the raw data layer (data warehouses and object stores), the compute layer for orchestrated pipelines (feature and inference pipelines), the ML Development services for model training and experiment management, and the state layer for features and models as well as model serving and model monitoring.

The interactive ML systems are typically a Gradio or Streamlit UI (on Hugging Face Spaces or Streamlit Cloud) and work with a model either hosted or downloaded from Hopsworks. They typically take user input and join it with historical features from Hopsworks Feature Store, and produce predictions in the UI.

These system runs a feature pipeline once/day to synthetically generate a new Iris Flower and write it to the feature store. Then a batch inference pipeline, that also runs once/day but just after the feature pipeline, reads the single flower added that day, and downloads the Iris model trained to predict the type of Iris flower based on the 4 input features: sepal length, sepal width, petal length, and petal width. The model’s prediction is written to an online Dashboard, and actual flower (outcome) is read from the feature store and also published to the same Dashboard - so you can see if the model predicted correctly.

User cyber security awareness: Training users about cyber security helps protect against various attacks that target their systems. Documenting and managing assets: We need to know the types of systems and devices that we have to manage and protect properly. Updating and patching systems: Ensuring that computers, servers, and network devices are correctly updated and patched against any known vulnerability (weakness). Setting up preventative security devices: firewall and intrusion prevention systems (IPS) are critical components of preventative security. Firewalls control what network traffic can go inside and what can leave the system or network. IPS blocks any network traffic that matches present rules and attack signatures. Setting up logging and monitoring devices: Without proper logging and monitoring of the network, it won’t be possible to detect malicious activities and intrusions. If a new unauthorized device appears on our network, we should be able to know.

Defensive security is somewhat the opposite of offensive security, as it is concerned with two main tasks: Preventing intrusions from occurring Detecting intrusions when they occur and responding properly

Offensive security focuses on one thing: breaking into systems.

Lists are a mutable data structure in Python.