Aidena

— AI STACK RECOMMENDATION

AI Predictive Maintenance for Industrial IoT

End-to-end system for ingesting IoT sensor data, building ML models for equipment failure prediction, and deploying real-time monitoring agents with observability.

Stays alive for 365 days after the last visit.

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AI Predictive Maintenance for Industrial IoT

End-to-end system for ingesting IoT sensor data, building ML models for equipment failure prediction, and deploying real-time monitoring agents with observability.

high confidence

Core Stack ℹ︎

Airbyte

Primary

Ingest sensor data from industrial IoT sources into data warehouse at scale. 300+ connectors support MQTT, REST APIs, and databases. Essential for centralizing training data and real-time streaming.

$0-$500/month

Dagster

Primary

Orchestrate ML pipelines: data ingestion, feature engineering, model training, and inference scheduling. Asset-based approach tracks data lineage and model dependencies for reproducible predictions.

$0-$300/month

BentoML

Primary

Package and deploy predictive models as scalable REST APIs. Auto-scaling, GPU support, and containerization enable real-time inference on incoming sensor streams without infrastructure overhead.

$0-$200/month

Complete the Stack ℹ︎

AgentOps

Alternative

Monitor prediction quality, latency, and cost of inference agents. Session replay and LLM call tracking help debug anomalies in maintenance predictions and optimize model performance.

$0-$300/month

Arize Phoenix

Alternative

Open-source observability for model predictions. Detect data drift in sensor inputs, monitor prediction accuracy over time, and evaluate model performance against ground truth maintenance events.

$0/month

Cloudflare Workers

Alternative

Edge deployment for lightweight inference and real-time alerting. Reduce latency for time-sensitive maintenance predictions and distribute inference globally across industrial sites.

$0-$200/month

Getting started

  1. 1Set up Airbyte to connect IoT data sources (MQTT brokers, REST APIs, databases) to a cloud data warehouse (Snowflake, BigQuery, or Postgres).
  2. 2Use Dagster to define asset pipelines: raw sensor data → feature engineering (rolling averages, anomaly flags) → training dataset.
  3. 3Train predictive models (XGBoost, LSTM) offline in Dagster jobs, versioning models with DVC.
  4. 4Package trained models as BentoML services with REST endpoints for real-time inference.
  5. 5Deploy BentoML to cloud (AWS, GCP, Azure) with auto-scaling for concurrent sensor streams.
  6. 6Integrate AgentOps to monitor prediction latency and cost per inference.
  7. 7Use Arize Phoenix to track model drift and evaluate prediction accuracy against actual maintenance events.
  8. 8Optional: Deploy edge inference on Cloudflare Workers for ultra-low-latency alerts at remote sites.
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