Available Now · 25+ TensorFlow Deployments

TensorFlow App Development Company

Keras · TFX · TF Serving · TF Lite

Miracuves is a TensorFlow development company that builds production deep learning systems — computer vision, NLP, recommendation models, and time-series forecasting — with Keras, TFX pipelines, TF Serving, and TensorFlow Lite. You get trained models, inference APIs, monitoring dashboards, and complete source code ownership.

25+ TF Models 95% Model Accuracy 100% IP Ownership NDA Day One
WhatsApp — Talk to Our Team Explore TensorFlow Solutions
Clutch Reviewed 4.9★ · Starting from $3,699 · View TensorFlow deployments
Miracuves Delivery RecordTensorFlow Engineering
6–12 Weeks
Delivery timeline
$3,699
Starting price
25+
TensorFlow Systems Live
TFX
Pipeline Standard
TensorFlow engineers active right now

Computer Vision

Quality Inspection CNN

Deployed in 8 weeks · 98.7% accuracy

Fintech

Fraud Detection Model

Deployed in 10 weeks · TF Serving
TensorFlow Pipeline Console ACTIVE (TF 2.x)
FRAMEWORK TensorFlow / Keras
PIPELINE TFX + TensorBoard
SERVING TF Serving + TF Lite
MONITORING Drift + Latency Alerts
25+ TensorFlow SystemsDeployed across cloud and edge
6–12 WeeksTypical delivery timeline
TFX + KerasProduction pipeline standard
TF Serving + LiteCloud and on-device inference
TensorBoardExperiment tracking on every build

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 TensorFlow Approach

How Miracuves delivers TensorFlow systems — from 25+ production deployments

After deploying 25+ production TensorFlow systems across computer vision, fintech, and e-commerce, Miracuves has a specific methodology for TensorFlow delivery. We start from proven TFX pipeline modules — data validation, transform, trainer, evaluator, and pusher — not from an untracked Keras notebook.

TFX pipelines deliver consistent, reproducible training and deployment from a unified codebase. For production, this eliminates the gap between Keras experimentation and TF Serving — one pipeline, automated retraining, full model artifacts yours on handoff.

Who this service is built for: Product leaders and engineering teams that need deep learning in production — image classification, object detection, NLP, recommendation models, or time-series forecasting. Miracuves TensorFlow development fits when you want Keras models served via TF Serving or TF Lite, TFX pipelines for retraining, and a company accountable for the full lifecycle — not a one-off notebook. If PyTorch or classical ML is a better fit, we say so upfront.

End-to-end TFX pipeline — ExampleGen, Transform, Trainer, Evaluator, Pusher to TF Serving
TensorFlow 2.x and Keras as primary stack — TensorBoard for experiment tracking, SavedModel artifacts
Distributed training on GPU with tf.distribute — TFX orchestration for automated retraining
GPU-accelerated training on AWS SageMaker with automated hyperparameter tuning
Production monitoring with Grafana and Prometheus — drift detection, accuracy tracking, automated alerts

From our TensorFlow team — E-commerce recommendation engine, 6 weeks

"We needed product recommendations at scale — 2M SKUs, real-time inference, weekly retraining. Miracuves built a TensorFlow two-tower model with TFX pipeline, TF Serving on Kubernetes, and TensorBoard monitoring. Click-through rate rose 35% in the first A/B test. The TFX pipeline retrained every Sunday without our team touching it."

Written by the Miracuves TensorFlow 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 TensorFlow at Miracuves

Time to first production model6–12 weeks
Experiment reproducibilityMLflow on every run
Inference deploymentAPI · Batch · Edge
Monitoring includedDrift + accuracy alerts
Deployment targetsCloud · Edge · Browser
Source code ownership100% yours
25+ TensorFlow Systems Deployed

What Miracuves builds — production TensorFlow systems you can ship

Discuss Your TensorFlow Use Case →
8 Weeks01

E-commerce

Recommendation Engine

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

From $12,999TensorFlowTF Serving
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 $22,999TensorFlow<200ms
6 Weeks04

Retail

Demand Forecasting

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

From $14,999TensorFlowTime Series
6 Weeks05

SaaS

Churn Prediction

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

From $11,999KerasCRM 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: TensorFlow is transformative for data-rich use cases — recommendation, prediction, classification, and anomaly detection. For simple rule-based automation or problems better suited to PyTorch research workflows, Miracuves may recommend an alternative stack. We tell you which fits before any commitment.
Technology Comparison

Custom TensorFlow pipeline vs PyTorch 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 TensorFlow · TFX Pipeline
← MIRACUVES DEFAULT		 PyTorch Stack DIY TensorFlow
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 TF · TFX · TF Serving · full ownership Research flexibility · dynamic graphs Notebook experiments · no MLOps

Choose Miracuves TensorFlow if…

You need Keras models · TFX pipelines · TF Serving or TF Lite deployment · a team accountable for model performance in production.

Consider an alternative if…

Your team is research-first on PyTorch · you only need a notebook prototype without production serving · or you lack sufficient labeled data and should start with data collection first. See AI Development →

Technical Architecture

How Miracuves engineers structure TensorFlow projects for production

These are the specific decisions our TensorFlow 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 TFX Pipeline

Strict separation: ExampleGen → StatisticsGen → SchemaGen → Transform → Trainer → Evaluator → Pusher. 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 — TensorBoard + TFX Pipeline Components

TensorBoard tracks every Keras training run with full metric and graph logging. TFX orchestrates the end-to-end TensorFlow 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 tf.distribute

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 TensorFlow 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_model.py — TensorFlow Keras
# Production TensorFlow/Keras training pipeline # Used in CV, NLP, and recommendation products import tensorflow as tf from tensorflow import keras def build_classifier(input_dim, num_classes): model = keras.Sequential([ keras.layers.Dense(256, activation='relu', input_shape=(input_dim,)), keras.layers.Dropout(0.3), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(num_classes, activation='softmax') ]) model.compile( optimizer=keras.optimizers.Adam(learning_rate=1e-3), loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) return model strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = build_classifier(input_dim=512, num_classes=10)
Keras Sequential model with tf.distribute for multi-GPU training. SavedModel export for TF Serving deployment. Used in every TensorFlow product Miracuves ships.
Our Service Models

Three ways Miracuves delivers your TensorFlow 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 TensorFlow stack Miracuves ships with

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

TensorFlow 2.x
Core deep learning framework
Keras
High-level model API
TFX
Production ML pipelines
TF Serving
Scalable inference APIs
TF Lite
Mobile & edge deployment
TF.js
Browser inference
TensorBoard
Training visualization
Python 3.12+
Pipeline & training code
Kubernetes
TF Serving orchestration
Grafana
Inference monitoring
AWS / GCP
GPU training infrastructure
PostgreSQL
Feature & metadata store
Redis
Real-time feature cache
Docker
Containerized training & serving
Airflow
Pipeline scheduling
BigQuery
Large-scale data warehouse
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 TensorFlow 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 TensorFlow project cost at Miracuves

Scoped TensorFlow 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 TensorFlow budget ranges

Scoped TensorFlow module: from $3,699 · 6–12 weeks.
Custom TensorFlow 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 TensorFlow development

Across computer vision, recommendation engines, fraud detection, and NLP — from e-commerce to healthcare — 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 TensorFlow development at Miracuves

When should we choose TensorFlow over PyTorch?

TensorFlow is the stronger fit when you need production-grade serving (TF Serving), mobile or edge inference (TF Lite), and governed MLOps pipelines (TFX). PyTorch often wins for fast research iteration. Miracuves recommends TensorFlow when deployment, scale, and long-term operations matter as much as model training.

What is included in a TensorFlow project at Miracuves?

A typical engagement covers data ingestion, Keras model development, experiment tracking in TensorBoard, TFX pipeline setup, TF Serving or TF Lite deployment, monitoring dashboards, and handover documentation. Scope is fixed in writing before development starts — no open-ended hourly billing.

How long does TensorFlow development take?

Most TensorFlow systems — training pipeline, model, serving layer, and monitoring — ship in 6–12 weeks depending on data complexity and inference requirements. Edge-optimized TF Lite builds or multi-model TFX pipelines may take 10–16 weeks. Timelines are confirmed in the discovery phase.

Can Miracuves deploy TensorFlow models on mobile and edge devices?

Yes. Miracuves converts trained Keras models to TensorFlow Lite with quantization and pruning for on-device inference on Android, iOS, and embedded hardware. For cloud workloads, models run behind TF Serving on Kubernetes with auto-scaling and latency SLAs.

Does Miracuves use TFX for production ML pipelines?

Yes. TFX is our standard for production TensorFlow pipelines — covering data validation, transformation, training, evaluation, and deployment as reproducible components. This reduces manual steps, improves auditability, and makes retraining schedulable without rebuilding from scratch.

What data do I need before starting a TensorFlow build?

Requirements depend on the use case: labeled images for vision, time-series logs for forecasting, or structured transaction data for classification. Miracuves runs a data readiness review in week one and flags gaps before training begins — we do not charge for model work on unusable data.

How does Miracuves monitor TensorFlow models after launch?

Every deployment includes dashboards tracking prediction latency, throughput, data drift, and accuracy against holdout benchmarks. Alerts fire when thresholds are breached. Miracuves includes 60 days of post-launch support; ongoing retraining and TFX pipeline maintenance are available on retainer.

What does TensorFlow development cost at Miracuves?

TensorFlow projects start at $3,699 for a scoped build with defined deliverables. Final pricing depends on model complexity, data volume, serving requirements, and edge vs cloud deployment. Miracuves provides a fixed quote after discovery — no surprise change orders mid-project.

Get Started

Ready to build your TensorFlow system with Miracuves?

Tell Miracuves what TensorFlow model you are building. We will confirm the right architecture, TFX pipeline scope, and delivery timeline — in writing, before any commitment is required from you.

25+TensorFlow 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 TensorFlow Engineering Team · Last updated May 2026 · Clutch & Google Reviews