TENSORFLOW DEVELOPMENT SERVICESProduction AI systems, delivered with TensorFlow.
TensorFlow is an open-source machine learning framework developed by Google that enables developers to build, train, and deploy machine learning and deep learning models at scale. It supports a wide range of tasks including neural networks, computer vision, natural language processing, and reinforcement learning. TensorFlow provides flexible tools and libraries for research and production, making it suitable for both experimentation and enterprise deployment.
import torch def train_step(batch): loss = model(batch) loss.backward() optimizer.step()25+
TensorFlow engineers on staff
60+
TensorFlow projects shipped
9 yrs
TensorFlow experience
98%
Client satisfaction
Pick the right tool — including when it isn't us
End-to-end open-source platform for machine learning and deep learning development. We'll tell you honestly when TensorFlow is the right fit — and when another stack would serve you better.
Use TensorFlow when
Best fit- You need deep learning models
- You need computer vision
- Enterprise-ready ML framework
- Highly scalable and performant
Consider alternatives when
Not ideal- A simple rules engine would solve the problem
- You have no labelled data and no plan to collect it
- Latency requirements are incompatible with model inference
- Regulatory constraints forbid cloud-based model APIs
From APIs to real-time platforms
Flexible Architecture
Build and train models using high-level APIs like Keras or low-level operations for complete control over model architecture.
GPU & TPU Support
Accelerate model training using GPUs and TPUs for high-performance deep learning workloads.
Scalable Training
Train models efficiently across distributed systems and large datasets.
Production Deployment
Deploy models to servers, mobile devices, browsers, and edge devices with TensorFlow Serving and Lite.
Deep Learning Models
Production-ready deep learning models with clear milestones and transparent delivery.
Computer Vision
Production-ready computer vision with clear milestones and transparent delivery.
Frameworks, tooling & ecosystem
MODELING
- TensorFlow
- Keras
- TensorFlow Lite
DATA & FEATURES
- TensorFlow.js
- ONNX
- Flexible Architecture
SERVING
- GPU & TPU Support
- Scalable Training
- Production Deployment
MLOPS
TensorFlow vs. the alternatives
Practical, no-hype reads on when to choose each.
Comparison
TensorFlow vs PyTorch
TensorFlow excels at deep learning models, while PyTorch may be a better fit when neural network training are the primary goal.
Read the comparisonComparison
TensorFlow vs OpenAI
TensorFlow excels at deep learning models, while OpenAI may be a better fit when ai chatbots are the primary goal.
Read the comparisonVetted TensorFlow engineers, ready to join your team
Hand-picked TensorFlow specialists with proven delivery experience. Flexible engagement — scale up or down monthly.
Request engineer profilesFrom kick-off to production — in four steps
Scope & profiles
We align on goals, stack and delivery model, then shortlist TensorFlow engineers matched to your needs.
01Technical interview
You interview shortlisted candidates. We handle scheduling, feedback loops and backup profiles.
02Onboarding & sprint 0
Engineers join your tools, meet the team and ship a small first deliverable within the first sprint.
03Iterate & scale
Scale the team up or down monthly as scope evolves — same engineers, no re-onboarding tax.
04