Pharos Production delivers PyTorch development services for enterprises building custom deep learning models. Our ML engineers work with PyTorch to develop computer vision systems, NLP models, recommendation engines and time-series forecasting solutions from research through production deployment. We handle the full PyTorch lifecycle - data pipeline engineering, model architecture design, distributed training on multi-GPU clusters, hyperparameter optimization, model compression (quantization, pruning, distillation) and deployment via TorchServe or ONNX Runtime. Our team builds custom neural networks when off-the-shelf models fall short. Pharos Production brings production ML discipline to PyTorch projects - experiment tracking with Weights and Biases, reproducible training pipelines, automated model evaluation, A/B testing infrastructure and monitoring for data drift and model degradation.
Enterprise-grade AI with responsible governance, data privacy and production-ready deployment
Computer vision systems
Object detection (YOLO, DETR), image classification, semantic segmentation, OCR and video analysis for manufacturing QA, medical imaging and retail analytics.
NLP and language models
Custom text classification, named entity recognition, sentiment analysis, summarization and domain-specific language model fine-tuning with Hugging Face Transformers.
Recommendation engines
Deep learning recommender systems using collaborative filtering, content-based models and hybrid approaches for e-commerce, media and advertising platforms.
Time-series forecasting
LSTM, Transformer and N-BEATS models for demand forecasting, financial prediction, anomaly detection and sensor data analysis.
Generative AI models
Custom diffusion models, VAEs and GANs for image generation, data augmentation, synthetic data creation and creative applications.
Edge and mobile ML
Model optimization with quantization, pruning and distillation for deployment on mobile (PyTorch Mobile), IoT devices and embedded systems.
| Factor | PyTorch | TensorFlow / JAX |
|---|---|---|
| Research adoption | 65%+ of ML papers use PyTorch | TF: declining in research. JAX: growing at Google |
| Dynamic graphs | Native eager execution, easy debugging | TF: eager mode added later. JAX: functional transforms |
| Production serving | TorchServe, ONNX, Triton | TF: TF Serving (mature). JAX: limited serving |
| Ecosystem | Hugging Face, torchvision, Lightning | TF: Keras, TFX. JAX: Flax, Optax |
| Distributed training | DDP, FSDP, DeepSpeed integration | TF: tf.distribute. JAX: pjit (TPU-native) |
| Mobile/edge | PyTorch Mobile, ExecuTorch | TF: TFLite (more mature). JAX: no mobile support |
| Learning curve | Pythonic, intuitive API | TF: complex API surface. JAX: functional paradigm |
Pharos Production recommends PyTorch for new deep learning projects due to its research dominance, Pythonic API and Hugging Face ecosystem integration. TensorFlow suits existing TF codebases and mobile-first ML (TFLite). JAX is best for Google TPU workloads and research requiring advanced autodiff.
Limitations: PyTorch mobile deployment (PyTorch Mobile, ExecuTorch) is less mature than TensorFlow Lite for on-device ML. PyTorch lacks an equivalent to TFX for end-to-end ML pipeline orchestration - teams typically combine PyTorch with Kubeflow or Airflow. For TPU-native workloads at Google Cloud scale, JAX provides better performance than PyTorch XLA.
Proprietary research based on 15+ PyTorch deep learning projects delivered by Pharos Production. Dataset covers computer vision, NLP, recommendation engines and time-series models. Methodology (Pharos Verified Delivery): aggregated training metrics, inference benchmarks and production deployment data. Full report available on request.
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PyTorch dominates ML research (65%+ of papers), has better debugging (eager execution), integrates natively with Hugging Face and offers a more Pythonic API. TensorFlow advantage in mobile deployment (TFLite) is narrowing with PyTorch ExecuTorch.
Yes. We deploy PyTorch models via TorchServe (managed serving with batching and scaling), ONNX Runtime (cross-platform inference), Triton (NVIDIA multi-framework server) or custom FastAPI services.
PyTorch powers production ML at Meta, Tesla and Uber.
It depends on the task. Image classification needs 1,000+ labeled images per class.
Fine-tuning pre-trained NLP models works with 500-5,000 examples. Transfer learning dramatically reduces data requirements compared to training from scratch.
Yes. We set up training infrastructure on AWS (SageMaker, EC2 GPU instances), GCP (Vertex AI, TPUs) or Azure ML.
We optimize for cost with spot instances, gradient accumulation and mixed-precision training (FP16/BF16).
Computer vision MVPs start from $55,000-$110,000. Custom NLP models range from $70,000 to $210,000.
Full ML platforms with training pipelines and monitoring 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
Fine-tuning large language models transforms general-purpose AI into domain-expert systems that understand your industry terminology, follow your output format requirements and achieve accuracy levels that prompting alone cannot reach. This guide covers the three dominant fine-tuning techniques in 2026 - LoRA, RLHF and DPO - with practical guidance on when to use each, how to […]
Computer vision use cases across manufacturing, healthcare, retail and security in 2026. Real implementation details, cost benchmarks and ROI data for CV deployments.
90+ engineers ready to deliver your PyTorch 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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