Tabla de Contenidos: “…Registering models with a model registry -- Serving models using model serving services -- The Gunicorn and Flask inference engine -- The TensorFlow Serving framework -- The TorchServe serving framework -- KFServing framework -- Seldon Core -- Triton Inference Server -- Monitoring models in production -- Managing ML features -- Automating ML pipeline workflows -- Apache Airflow -- Kubeflow Pipelines -- Designing an end-to-end ML platform -- ML platform-based strategy -- ML component-based strategy -- Summary -- Chapter 8: Building a Data Science Environment Using AWS ML Services -- Technical requirements -- SageMaker overview -- Data science environment architecture using SageMaker -- Onboarding SageMaker users -- Launching Studio applications -- Preparing data -- Preparing data interactively with SageMaker Data Wrangler -- Preparing data at scale interactively -- Processing data as separate jobs -- Creating, storing, and sharing features -- Training ML models -- Tuning ML models -- Deploying ML models for testing -- Best practices for building a data science environment -- Hands-on exercise - building a data science environment using AWS services -- Problem statement -- Dataset description -- Lab instructions -- Setting up SageMaker Studio -- Launching a JupyterLab notebook -- Training the BERT model in the Jupyter notebook -- Training the BERT model with the SageMaker Training service -- Deploying the model -- Building ML models with SageMaker Canvas -- Summary -- Chapter 9: Designing an Enterprise ML Architecture with AWS ML Services -- Technical requirements -- Key considerations for ML platforms -- The personas of ML platforms and their requirements -- ML platform builders -- Platform users and operators -- Common workflow of an ML initiative -- Platform requirements for the different personas -- Key requirements for an enterprise ML platform…”