Skip Docker and Kubernetes. Deploy Streamlit apps with one click. Run notebooks on data changes. Add AI guardrails with SQL.
What Product & Data Science Teams Face
Complex deployment, no production observability, manual model updates, and data access bottlenecks.
Deployment Complexity
Build Streamlit app. Need to write Dockerfile, K8s manifest, CI/CD pipeline. Debug networking, DNS, SSL. 2 weeks to production—competitor shipped 3 features.
No Prod Observability
ML model in production. User reports wrong predictions. No logs, no error tracking. Spend 4 hours reproducing issue locally—cannot debug prod behavior.
Manual Model Updates
Retrain model weekly via cron job. Source data changes daily, model stale by Wednesday. Predictions degrade, user complaints increase—no event-driven retraining.
Data Access Friction
Need customer usage data for feature analysis. Submit ticket to Analytics, wait 3 days for access. Competitor ships feature while waiting for data.
No AI Guardrails
Deploy GPT-4 feature. No cost limits, no PII checks. A user can ask for sensitive data like all SSNs and the model will happily return it. Security incident, feature rolled back.
Notebook Reproducibility
Data Scientist shares notebook with Product Manager. It works locally but fails on the PM laptop (missing packages, different data). Long email threads to debug.
How Fastero Transforms Your Workflow
One-click deployment, event-driven execution, and AI guardrails—zero infrastructure management.
❌ Before (DevOps Hell)
Write Dockerfile, K8s manifest
Debug networking, DNS issues
2 weeks to production
✅ After (One Click)
Write Streamlit app.py
Click Deploy
Live in 30 seconds
❌ Before (Manual)
Build Streamlit app locally. Write Dockerfile, K8s manifest. Push to git, CI/CD runs. Debug deployment failures (port conflicts, DNS). 2 weeks from code to production URL.
✅ After (With Fastero)
Write app.py in Fastero browser IDE and click Deploy. Fastero handles containers, networking, SSL, and custom domain. Live URL in under a minute—share with stakeholders immediately.
❌ Before (Manual)
Retrain ML model via weekly cron job. Source data changes daily—model stale mid-week. Predictions degrade, user complaints increase. Manually monitor model performance.
✅ After (With Fastero)
CDC detects training data change (new rows, schema update). Triggers notebook: retrain model, validate metrics, deploy if improved. Model always current—zero manual monitoring.
❌ Before (Manual)
Deploy GPT-4 feature. A user can ask for customer emails with credit card numbers. No guardrails—model returns PII. Security incident, feature rolled back, audit process.
✅ After (With Fastero)
Define guardrail policy in Fastero to block queries that look like PII extraction (credit cards, SSNs, passwords). AI requests that match are blocked before they reach the model, with a clear explanation.
Features Built for Product & Data Science
One-click deployment, event-driven notebooks, AI guardrails, and self-service data access—no DevOps required.
One-Click Streamlit Deploy
Write app.py in browser IDE, click Deploy. Fastero handles containers, SSL, custom domains. App updates automatically when data changes—no manual refresh.
Event-Driven Notebooks
Trigger Jupyter notebook on data change (CDC), not cron schedule. Training data updates → retrain model. Feature table refreshes → regenerate predictions.
AI Guardrails
SQL policies block risky AI queries (PII extraction, expensive models). Requests fail pre-execution with explanation—zero PII leaks, controlled costs.
NL2SQL for Self-Service
A Product Manager can ask for DAU by cohort in plain English. NL2SQL generates SQL, runs the query, and returns a chart—no ticket to Analytics.
Hosted Jupyter Notebooks
Browser-based Jupyter with package management and git integration. Share notebooks via URL—anyone opens them and sees the same environment and data.
Production Observability
Every notebook run logged (input data, output, errors). Streamlit app errors captured with stack traces. Debug prod issues in minutes, not hours.
What changes with Fastero for your team
Ship experiments and internal tools in days instead of waiting on infra tickets.
No Kubernetes, Docker, or CI/CD pipelines for notebooks and Streamlit apps.
Models can retrain in response to data changes instead of only on a cron.
Guardrails reduce the risk of AI features returning sensitive data.
Integrates with Your Stack
Connect to notebooks, git repos, and ML tools you already use.
Jupyter
Hosted notebooks with package management
Streamlit
One-click deploy with auto-updates
Python
pandas, scikit-learn, PyTorch, TensorFlow
GitHub
Import notebooks from git repos
A Day in the Life with Fastero
Product Manager asks for user segmentation analysis
PM uses NL2SQL to request users by signup cohort and retention rate in plain language. The system generates SQL, runs it, and returns a chart in about a minute—no ticket to Analytics.
Build churn prediction dashboard
Write Streamlit app (load churn model, display predictions). Click Deploy—app live with custom domain in 30 seconds. Share URL with CS team for feedback.
Training data updated (new user signups)
CDC detects new rows in training table. Triggers notebook: retrain churn model, validate AUC (must be > 0.8), redeploy if improved. Model updated automatically.
User reports wrong prediction in dashboard
Check audit logs: see exact input data, model version, prediction, timestamp. Reproduce issue in 5 minutes—bug in data preprocessing, not model. Fix and redeploy.
Deploy GPT-4 feature for customer support
Add an AI guardrail policy that blocks queries asking for passwords, SSNs, or credit cards. Test with a request for user passwords—the query is blocked with an explanation, and you can ship the feature with more confidence.
Common Questions
How does one-click Streamlit deploy work?
Write app.py in browser IDE (Fastero Workbench). Click Deploy. Fastero builds a container, deploys it to a managed cluster, and configures SSL + custom domain. The app gets a public URL without you having to touch Docker or Kubernetes.
Can I use my own Python packages?
Yes—Jupyter notebooks support pip install (install in a cell). Streamlit apps use requirements.txt (Fastero installs packages on deploy). Common packages like pandas and numpy are pre-installed.
How do AI guardrails work?
Define a policy in SQL that flags requests which look like PII extraction (for example, containing credit card or SSN patterns). Fastero checks the policy before sending anything to GPT-4. If it matches, the request is blocked with an explanation, so sensitive data is not exposed by the model.
What triggers notebook execution?
CDC on training data (new rows, schema change), cron schedule (daily 3am), manual trigger (click Run), or webhook (external system calls API). Notebook runs in managed Jupyter environment—output logged for debugging.
Can I version control notebooks?
Yes—notebooks stored in git (GitHub, GitLab). Import notebook from repo (git clone), make changes, commit/push from Fastero UI. Diff tool shows changes between versions (cell-by-cell diff).
How is this different from Streamlit Cloud?
Streamlit Cloud focuses on GitHub-based deploys. Fastero lets you write code in the UI and click Deploy, and can refresh apps when data changes. Fastero also adds enterprise features like RBAC and SSO for internal and customer-facing apps.
Ready to ship more experiments to production?
One-click Streamlit deploy. Event-driven notebooks. AI guardrails. Skip the infra work and focus on building features.