Introduction to deploying machine learning models, introduction to basic concepts

Hardware for inference and training (CPU, memory, hardware accelerators - GPU, TPU)

Virtualization, cloud, cloud platforms and services (Azure)

Virtual Machines, Linux and Packaged Systems

Representation of models in computer systems, estimation of model hardware requirements for inference and training

The lifecycle of machine learning models

Distributed training with TensorFlow, model serialization - saving and loading, model versioning

Model delivery architectures, APIs and endpoints, batch vs. real-time inference, microservices and serverless architecture, containerization (docker, kubernetes)

Scalability and load balancing, security, monitoring and model management

Machine Learning Platforms 1 (MLFlow, Azure ML)

Machine Learning Platforms 2 (Apache Spark, Databricks)

Reserve