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Professional Python for AI Engineering: Developing Advanced Solutions in Data Science and Deep Learning
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Publisher: Independently Published
Pages: 218
Weight: 0.85
Height: 0.46 Width: 7.00 Depth: 10.00
Language: English
You'll explore how to:
- Build reproducible Python environments and manage dependencies for AI projects
- Optimize memory usage, algorithms, data loading, and expensive computations
- Process massive datasets using multiprocessing, Dask, Polars, and out-of-core techniques
- Apply architectural patterns to create modular and maintainable machine learning systems
- Structure PyTorch and TensorFlow projects for production
- Optimize GPU utilization, mixed-precision training, distributed training, and hyperparameters
- Build scalable and fault-tolerant model training pipelines
- Serialize and deploy models with ONNX, TorchScript, REST APIs, gRPC, and containers
- Test, profile, debug, monitor, and continuously improve AI codebases
- Connect data pipelines, feature stores, retraining workflows, telemetry, and security into end-to-end AI engineering systems
Take your Python skills beyond scripting and into professional AI engineering.
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