π Banner 301/367
π§ MLOps | βοΈ Automation | π SQL | π Data Science | π Experiment Tracking | π Tableau
β‘ How it works (architecture deep-dive π¬ for engineers)
This profile is a self-updating MLOps demo β a living portfolio showcasing production-grade automation.
- π€ Banner rotation: 367 GIFs Β· natural sorting Β· cache-busted CDN URLs
- π§© Dynamic insights: Context-aware NLG (time/season/DOW algorithms)
- β±οΈ Next Update badge: Shields.io endpoint Β· HLS gradient Β· sub-minute precision
- π‘ Observability: JSONL telemetry Β· heartbeat pings Β· state persistence
- βοΈ Zero-touch ops: 5475+ runs Β· 133 mutations Β· idempotent commits
| File | Version | Description |
|---|---|---|
| update_readme.py | Banner engine + NLG + JSONL pipeline | |
| build_next_badge.py | HLS gradient renderer + countdown |
| Workflow | Schedule | Status |
|---|---|---|
| Auto Update README | Daily 12:15 UTC | |
| Next Update Badge | Every 20min | |
| CI/CD Pipeline | On push/PR |
π View all runs β
.
ββ update_log.jsonl # CI run timeline (1 JSON per run: ts_utc, run_id, run_number, sha, banner_*, insight_*)
ββ update_log.txt # Grep-friendly mirror of update_log.jsonl (ts UTC, run=β¦, sha=β¦; rolling tail)
ββ badges/
β ββ next_update.json # Live Shields.io badge state (label, message like '~14h 35m', color bucket)
β ββ next_update_log.jsonl # Badge countdown snapshots (ts, next_utc, minutes_left, message, color, jitter params)
β ββ next_update_log.txt # Human-readable badge ETA tail ([ts] color=β¦ msg='β¦' next_utc=β¦ mins_left=β¦)
β ββ github_followers.json # Endpoint payload for the Followers badge (schemaVersion/label/message/color)
β ββ github_stars.json # Endpoint payload for the Stars badge
β ββ total_updates.json # Endpoint payload for the Updates badge
ββ .ci/
ββ heartbeat.log # GitHub Actions heartbeat ledger (Updated on / Triggered by / Commit SHA / Run ID / Run number)
ββ update_count.txt # Monotonic mutation counter (powers the Β«N mutations shippedΒ» tagline)
π Browse logs:
π update_log.jsonl Β·
π update_log.txt Β·
π heartbeat.log Β·
π’ update_count.txt
β±οΈ next_update.json Β·
π‘ next_update_log.jsonl Β·
π next_update_log.txt
π₯ github_followers.json Β·
β github_stars.json Β·
π total_updates.json
Now
- βοΈ AWS CCP (CLF-C02) β CloudWolf (active learning)
- π§± Azure Data Engineering Projects (Udemy):
- Databricks Lakehouse (Formula1 β batch & lakehouse design)
- Data Factory pipelines (Covid19 β ingestion & orchestration)
- Azure Synapse Analytics (NYC Taxi β analytics & SQL warehouse)
Next
- π Streaming & cloud systems: Kafka / Spark / Flink / Airflow + AWS
- π§ͺ More portfolio releases: architecture diagrams, CI/CD, and production-style enhancements
Focus
- Data Engineering | MLOps | SQL | Automation
- Building cloud-native, cost-aware data platforms
π§ 2+ years of continuous, hands-on learning with a strong focus on real-world projects and automation
- π SuperDataScience β MLOps & AI specialization
- π Udemy β Data Engineering & Cloud projects
- βοΈ CloudWolf β AWS Cloud & Certification preparation
| π Platform | π Link |
|---|---|
| π§ GitHub | Evgenii Matveev |
| π Portfolio | Data Science Portfolio |
| π LinkedIn | Evgenii Matveev |
π« AI Copilot Ecosystem
| Assistant | Role | Usage |
|---|---|---|
| ChatGPT 5.2 | Core copilot for architecture & automation | Rapid execution |
| Claude Sonnet 4.5 | Contextual planner & strategic reasoner | Long-range thinking |
π‘ Note: Dual-Copilot Workflow
This ecosystem operates as a dual-copilot workflow β two AI systems working in synergy:
- ChatGPT β architecture, reasoning, documentation
- Claude Sonnet 4.5 β long-context planning & strategic alignment
This dual-copilot system balances speed and depth β
ChatGPT executes and refines Β· Claude plans and connects.
Together they form my AI-powered engineering loop for continuous innovation.
β¨ Optimized for precision, powered by automation, evolving through insight.
π€ Automation Logs
πͺ Run Meta (click to expand)
- π Updated (UTC): 2025-12-26 13:07 UTC
- π€ Run: #5586 β open run
- 𧬠Commit: e11dea6 β open commit
- β»οΈ Updates (total): 207
- π Workflow: Auto Update README Β· Job: update-readme
- β¨ Event: schedule Β· π§βπ» Actor: evgeniimatveev
- π Schedule: 24h_5m
- π Banner: 301/367
ποΈRecent updates (last 5)
| Time (UTC) | Run | SHA | Banner | Event/Actor | Insight |
|---|---|---|---|---|---|
| 2025-12-26 13:07:57 | 5586 | e11dea6 |
301/367 (301.gif) | schedule/evgeniimatveev | π‘ PIPELINES, NOT FIRE-DRILLS β’ RUN #5586 β HIBERNATE AND OPTIMIZE π§ | WRAP IT UP LIKE A PRO! β‘ TIGHTEN SLAS, WIDEN OBSERVABILITY, REβ¦ |
| 2025-12-25 13:12:06 | 5585 | 5bb3d97 |
300/367 (300.gif) | schedule/evgeniimatveev | π‘ LOW TOIL, HIGH LEVERAGE β’ RUN #5585 β Deep focus and model tuning βοΈ | Test, iterate, deploy! π Perfect time for CI/CD magic β‘ π§ |
| 2025-12-24 13:07:24 | 5584 | 4bab068 |
299/367 (299.gif) | schedule/evgeniimatveev | π‘ TRACK β’ TUNE β’ TRUST β’ RUN #5584 β Harden CI, cache models, reduce cold starts π§ | Halfway there β keep automating! π οΈ Measure β iβ¦ |
| 2025-12-23 13:05:53 | 5583 | 2eb2d7e |
298/367 (298.gif) | schedule/evgeniimatveev | π‘ PIPELINES, NOT FIRE-DRILLS β’ RUN #5583 β DEEP FOCUS AND MODEL TUNING βοΈ | KEEP UP THE MOMENTUM! π₯ GUARDRAILS ON, FEATURE FLAGS REAβ¦ |
| 2025-12-22 13:13:33 | 5582 | 9b842ec |
297/367 (297.gif) | schedule/evgeniimatveev | π‘ OBSERVE β’ ALERT β’ IMPROVE β’ RUN #5582 β DEEP WORK: SCHEMA DESIGN & CONTRACTS βοΈ | START YOUR WEEK STRONG! π GUARDRAILS ON, FEATUREβ¦ |
Night-mode palettes Β· Daily AβG theme rotation Β· Fully automated via GitHub Actions
| π§© Workflow | βοΈ Automation | π Insights |
|---|---|---|
| Design β Build β Scale | Deploy CI/CD with intelligence | Measure β Learn β Improve |
- βοΈ Automate end-to-end ML pipelines (train β eval β deploy)
- π Track experiments with MLflow & W&B; analyze runs via PostgreSQL/SQL
- π Build stakeholder dashboards in Tableau / Power BI
π€ MLOPS Insight: π‘ PIPELINES, NOT FIRE-DRILLS β’ RUN #5586 β HIBERNATE AND OPTIMIZE π§ | WRAP IT UP LIKE A PRO! β‘ TIGHTEN SLAS, WIDEN OBSERVABILITY, REDUCE TOIL π§ β






