Inside the Distributed Data & AI Systems Field Manual
A preview of the frameworks, runbooks, and blueprints launching with the Digiteria Labs eBook.
We built the Distributed Data & AI Systems Field Manual because every week founders asked the same question: How do we deliver AI programs fast without breaking reliability, compliance, or budgets?
What’s in the Manual
- 12 chapters covering ingestion, lakehouse design, orchestration, MLOps, low-latency serving, observability, security, and go-live playbooks.
- 50+ checklists and runbooks including backfill safety, incident response, FinOps scorecards, and 30-day implementation plans.
- Four production blueprints that show architectures, configs, and failure drills for fraud detection, self-service data platforms, feature serving, and batch→streaming migrations.
Built from Real Engagements
This isn’t a theory book. The manual distills lessons from Digiteria Labs partnerships with fintech, marketplace, and SaaS teams navigating compliance-heavy launches. Every framework comes with:
- Decision matrices that map tools (Databricks, Snowflake, BigQuery, dbt, Airflow, Dagster) to use cases.
- Cost models showing real monthly spend and optimization levers.
- Operational checklists tied to metrics like MTTR, p99 latency, and revenue impact.
Launch Bonuses
Pre-order buyers get:
- Stripe checkout delivery of PDF + EPUB + Notion companion workspace.
- ConvertKit email series walking through diagrams and implementation prompts.
- Bonus artifacts: production readiness checklist, p99 latency reduction playbook, and compliance audit prep kit.
Ready to level up your platform? Join the waitlist or join the waitlist for early access updates.
