Deploying AI models using TensorFlow on Google Cloud.
TensorFlow/Keras Engineer
TensorFlow, Keras, Google Cloud
Best for
- ▸Building TensorFlow Serving REST/gRPC endpoints for production model inference
- ▸Optimizing tf.data pipelines with caching, prefetching, and parallel processing
- ▸Implementing distributed training strategies across multi-GPU and multi-node setups
- ▸Converting models to TF Lite for mobile/edge deployment with quantization
What you'll get
- ▸Complete tf.data pipeline code with interleave, cache, and prefetch optimizations including performance benchmarks
- ▸Multi-GPU distributed training implementation using MirroredStrategy with proper data sharding and gradient aggregation
- ▸TensorFlow Serving deployment configuration with batching, model versioning, and monitoring setup
Clear problem definition with model architecture requirements, data characteristics, and deployment constraints (latency, throughput, edge vs cloud).
Production-ready TensorFlow code with optimized data pipelines, model architectures, training loops, and deployment configurations including performance benchmarks.
What's inside
“You are a TensorFlow/Keras Engineer. You build production-grade ML models using TensorFlow's ecosystem, optimizing for performance, scalability, and deployment across cloud, edge, and mobile environments. - Architect end-to-end ML systems spanning data pipelines (tf.data), model training (distribute...”
Covers
Not designed for ↓
- ×PyTorch model development or conversion to ONNX format
- ×MLOps platform architecture beyond TensorFlow ecosystem tools
- ×Cloud infrastructure provisioning or Kubernetes cluster management
- ×Data preprocessing workflows outside of tf.data and TFX Transform
SupaScore
88.85▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 6 sources (4 official docs, 2 paper)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial version
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
TensorFlow Production ML Pipeline
End-to-end workflow from model development through production serving with observability
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