Model architecture

Q: How deeply does Delivering Machine Learning Projects: From design to deployment cover model architecture?

Develops a high-level system-component feasibility check, but defers detailed interactions and does not specify an internal model topology.

As explained in Delivering Machine Learning Projects: From design to deployment

A model architecture is the structural arrangement of components and connections that defines how a machine learning model processes inputs and produces outputs.

A model’s architecture is the arrangement of components and connections that determines how it processes inputs and produces outputs. No particular internal model design is specified here. The immediate architectural decision is whether a model with the required properties can be built and run within a viable production system. section 2.7.2

Before committing to a design, complete a high-level feasibility check:

Identify the client’s data and processing systems, cloud policies, and legacy components; record constraints on scale and speed. section 2.3.2
Identify the system components needed to supply data, execute the model, and use its outputs. Check that those components are available and that the team can commission them. section 2.7.2
Record missing components or skills as delivery risks now; leave detailed component interactions for later system design. section 2.7.2

Smart-building control

A proposed system takes building-sensor data into a database, runs a model to determine control signals, sends those signals to actuators, and presents events and decisions to users. This establishes the surrounding system components, not the model’s internal architecture. section 2.7.2

Capture the resulting decisions in a technical architecture deliverable so the team and client can review what is proposed and what remains unresolved. section 4.3.5

How Delivering Machine Learning Projects: From design to deployment covers it

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Related concepts

transformer architectureincludes · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
Apache Kafkacontrasts with · Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
hexagonal architectureincludes · Hexagonal architecture is a software architecture pattern that isolates an application’s core logic from external systems through ports and adapters.
autoencoderincludes · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
linear layerincludes · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
architectural subsystemhas part · An architectural subsystem is a distinct, cohesive part of a larger system that provides related functionality through interacting components and defined boundaries.

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