate LLM APIs into production applications:

1. Create provider abstraction layer for vendor flexibility
2. Implement retry logic with exponential backoff
3. Configure fallback to secondary provider
4. Set up token counting and context truncation
5. Add response caching for repeated queries
6. Implement cost tracking per request
7. Add structured output validation with Pydantic
8. Validation: Response parses correctly, cost within budget

Provider Abstraction

from abc import ABC, abstractmethod
from tenacity import retry, stop_after_attempt, wait_exponential

class LLMProvider(ABC):
    @abstractmethod
    def complete(self, prompt: str, **kwargs) -> str:
        pass

@retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=10))
def call_llm_with_retry(provider: LLMProvider, prompt: str) -> str:
    return provider.complete(prompt)

Cost Management

Do not hardcode prices, and do not trust a price table you find in a document (including this one). Providers reprice several times a year, and a stale figure produces a confidently wrong business case.

Work in tiers and look the current numbers up at request time:

Tier	Typical use	Relative cost
Small	Classification, extraction, routing, short output	1x baseline
Mid	Summarisation, structured output, moderate reasoning	~10-25x small
Large	Multi-step reasoning, code generation, long context	~50-100x small

Read the live rate from your provider's pricing page and pass it in, the way engineering-team/skills/senior-prompt-engineer/scripts/prompt_optimizer.py takes --price-per-mtok. The ratios between tiers are far more stable than the absolute prices, so build the model-routing decision on the ratio.

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RAG System Implementation

Build retrieval-augmented generation pipeline:

1. Choose vector database (Pinecone, Qdrant, Weaviate)
2. Select embedding model based on quality/cost tradeoff
3. Implement document chunking strategy
4. Create ingestion pipeline with metadata extraction
5. Build retrieval with query embedding
6. Add reranking for relevance improvement
7. Format context and send to LLM
8. Validation: Response references retrieved context, no hallucinations

Vector Database Selection

Database	Hosting	Scale	Latency	Best For
Pinecone	Managed	High	Low	Production, managed
Qdrant	Both	High	Very Low	Performance-critical
Weaviate	Both	High	Low	Hybrid search
Chroma	Self-hosted	Medium	Low	Prototyping
pgvector	Self-hosted	Medium	Medium	Existing Postgres

Chunking Strategies

Strategy	Chunk Size	Overlap	Best For
Fixed	500-1000 tokens	50-100	General text
Sentence	3-5 sentences	1 sentence	Structured text
Semantic	Variable	Based on meaning	Research papers
Recursive	Hierarchical	Parent-child	Long documents

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Model Monitoring

Monitor production models for drift and degradation:

1. Set up latency tracking (p50, p95, p99)
2. Configure error rate alerting
3. Implement input data drift detection
4. Track prediction distribution shifts
5. Log ground truth when available
6. Compare model versions with A/B metrics
7. Set up automated retraining triggers
8. Validation: Alerts fire before user-visible degradation

Drift Detection

from scipy.stats import ks_2samp

def detect_drift(reference, current, threshold=0.05):
    statistic, p_value = ks_2samp(reference, current)
    return {
        "drift_detected": p_value < threshold,
        "ks_statistic": statistic,
        "p_value": p_value
    }

Alert Thresholds

Metric	Warning	Critical
p95 latency	> 100ms	> 200ms
Error rate	> 0.1%	> 1%
PSI (drift)	> 0.1	> 0.2
Accuracy drop	> 2%	> 5%

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Reference Documentation

MLOps Production Patterns

references/mlops_production_patterns.md contains:

• Model deployment pipeline with Kubernetes manifests
• Feature store architecture with Feast examples
• Model monitoring with drift detection code
• A/B testing infrastructure with traffic splitting
• Automated retraining pipeline with MLflow

LLM Integration Guide

references/llm_integration_guide.md contains:

• Provider abstraction layer pattern
• Retry and fallback strategies with tenacity
• Prompt engineering templates (few-shot, CoT)
• Token optimization with tiktoken
• Cost calculation and tracking

RAG System Architecture

references/rag_system_architecture.md contains:

• RAG pipeline implementation with code
• Vector database comparison and integration
• Chunking strategies (fixed, semantic, recursive)
• Embedding model selection guide
• Hybrid search and reranking patterns

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Tools

Model Deployment Pipeline

python scripts/model_deployment_pipeline.py --model model.pkl --target staging

Generates deployment artifacts: Dockerfile, Kubernetes manifests, health checks.

RAG System Builder

python scripts/rag_system_builder.py --config rag_config.yaml --analyze

Scaffolds RAG pipeline with vector store integration and retrieval logic.

ML Monitoring Suite

python scripts/ml_monitoring_suite.py --config monitoring.yaml --deploy

Sets up drift detection, alerting, and performance dashboards.

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Tech Stack

Category	Tools
ML Frameworks	PyTorch, TensorFlow, Scikit-learn, XGBoost
LLM Frameworks	LangChain, LlamaIndex, DSPy
MLOps	MLflow, Weights & Biases, Kubeflow
Data	Spark, Airflow, dbt, Kafka
Deployment	Docker, Kubernetes, Triton
Databases	PostgreSQL, BigQuery, Pinecone, Redis