TensorFlow & PyTorch

TensorFlow and PyTorch are the two most powerful open-source frameworks for building machine learning systems — from simple classification models to state-of-the-art neural networks. At TAS, we help businesses move from R&D to real-world impact with production-grade ML pipelines powered by these technologies.

🧠 ML Engineering that Moves from Notebook to Production

Whether you need a vision system, time-series model, custom classifier, or deep learning engine — we bring the tools, engineering discipline, and deployment expertise to make it work at scale.

⚙️ What We Build with TensorFlow & PyTorch

🔍 ML Model Development

• Custom classifiers, regressors, segmenters

  

• CNNs, RNNs, transformers, GANs

• NLP (NER, classification, summarization)

• Computer Vision (OCR, object detection, facial analysis)

• Time series forecasting (ARIMA, LSTM, Prophet-style)

🛠️ Data Preprocessing & Feature Engineering

• Scalable pipelines for tabular, image, text, and audio data

• Automated cleaning, normalization, augmentation, encoding

• Feature selection & dimensionality reduction

🚀 Model Training & Optimization

• Model tuning using TensorBoard, Optuna, Hyperopt

• Cross-validation, ensembling, transfer learning

• GPU/TPU acceleration for faster training

• TensorFlow Extended (TFX) pipelines for production

🧪 Evaluation & Monitoring

• Model evaluation dashboards

• Custom metrics & explainability (SHAP, LIME)

• A/B testing & drift monitoring

⚙️ ML Deployment & Inference

• REST API with FastAPI / Flask

• Model packaging via Docker / ONNX

• TensorFlow Serving / TorchServe

• Scalable cloud deployment on AWS / GCP / Azure

• Integration with Streamlit, Gradio, and React dashboards

📈 Real Projects Built Using ML

🏥 Medical Transcription & Summarization System

Built multi-lingual transcription + summarization models using TensorFlow & GPT to automate clinical documentation in hospitals.

🧠 AI-Based Voice Assistant for Rural Call Centers

Used TensorFlow + Whisper for audio preprocessing and multilingual voice handling in India’s rural outreach platforms.

🌾 ML-Powered Yield Farming LP Scanner

Used TensorFlow for building a ranking model that evaluates yield farming opportunities across multiple chains based on volatility, TVL, and APR history.

🧰 Tools & Frameworks We Use

💡 Why TAS for TensorFlow & PyTorch Development?

• ✅ Applied AI – We build real ML systems, not just models.

• 🔄 End-to-End Support – From data to dashboard to deployment.

• 🧠 Custom Architectures – Optimized for your data and business goals.

• ⚙️ ModelOps Ready – CI/CD for machine learning.

• 🏁 Production Speed – Rapid MVPs with high accuracy benchmarks.

❓ Build & Deploy ML Models with TensorFlow & PyTorch – FAQs

Q1. What services do you provide with TensorFlow and PyTorch?
We offer end-to-end machine learning services including model design, training, fine-tuning, deployment, and optimization. Our expertise spans computer vision, natural language processing (NLP), predictive analytics, recommendation systems, and generative AI using TensorFlow and PyTorch.

Q2. Why should I choose TAS for ML model development?
At TAS, we blend deep ML expertise with product engineering experience. We don’t just build models — we ensure they’re production-ready, scalable, and optimized for your specific business needs across industries like fintech, healthcare, retail, and Web3.

Q3. What types of ML models can you build?
We develop a wide range of models:

• Classification & prediction models (fraud detection, risk scoring)

• NLP models (chatbots, sentiment analysis, document summarization)

• Computer vision models (image recognition, object detection, OCR)

• Generative AI models (text, image, and recommendation systems)

Q4. How long does it take to build and deploy an ML model?
A basic proof-of-concept can be developed in 4–6 weeks, while a full-scale production ML system with APIs, integrations, and monitoring may take 3–6 months, depending on data complexity and requirements.

Q5. What technologies and tools do you use?
We specialize in TensorFlow, PyTorch, Hugging Face Transformers, LangChain, Scikit-learn, FastAPI, Kubernetes, and cloud ML services (AWS Sagemaker, GCP Vertex AI, Azure ML) for scalable deployments.

Q6. Do you provide custom training and fine-tuning of models?
Yes. We fine-tune pre-trained models or build custom architectures from scratch using your domain-specific data, ensuring higher accuracy and better business outcomes.

Q7. How much does ML model development cost?
Costs vary by complexity, data preparation, and infrastructure needs. A basic model starts from a few thousand dollars, while enterprise-grade AI systems with custom training and scaling require higher investment. We provide transparent, tailored pricing.

Q8. Do you provide post-deployment monitoring and optimization?
Absolutely. We offer ongoing monitoring, retraining, performance tuning, and feature upgrades to keep your ML models accurate and future-ready.

📞 Let’s Build Your ML Stack the Right Way

Need a model that solves your business challenge — and runs in production?
TAS is your expert partner in TensorFlow and PyTorch development.

👉 [Book a Free ML Strategy Call]