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Master generative models that can create new data—from autoencoders for compression to VAEs for sampling and GANs for adversarial generation. Complete your ML journey by learning MLOps: the practices and tools needed to deploy, monitor, and maintain ML systems in production.
Generative models represent one of the most exciting frontiers in machine learning: systems that can create new data indistinguishable from real examples. Unlike discriminative models that learn boundaries between classes, generative models learn the underlying distribution of the data itself.
The progression from autoencoders to VAEs to GANs represents increasingly sophisticated approaches to generation. Autoencoders learn compressed representations useful for reconstruction. VAEs add probabilistic structure that enables sampling. GANs use adversarial training to produce highly realistic outputs.
Equally important is understanding how to deploy ML models in production. MLOps (Machine Learning Operations) encompasses the practices needed to reliably deploy and maintain ML systems. This includes experiment tracking, model versioning, continuous training, serving infrastructure, and monitoring for data drift.
The gap between a working notebook and a production system is substantial. Models in production face real-world challenges: changing data distributions, latency requirements, scaling concerns, and the need for reproducibility. Understanding MLOps is essential for any practicing ML engineer.
This chapter covers:
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Learning compressed representations through encoder-decoder bottlenecks — dimensionality reduction, denoising, and pre-training.
Probabilistic and adversarial approaches
Probabilistic latent spaces with the reparameterization trick — sampling new data and smooth latent interpolation.
Adversarial training between generator and discriminator — minimax game, mode collapse, and Wasserstein distance.
The full ML lifecycle — experiment tracking, model registry, serving infrastructure, and drift monitoring.
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