Complete ML CI/CD Pipeline: Data Prep → Training (SageMaker) → Validation → Deployment (Docker+K8s) → Monitoring (Grafana)

Are You Investing in Training ML ModelsThat Never Reach Production?

87% of ML models are NEVER deployed (Forbes 2025). I help you implement CI/CD pipelines for ML that scale automatically. From Jupyter Notebook to production in 4-6 weeks.

📊 30-min Technical Demo (See Architecture)💬 WhatsApp: Deployment problem

We review your current stack • Deployment roadmap • No commitment

9 months → 4 weeks

Verified deployment time

ML-Specific CI/CD

SageMaker / Vertex / Azure / Databricks

6-Week Guarantee

Model in production or refund

AWS ML Specialty

Azure AI Engineer

Databricks Certified

MLflow Contributors

5 Signs Your ML Team Is Stuck

Sound Familiar? (You Recognize At Least 3)

ML model graveyard: model_final_v3.ipynb, REAL_final.ipynb, 87% models never reach production (Gartner 2024)

☑️

Models in Notebooks for 6+ Months

Data Scientists train excellent models. 95%+ accuracy

But... everything is in .ipynb notebooks on their laptops

Cost: Full DS team with no real production output

Frustration + Embarrassment: "Why aren't we deploying?"

☑️

DevOps Doesn't Know How to Deploy ML

They master Docker, Kubernetes, traditional CI/CD

But ML models: GPUs, versioning, A/B testing = different

Cost: 9 months of failed attempts

Team conflict + Helplessness

☑️

Zero Performance Visibility

Deployed 1 model 3 months ago. Current accuracy?

No drift monitoring, no alerts when it degrades

Cost: Model silently degrades

Fear: "Is it broken and we don't know?"

☑️

Manual Re-training

New data → Manual re-train → Manual re-deploy

2-week process. No automated pipeline

Cost: 40 hours/month DS wasted

Tedium: "There should be a better way"

☑️

Impossible A/B Testing

Want to test new model vs current one

No infrastructure. Afraid to replace the old one

Cost: Innovation paralyzed

Stuck with suboptimal model

📊 The Real Cost of NOT Having MLOps:

Investment Without Return

✗ DS team salaries producing only demos

✗ Idle infrastructure (unused production GPUs)

✗ Lost opportunities (undeployed ML features)

✗ Manual re-work on deployment attempts

= 9 months from trained model → production (if it arrives)

How long have you been stuck?

Schedule Free Audit →

The Solution: ML-Specific CI/CD Pipelines in 4-6 Weeks

From Notebooks to Production Systematically

MLOps 3-tier architecture: Development (Jupyter+DVC+Git), Training (SageMaker+Model Registry), Production (Docker+K8s+Monitoring)

STAGE 1

Training Pipeline

MLflow tracking

2 weeks

STAGE 2

Automated Testing

Validation gates

1 week

STAGE 3

CI/CD Deployment

Canary/Blue-Green

1 week

STAGE 4

24/7 Monitoring

Drift detection

Ongoing

9 months → 4 weeks

FinTech startup deployed 3 models in 6 weeks

ROI: $80k saved vs 9 months idle

Automated Monitoring

Real-time dashboard: accuracy, latency, drift detection

CTO sleeps soundly, DS knows status

Built-In A/B Testing

Deploy new model to 10% traffic, compare, auto rollback

Fearless innovation, continuous improvement

Multi-Platform Support

SageMaker, Azure ML, Vertex AI, Databricks, hybrid on-prem

Your current stack, no vendor lock-in

Want to see the specific pipeline for YOUR stack?

30-min Technical Demo →

3 Real Cases: From Notebooks to Production

4-6 Weeks Verified

Production monitoring dashboard: Model accuracy 94.2%, latency 87ms, drift detection, fraud detection confusion matrix

A/B test Model v1 vs v2: CTR 2.3%→3.8% (+65%), Conversion 0.8%→1.4% (+75%), Revenue impact +$361k annual

FinTech Startup

Fraud Detection (Series A)

Challenge:

Model 95% accuracy, stuck in staging for 6 months. DevOps doesn't know how to deploy

Before

6 months

stuck

After

5 weeks

live production

Results:

• Latency: 24h batch → <50ms real-time
• False positives: -40%
• $450k fraud prevented year 1

"From models in notebooks to production API in 5 weeks. Now we iterate weekly."

— CTO FinTech

Stack: AWS SageMaker, MLflow, TensorFlow

Pricing: From $12k

ROI: $430k net year 1

E-commerce

Product Recommendations

Challenge:

Monthly manual re-training, 2-week process, no A/B testing

Before

2 weeks

manual deploy

After

2 hours

automated

Results:

• Re-training: Monthly → Weekly auto
• CTR recommendations: Significant improvement
• Revenue: Verified substantial increase

"Deploying new model was a nightmare. Now we commit code and it's live in 2h with canary."

— VP Engineering

Stack: Vertex AI, Kubeflow, PyTorch

Pricing: From $10k

ROI: $310k net year 1

HealthTech

Predictive Diagnostics (HIPAA)

Challenge:

On-prem model, HIPAA compliance blocking cloud deployment

Before

On-prem

1 hospital

After

Cloud HA

12 hospitals

Results:

• Latency: Drastic reduction with cloud auto-scaling
• Availability: 98.5% → 99.95%
• Revenue: Verified substantial growth

"Afraid of cloud due to HIPAA. BCloud implemented compliant Azure ML. Game changer."

— CTO HealthTech

Stack: Azure ML, MLflow, HIPAA

Pricing: From $18k

ROI: $362k net year 1

🚀

Multiple Production Pipelines

⚡

Implementation in Weeks

✅

Track Record Without Critical Incidents

☁️

Multi-Cloud Expertise

Pipeline stages: Data prep DVC code, SageMaker training, MLflow metrics, kubectl deploy, Grafana alert monitoring

ML deployment comparison: Batch (minutes-hours), Real-time (<100ms), Edge (<10ms) with use cases and complexity

Full Grafana dashboard: Model accuracy/prediction volume/error rate, performance over time, drift detection, latency distribution

Technical Guarantees: If We Don't Deploy, You Don't Pay

25+ MLOps projects. I've NEVER missed a deadline.

🎯

First Model Production in 6 Weeks

If your first model isn't in production in 6 weeks, I work for free until we achieve it.

Track Record: Projects consistently completed within agreed timeline

💎

Complete Knowledge Transfer

Exhaustive documentation + training. Your team autonomous post-project.

Deliverables: 80-100 page runbook, video tutorials, source code.

Technical FAQs

Does it work with our current stack (SageMaker/Vertex/Azure/Databricks)?

Yes. We support AWS SageMaker (40% of projects), Google Vertex AI (25%), Azure ML (20%), Databricks (15%). Also custom Kubernetes (Kubeflow), hybrid on-prem GPUs + cloud. Day 1 audit identifies best fit for YOUR case.

Do we need to change our existing Data Science code?

Minimal. Typical changes: add MLflow logging (3-5 lines), parameterize training script, Dockerfile. NOT required: model rewrite, framework change (TensorFlow→PyTorch), codebase refactor. Adaptation time: 4-8 hours Data Scientist.

How much time does my team need to dedicate during implementation?

Minimal. Total: ~25-30 hours over 4-6 weeks. Breakdown: Kick-off 4h, Reviews 12h, Training 10h, Ad-hoc 4h. TOTAL: 30h over 6 weeks = 5h/week average (1h/day team). I do the heavy lifting.