Available Now · 30+ Production Models

Machine Learning App Development Company

Predictive Models · MLOps · Recommendation Engines

Miracuves is a machine learning development company that builds production ML systems — predictive models, recommendation engines, fraud detection, and demand forecasting — with TensorFlow, PyTorch, scikit-learn, and full MLOps. You get trained models, inference APIs, monitoring dashboards, and complete source code ownership.

100+ ML Systems 95% Model Accuracy 100% IP Ownership NDA Day One
WhatsApp — Talk to Our Team Explore ML Solutions
Clutch Reviewed 4.9★ · Starting from $3,699 · View ML deployments
Miracuves Delivery RecordML Engineering
6–12 Weeks
Delivery timeline
$3,699
Starting price
30+
ML Models in Production
95%
Avg Model Accuracy
ML engineers active right now

E-commerce ML

Recommendation Engine

Deployed in 8 weeks · From $12,999

Manufacturing

Predictive Maintenance

Deployed in 8 weeks · 60% downtime cut
ML Pipeline Console ACTIVE
FRAMEWORK TensorFlow / PyTorch
EXPERIMENT TRACKING MLflow + Kubeflow
VALIDATION Holdout + Drift Checks
DEPLOYMENT K8s / SageMaker / Edge
30+ ML EngineersSpecialized in AI/ML development
25+ ML ModelsIn Production
MLOps StandardMLflow + Kubeflow on every project
TensorFlow+PyTorchCertified Engineers
95% Model AccuracyAvg across deployed systems

White-Label Ready

Fully rebrandable on delivery

NDA Day One

IP protected first call

Full Source Code

Delivered at handoff

60-Day Support

Post-launch included

100% IP Ownership

Yours — always

Clutch Reviewed 4.9★

Third-party verified
Our ML Approach

How Miracuves delivers machine learning systems — from 30+ production deployments

After deploying 30+ production ML systems across manufacturing, fintech, and e-commerce, Miracuves has a specific methodology for machine learning delivery. We start from proven pipeline modules — data ingestion, feature engineering, model training, validation, deployment, and monitoring — not from an untracked Jupyter notebook.

ML pipelines deliver consistent, reproducible results from a unified codebase. For production deployments, this eliminates the gap between data science experimentation and engineering — one pipeline, automated retraining, full model artifacts yours on handoff.

Who this service is built for: Product leaders, data teams, and enterprises that need predictive models in production — recommendation engines, fraud scoring, demand forecasting, churn prediction, predictive maintenance, or computer vision QC. Miracuves ML development fits when you have (or can collect) labeled data, need models served via API with monitoring, and want a company accountable for MLOps — not a one-off notebook. If your problem is better solved with rules or a simple dashboard, we say so upfront.

End-to-end ML pipeline — data ingestion, feature engineering, model training, deployment, monitoring
TensorFlow and PyTorch as primary frameworks — scikit-learn for classical ML, MLflow for experiment tracking
Kubeflow orchestration for distributed training and automated model retraining pipelines
GPU-accelerated training on AWS SageMaker with automated hyperparameter tuning
Production monitoring with Grafana and Prometheus — drift detection, accuracy tracking, automated alerts

From our ML team — Factory predictive maintenance, 8 weeks

"500 machines, 2TB of sensor data monthly, and zero visibility into failures until production stopped. Miracuves built a TensorFlow LSTM pipeline with MQTT ingestion, automated retraining every 24 hours, and a Grafana dashboard our maintenance team actually uses. Downtime dropped 60% in Q1 — the system paid for itself in four months."

Written by the Miracuves ML Engineering Team · May 2026 · View Deployed Portfolio →
30+
Production ML systems deployed
60%
Avg downtime reduction (predictive maintenance)
94%
Peak model accuracy on monitored projects
6–12
Weeks from brief to production API
$3,699
Published starting investment anchor
100%
Model artifacts + code ownership
Training
Model fitting
Inference
Prediction
Pipelines
MLOps workflows

Why ML at Miracuves

Time to first production model6–12 weeks
Experiment reproducibilityMLflow on every run
Inference deploymentAPI · Batch · Edge
Monitoring includedDrift + accuracy alerts
Framework flexibilityTensorFlow · PyTorch · sklearn
Source code ownership100% yours
30+ ML Systems Deployed

What Miracuves builds — production ML systems you can ship

Discuss Your ML Use Case →
8 Weeks01

E-commerce

Recommendation Engine

Collaborative filtering and real-time personalization with A/B testing, feature store, and inference API.

From $12,999PyTorchReal-time
8 Weeks02

Manufacturing

Predictive Maintenance

IoT sensor ingestion, LSTM anomaly detection, Grafana alerts — failures predicted 48–72 hours ahead.

From $18,999TensorFlowIoT + MQTT
10 Weeks03

Fintech

Fraud Detection System

Real-time transaction scoring, ensemble models, sub-200ms inference, and analyst review dashboards.

From $22,999PyTorch<200ms
6 Weeks04

Retail

Demand Forecasting

Time-series models for SKU-level inventory planning with seasonality, promotions, and external signals.

From $14,999scikit-learnProphet
6 Weeks05

SaaS

Churn Prediction

Behavioral feature engineering, gradient boosting classifiers, and CRM-integrated risk scores for retention teams.

From $11,999XGBoostCRM hooks
12 Weeks06

Manufacturing

Computer Vision QC

Defect detection on production lines with CNN models, edge deployment via TensorFlow Lite, and audit trails.

From $24,999TensorFlowEdge + Cloud
Honest note: Machine learning is transformative for data-rich use cases — recommendation, prediction, classification, and anomaly detection. For simple rule-based automation or applications with insufficient training data, Miracuves may recommend a non-ML approach. We tell you which fits before any commitment.
Technology Comparison

Custom ML pipeline vs AutoML vs in-house — which fits your project?

Most development companies avoid this question because they only know one stack. Miracuves answers it honestly — your technology choice determines long-term cost, performance, and maintenance.

Metric Miracuves ML · Custom Pipeline
← MIRACUVES DEFAULT		 AutoML Platform DIY Data Science
Model Accuracy 95% target — custom tuned per use case 70–85% — generic pre-built models Variable — depends on team expertise
Pipeline Control Full — data ingestion to monitoring Limited — platform-provided pipeline only Full — but requires DevOps setup
Time to Production 6–12 weeks — full pipeline deployed Fast — pre-built templates Slow — build everything from scratch
Customization Full — custom architecture, custom models Constrained — platform model zoo only Full — unlimited, requires expertise
Best For Custom ML · production systems · full ownership Quick prototyping · standard use cases Research · in-house ML teams

Choose Miracuves ML if…

You need custom models · end-to-end pipeline ownership · production-grade MLOps · a team accountable for model performance in production.

Consider an alternative if…

You only need a no-code AutoML dashboard · your team already has senior ML engineers and MLOps · or you lack sufficient labeled data and should start with data collection first. See AI Development →

Technical Architecture

How Miracuves engineers structure ML projects for production

These are the specific decisions our ML engineering team makes on every project — choices that determine whether a model scales in production or becomes a notebook that cannot be operationalized.

Architecture — Modular ML Pipeline

Strict separation: Data Ingestion → Feature Engineering → Model Training → Model Evaluation → Deployment → Monitoring. Every stage is independently deployable and testable. This is how Miracuves delivers production ML systems that can be retrained and redeployed without pipeline disruption.

Experimentation — MLflow for Tracking, Kubeflow for Pipelines

MLflow tracks every experiment with full parameter, metric, and artifact logging. Kubeflow orchestrates the end-to-end ML pipeline — from data validation through model deployment. The most common problem inherited from other teams: untracked experiments in Jupyter notebooks with no reproducibility. We eliminate this on day one.

Performance — GPU Training with Distributed Computing

All model training runs on GPU instances with automatic distributed computing for large datasets. We profile every training run with TensorBoard — CPU-only training is used for initial prototyping, never for production model training.

What most Machine Learning agencies get wrong

Untracked Jupyter notebooks with no reproducibility. Training on production data without holdout validation. Models deployed without monitoring or drift detection. No versioned artifacts — impossible to roll back. Miracuves has inherited every one of these — starting with MLOps discipline is always faster than cleaning up.

train_pipeline.py — sklearn ML Pipeline
# Production ML pipeline with scikit-learn # Used in recommendation + prediction products from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split def build_ml_pipeline(X, y): # Feature engineering + model training pipe = Pipeline([ ('scaler', StandardScaler()), ('classifier', RandomForestClassifier( n_estimators=100, max_depth=10, random_state=42 )) ]) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) pipe.fit(X_train, y_train) return pipe, (X_test, y_test)
Calls scikit-learn for model training with full pipeline encapsulation. Feature scaling, model fitting, and train/test split in a single deployable artifact. Used in every ML product Miracuves ships.
Our Service Models

Three ways Miracuves delivers your ML project

Every engagement is with Miracuves as a company — a complete team, a defined process, and full delivery accountability. Choose the model that matches your project stage.

Most Popular
Data Ingest
Model API
MLOps Dash

Fixed Scope · Fixed Price

ML Solution Package

Miracuves deploys a scoped ML module — data pipeline, trained model, inference API, and monitoring dashboard — in 6–12 weeks. Artifacts fully yours.

Starting from $3,699 — published anchor price
Use-case templates: recommendation, fraud, forecasting, churn
Data pipeline, training, deployment, monitoring included
MLflow experiment tracking and model registry
Full source code · model artifacts · NDA · 60-day support
MLPipeline FeatureStore Trainer InferenceAPI ETL MLflow Monitor

Custom Development · Full Pipeline

Custom ML Development

Miracuves builds from your specification — custom architecture, custom flows, unique features. Full team: engineer, backend, QA, PM.

Scoped and priced before development begins
Custom pipeline designed specifically for your data
Weekly sprint demos — working model every sprint
Model deployment and API endpoint management
Full source code · model weights · IP 100% yours
Wk 1
Wk 2
Wk 3
Wk 4

ML Retainer · Monthly

Ongoing ML Development

Miracuves works as your ongoing development partner — new features, releases, maintenance on a monthly retainer with weekly sprint demos.

From $2,299/month — cancel with 2 weeks notice
Dedicated Miracuves ML team assigned to your project
Direct communication with ML engineers — no account manager relay
Weekly model performance reports — accuracy, drift, latency metrics
Scales up or down as your ML needs evolve
Quality Standards

How Miracuves ensures every ML delivery meets production standard

Every project passes through Miracuves' quality gates before handoff — not as a checklist, as a non-negotiable delivery standard applied to every codebase we ship.

Modular pipeline — Data Ingestion / Feature Engineering / Training separatedArchitecture
MLflow experiment tracking — full reproducibility on every runExperimentation
GPU-accelerated training — distributed computing for large datasetsPerformance
Holdout validation — tested on unseen data before deploymentValidation
CI/CD pipeline — automated model training, evaluation, and deploymentMLOps
No data leakage — strict temporal train/test separation enforcedData Quality
Production monitoring — drift detection, accuracy tracking, automated alertsMonitoring
Enforced QA Gates

Our 6 Continuous Delivery Gateways

Every model artifact, training pipeline, and inference profile must successfully clear all six quality control gates before repository handoff.

01

Code Review on Every Pull Request

Every line merged into your main branch is reviewed by a senior Miracuves engineer. No untested code reaches your production environment under any circumstances.

02

Automated ML Test Coverage Required

Unit tests for feature engineering, integration tests for training pipelines, and contract tests for inference APIs. Minimum coverage enforced before any model is promoted to production.

03

Holdout Validation Before Production

Every model is evaluated on temporally separated holdout data — never on training sets. Inference latency and throughput are profiled under production load before deployment is approved.

04

Handoff Package — Not Just a Repository

Source code, model artifacts, environment setup guide, API documentation, deployment credentials, monitoring dashboards, and post-launch runbook — all included in every project handoff.

05

Model Registry and Rollback Ready

Every deployed model version is registered in MLflow with parameters, metrics, and artifacts. Rollback to a previous version is a configuration change — not a rebuild.

06

Post-Deployment Monitoring — 60-Day Active Support

Grafana dashboards track accuracy, drift, and inference latency from day one. Miracuves monitors model health during the 60-day post-deployment window — proactive retraining recommendations, not reactive firefighting.

Technology Stack

The ML stack Miracuves ships with

Matched to your architecture and delivery requirements — not a one-size-fits-all default.

TensorFlow
Deep learning · production inference
PyTorch
Research · dynamic computation graphs
scikit-learn
Classical ML · pipeline API
MLflow
Experiment tracking · model registry
Kubeflow
Pipeline orchestration · distributed training
Python 3.11+
Primary language · data science ecosystem
Jupyter
Exploratory analysis · prototyping
Docker
Containerized model deployment
Kubernetes
Orchestration · auto-scaling inference
AWS SageMaker
Managed training · GPU infrastructure
Postgres
Feature store · metadata storage
Redis
Feature caching · real-time inference
FastAPI
Model serving · REST API endpoints
Streamlit
ML dashboards · model demos
Grafana
Monitoring dashboards · metrics
Prometheus
Model monitoring · alerting
Our Process

From brief to deployed ML system — what happens and when

Every Machine Learning engagement follows the same delivery spine — whether you start from a scoped ML module or a custom architecture. You always know what Miracuves is doing, what data you need to provide, and what gets delivered at each step. Timelines reflect standard ML delivery; complex builds run milestone-based with the same checkpoints.

Brief & NDA

Share your concept via WhatsApp. NDA signed same day. We ask 6 specific questions.

Step 01

Scope & Plan

Right solution base, stack, and model confirmed. No payment before scope is agreed.

Step 02

Build & Demo

Repo created, architecture set. First commit in 24h. Weekly working demo runs.

Step 03

QA & Polish

Holdout validation, latency profiling, and drift checks on staging data mirroring production.

Step 04

Launch & Handoff

Model artifacts, pipeline code, API docs, and monitoring dashboards delivered. 60 days active support.

Step 05
Same DayNDA turnaround
6–12 WeeksStandard ML delivery
24 HoursFirst commit after scope
60 DaysPost-launch support
Transparent Pricing

What ML development costs at Miracuves

We publish prices because we are confident in what we deliver. No "contact us for pricing" pages. No hidden fees after scope is agreed.

Scoped ML Module

$3,699 from

Fixed scope · 6–12 week delivery · published anchor

  • ML solution — data pipeline + trained model + API
  • Admin panel included as standard
  • Branding and white-label applied
  • Full source code on handoff
  • 60-day post-launch support
  • NDA protected from day one
Start an ML Project
Most Requested

Custom ML Development

Custom Quote

Scoped before build · milestone billing

  • Full ML team — ML engineer + data engineer + MLOps
  • Custom architecture for your spec
  • Weekly sprint demos — working software
  • Production deployment — Kubernetes, SageMaker, or edge
  • Full source code · complete IP transfer
  • Milestone billing — no pay before delivery
Get a Scope & Quote

Ongoing Development

$2,299/mo

Monthly retainer · cancel with 2 weeks notice

  • Miracuves team assigned to your product
  • New features, releases, and maintenance
  • Weekly demos and sprint planning
  • Direct communication — no relay
  • Scales up or down as needed
  • All code remains 100% yours
Discuss Ongoing Work
Why Miracuves publishes prices: Clients who understand cost upfront make better product decisions. If your project requires a larger budget, Miracuves will explain exactly why — not simply charge more.

What affects ML project cost at Miracuves

Scoped ML module pricing stays fixed when the use case matches a proven template (recommendation, churn, forecasting). Custom ML builds scale with: data volume and quality, model complexity (classical vs deep learning), real-time inference requirements, number of data sources, MLOps scope (retraining cadence, A/B testing), compliance (HIPAA, PCI), and edge vs cloud deployment.

Typical Machine Learning budget ranges

Scoped ML module: from $3,699 · 6–12 weeks.
Custom ML platform: $18,000–$80,000 · 10–20 weeks depending on scope.
Ongoing retainer: from $2,299/month for feature work and maintenance.
Every quote is written before payment — no surprise invoices after kickoff.

Client Reference

What a real ML project looks like at Miracuves

A manufacturing company with 500+ machines across three factories needed to reduce unplanned downtime. Their IoT sensor data was siloed, and maintenance was purely reactive — costing $2M+ annually in lost production.

01

The Challenge

Five hundred machines generating 2TB of sensor data monthly with no centralized pipeline. Different sensor formats, missing timestamps, and no historical labeling of failure events. Needed a predictive system that could identify failures 48+ hours in advance.

02

What Miracuves Delivered

Built a TensorFlow-based anomaly detection pipeline: IoT data ingestion via MQTT, feature engineering with scikit-learn, LSTM autoencoder for sequence anomaly detection, and a Grafana dashboard for real-time monitoring. Deployed on Kubernetes with automated retraining every 24 hours.

03

Outcome

60% reduction in unplanned downtime in the first quarter. Identified bearing failures 72 hours before breakdown. System paid for itself within 4 months of deployment. Retrained daily with new sensor data — model accuracy improved from 82% to 94% over 6 months.

60%Downtime reduction
8 WeeksFull deployment
94%Model accuracy
View All Case Studies →

Client Testimonial

"We had been dealing with unexpected machine breakdowns for years — each one costing us $15K+ in lost production. Miracuves didn't just build a model; they built an entire pipeline from our factory floor sensors to a dashboard our maintenance team could act on. The 60% downtime reduction exceeded our target. Their ML team understood our industrial context from day one."

MR

M.R., VP of Operations

Manufacturing · Factory Predictive Maintenance

⭐ Read Clutch & Google Reviews →

Project Brief

Solution typePredictive Maintenance (ML)
Delivery timeline8 weeks
InfrastructureTensorFlow + Kubernetes
Key integrationsMQTT · IoT Sensors · Grafana
Data volume2TB/month · 500 machines
Source code100% client-owned
-60%
Unplanned downtime
72h
Early warning time
$2M+
Annual savings
Client Reviews

What clients say about Miracuves ML development

Across predictive maintenance, fraud detection, recommendation engines, and NLP projects — from manufacturing to fintech — verified on Clutch and Google.

★★★★★

Clutch · Predictive Maintenance

"Miracuves deployed a predictive maintenance system for our factory floor that reduced downtime by 60% in the first quarter. The TensorFlow pipeline ingests sensor data from 500 machines and predicts failures 72 hours in advance. Our maintenance team now works proactively instead of firefighting. The ROI was visible within 4 months."

MR

M.R., VP of Operations

Manufacturing · 500-Machine Factory

TensorFlow · Predictive Maintenance · IoT
★★★★★

Google Reviews · Fintech Platform

"We needed a real-time fraud detection system for our payment platform processing 50K transactions daily. Miracuves built an anomaly detection pipeline that flags suspicious transactions within 200ms. Our fraud loss dropped 80% in the first month. Their understanding of both ML engineering and production deployment was exceptional."

SK

S.K., CTO

Payment Platform · South-East Asia

PyTorch · Fraud Detection · Real-time
★★★★★

Clutch · Recommendation Engine

"Miracuves built our product recommendation engine from scratch — collaborative filtering with real-time personalization. A/B testing showed a 35% increase in click-through rates and 22% improvement in average order value. The ML team set up monitoring dashboards so we can track model performance ourselves. Professional and technically excellent."

AL

A.L., Head of Product

E-commerce Platform · North America

scikit-learn · Recommendation · A/B Testing
4.9 / 5.0 Clutch average rating
4.8 / 5.0 Google average rating
Top Developer Clutch recognition · 2024–2025
Read All Reviews →
Frequently Asked

Questions about ML development at Miracuves

Does Miracuves build custom ML models or use pre-built ones?

Miracuves builds custom models tailored to your data and use case. While we leverage proven frameworks like TensorFlow, PyTorch, and scikit-learn, every model is trained on your specific data with your specific success metrics. We do not deploy generic pre-built models as production solutions unless your use case genuinely calls for it.

What data do I need to provide for an ML project?

The data requirements vary by use case, but typically include historical records relevant to the prediction target — transaction logs for fraud detection, sensor readings for predictive maintenance, user behavior data for recommendation engines. Miracuves helps assess your data readiness in the discovery phase and can recommend data collection strategies if gaps exist.

How long does it take to deploy an ML solution?

A scoped ML solution — data pipeline, model training, API deployment, and monitoring dashboard — ships in 6–12 weeks depending on data complexity and model requirements. Custom builds with unique architectures take 10–16 weeks. All timelines include testing, validation, and deployment. Timelines are stated in writing before any payment.

What is included in the ML monitoring and maintenance?

Every ML delivery includes a monitoring dashboard (Grafana) tracking model accuracy, prediction latency, data drift, and system health. Automated alerts trigger when performance degrades. Miracuves includes 60 days of post-deployment support. Model retraining and ongoing MLOps are available through our monthly retainer model.

Can Miracuves deploy ML models on edge devices?

Yes. We support edge deployment using TensorFlow Lite and ONNX Runtime for devices with limited compute. Computer vision models, in particular, are optimized for edge inference with quantization and pruning. For cloud deployment, models run as containerized microservices on Kubernetes with auto-scaling.

Do I need a large dataset to work with Miracuves?

Not necessarily. Miracuves works with datasets of all sizes. For smaller datasets, we use classical ML approaches (scikit-learn) with careful cross-validation to avoid overfitting. For larger datasets, we leverage deep learning with GPU acceleration. If data is truly insufficient, we will recommend starting with data collection before model development.

How does Miracuves handle model versioning and reproducibility?

Every model experiment is tracked in MLflow — parameters, metrics, and artifacts are logged and versioned. The full pipeline is defined as code (Kubeflow), meaning any previous version can be fully reproduced. Model registry maintains a history of deployed versions with rollback capability. This is a non-negotiable standard on every project.

What happens if model accuracy degrades after deployment?

Monitoring dashboards track accuracy and drift continuously. If degradation is detected, Miracuves investigates root cause — data drift, concept drift, or infrastructure issues. Under the 60-day post-deployment support window, corrective retraining is included. For ongoing needs, our monthly retainer covers model retraining, pipeline updates, and continuous improvement.

Get Started

Ready to build your ML system with Miracuves?

Tell Miracuves what you are building. We will confirm the right solution base, service model, and delivery timeline — in writing, before any commitment is required from you.

30+ML systems delivered
6–12 WeeksML delivery
100%Source code yours
Same DayNDA turnaround
WhatsApp — Start Now Contact & Brief Form

NDA signed before we discuss your project details

Page reviewed by the Miracuves ML Engineering Team · Last updated May 2026 · Clutch & Google Reviews