Benefits of machine learning pipelines – Introduction to Pipelines and Kubeflow

There are various benefits of machine learning pipelines:

Execution

The pipeline gives users the ability to program many phases to carry out in parallel in a dependable and unsupervised manner. This indicates that users are free to concentrate on other things concurrently while the process of data modelling and preparation is being carried out. Since each stage of the ML pipeline process runs as an independent component, the program can run more rapidly and provide an efficient and high-quality result.

Model versioning and tracking

Instead of manually keeping track of the data and outputs for each iteration, we may make use of a pipeline to clearly identify and version the data sources, as well as the inputs and outputs.

Troubleshooting

The pipeline approach divides each job into its components (such as different components for data pre-processing and model training). As a result, it is much simpler to debug the whole piece of code and pinpoint where the problems lie inside a certain component.

Resource utilization

The usage of several pipelines that are successfully coordinated across physically separate but similarly equipped computer systems and storage facilities is now possible. It enables effective use of hardware by distributing pipeline stages over several machines (CPUs, GPUs, Data Science VMs, and so on) depending on the computation complexity of each step.

Introduction to Kubeflow

The objective of the Kubeflow project is to simplify the deployment of ML workflows on Kubernetes while also enabling them to be portable and scalable. The aim of Kubeflow is to simplify the process of deploying open-source ML systems to a wide variety of infrastructures. Any environment in which Kubernetes is operational should also support the use of Kubeflow. The Kubeflow Pipelines SDK gives users access to a collection of Python packages that can be used to define and execute the ML workflows.

Components of Kubeflow

In Kubeflow Pipelines (KFP), the most fundamental building block of any piece of processing mechanism is known as a component. A component is a named template that specifies how to operate a container by making use of an image, a command, and several parameters. Because a component can also contain inputs and outputs, it can be thought of as a computational template in the same way that a function is. The dynamic data, known as component inputs, are utilized in either the container instructions or the arguments. Outputs from components might be artifacts of ML that are serializable.

There are three different ways to write components with these three characteristics:

• Lightweight Python function-based components: Lightweight Python function-based components are easiest to author (also known as a lightweight component). Lightweight components create a component that runs a single Python function in a container during execution.
• Containerized Python components: Containerized Python components let users bundle Python function-based components in containers. Containerized Python components allow authors to utilize extra source code outside the component’s Python function description, including several files. This is the ideal way for publishing Python components that require more source code than can be contained in a single function or for reusing source code in several components.
• Custom container components: You may provide a container to execute as your component by using custom container components. Using an image, command, and args, you may specify a container using the ContainerSpec object of KFP.