Pharos Production provides TensorFlow development services for enterprises deploying ML models at scale. Our team builds production ML pipelines with TensorFlow, Keras, TensorFlow Serving, TFX and TensorFlow Lite for mobile and edge deployment. We develop custom neural networks for image classification, object detection, natural language understanding, anomaly detection and predictive analytics. TensorFlow excels in production environments with its mature serving infrastructure, model versioning and cross-platform deployment from cloud servers to mobile devices and edge hardware. Pharos Production brings end-to-end ML engineering to TensorFlow projects - feature stores, training pipeline orchestration with TFX, model validation, canary deployments and real-time performance monitoring. We help enterprises build ML systems that are reproducible, auditable and scalable.
Enterprise-grade AI with responsible governance, data privacy and production-ready deployment
Production ML pipelines
End-to-end ML workflows with TFX - data validation (TFDV), preprocessing (TF Transform), training, evaluation (TFMA), model validation and serving with automated retraining triggers.
Mobile and edge ML
On-device models with TensorFlow Lite for image classification, object detection, pose estimation and text classification on Android, iOS and embedded devices.
Image classification and detection
Transfer learning with EfficientNet, ResNet and MobileNet for product recognition, defect detection, medical imaging and satellite image analysis.
Natural language processing
Text classification, sentiment analysis, named entity recognition and question answering with Keras NLP and TensorFlow Hub pre-trained models.
Anomaly detection
Autoencoders and isolation forests for fraud detection, equipment failure prediction, network intrusion detection and quality control.
Browser-based ML
TensorFlow.js for client-side inference, privacy-preserving ML, interactive demos and real-time predictions without server round-trips.
| Factor | TensorFlow | PyTorch / scikit-learn |
|---|---|---|
| Production tooling | TFX, TF Serving, TF Lite - most mature | PyTorch: TorchServe. scikit-learn: Flask/FastAPI |
| Mobile/edge | TF Lite - industry standard for on-device ML | PyTorch: ExecuTorch (newer). scikit-learn: N/A |
| Pipeline orchestration | TFX with built-in validation and monitoring | PyTorch: Kubeflow/Airflow. scikit-learn: manual |
| Browser ML | TensorFlow.js - mature and widely used | PyTorch: ONNX.js (limited). scikit-learn: N/A |
| Research adoption | Declining in research, strong in production | PyTorch: 65%+ of papers. scikit-learn: classical ML |
| API simplicity | Keras high-level API, clean and intuitive | PyTorch: Pythonic. scikit-learn: simplest API |
| Google Cloud | Native Vertex AI integration, TPU support | PyTorch: Vertex AI support. scikit-learn: limited |
Pharos Production recommends TensorFlow for production ML pipelines with TFX, mobile/edge deployment with TF Lite and Google Cloud workloads. PyTorch suits research-heavy projects and Hugging Face workflows. scikit-learn is best for classical ML (non-deep-learning) tasks.
Limitations: TensorFlow has a steeper learning curve than PyTorch due to its larger API surface (TF1 vs TF2, Keras vs tf.keras). Research community has shifted to PyTorch - finding cutting-edge model implementations in TensorFlow is harder. TensorFlow eager mode performance lags behind PyTorch for dynamic models. Migration from TF1 to TF2 can be complex for legacy codebases.
Proprietary research based on 15+ TensorFlow production ML projects delivered by Pharos Production. Dataset covers TFX pipelines, mobile models, anomaly detection and NLP systems. Methodology (Pharos Verified Delivery): aggregated training, serving and deployment metrics. Full report available on request.
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TensorFlow is best for production ML pipelines (TFX), mobile deployment (TF Lite) and Google Cloud integration. PyTorch is better for research, Hugging Face models and dynamic architectures.
We help teams choose based on deployment targets and team expertise.
Yes. We migrate TF1 codebases to TF2 with Keras, converting session-based code to eager execution, updating deprecated APIs and modernizing the training loop. Typical migration takes 4-8 weeks depending on codebase size.
TFX provides built-in data validation, feature engineering, model evaluation and serving that would take months to build from scratch. It integrates with Apache Beam, Airflow and Kubeflow for orchestration.
Custom pipelines offer more flexibility but require significantly more maintenance.
TensorFlow Lite is the industry standard for on-device ML. We deploy image classification, object detection and NLP models on Android and iOS with <10ms inference latency and minimal battery impact.
ML pipeline MVPs start from $55,000-$110,000. Mobile ML projects range from $40,000 to $140,000.
Enterprise TFX platforms with automated retraining cost $150,000 to $420,000+.
Pharos Production works in three engagement models, from a focused PoC to a production MVP to a full enterprise platform, with typical budgets from $10,000 to $400,000+ depending on scope and complexity.
Focused validation of your riskiest technical assumption with a working spike and a clear build-or-pivot recommendation.
Production-ready first version with core flows, real backend and the integrations to onboard first paying users.
Full-scale build with architecture, DevOps, QA, security and long-term evolution.
Prices vary based on project scope, complexity, timeline and requirements. Hourly rates range from $35 to $75 depending on role and seniority. Contact us for a personalized estimate.
Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.
We'll design, build and launch your MVP, ensuring it meets the core requirements of your software solution.
We'll create a complete software solution that is custom-made to meet your exact specifications.
Our company will be right there with you, keeping your software solution running smoothly, fixing issues and rolling out updates.
Recognized on Clutch, GoodFirms and The Manifest for software engineering excellence
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90+ engineers ready to deliver your TensorFlow project on time and within budget
Contact us today to discuss your project. We're ready to review your request promptly and guide you on the best next steps for collaboration
Same dayWe're committed to keeping your information confidential, so we'll sign a Non-Disclosure Agreement
1 dayAfter we chat about your goals and needs, we'll craft a comprehensive proposal detailing the project scope, team, timeline and budget
3-5 daysLet's connect on Google Meet to go through the proposal and confirm all the details together!
1-2 daysAs soon as the contract is signed, our dedicated team will jump into action on your project!
Same dayHeadquarters in Las Vegas, Nevada. Engineering office in Kyiv, Ukraine.
We also work with clients through dedicated local teams in Las Vegas, New York and San Francisco.
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