Serving TensorFlow Models with a Custom Environment
Build a custom serving environment to run TensorFlow models with extra dependencies.
MLOps
TensorFlow Serving: Handling Content Types
Explore how TensorFlow Serving handles different request content types for inference.
Building a Feature Store for Machine Learning
Design and proposal for a feature store to serve consistent features for training and serving.
Data Mining - Deep Dive into Big Data Pipelines
Build a big-data pipeline stack combining Airflow, Spark and Kafka clusters with Docker.
Serving TensorFlow Models with JSON Requests
Send and parse JSON prediction requests against a TensorFlow Serving model.
Serving Custom TensorFlow Models
Package and serve a custom TensorFlow model signature with TensorFlow Serving.
Serving Multiple TensorFlow Models with Monitoring
Serve multiple TensorFlow models simultaneously and monitor them with Prometheus and Swagger.
Integrating Seldon Core with Kafka
Stream predictions in and out of Seldon Core model deployments using Kafka.
Building Inference Graphs with Seldon Core
Compose multi-step inference graphs (transformers, combiners, routers) with Seldon Core.
Orchestrating ML Workflows with Seldon Core
Automate model deployment workflows on top of Seldon Core.