AI platform

The emergence of AI is the ability of a digital computer or computer-controlled system to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes that are characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

Big data, modern machine learning, and other advanced analytics technologies are used as platforms to improve IT operations (monitoring, automation, and service) functions directly and indirectly by providing proactive, personalized, and dynamic insight. MLOps is a set of practices for data scientists and operations professionals to collaborate and communicate on Machine Learning workflows.

MicroServices & Containerization

Microservices describes the architectural pattern of composing a distributed application from separately deployable services that perform specific business functions and communicate over web interfaces. DevOps teams encapsulate individual pieces of functionality in microservices and build larger systems by composing the microservices like building blocks.

Containerization of applications is an OS-level virtualization method used to deploy and run distributed applications essentially bundling an application’s code with all the files and libraries it needs to run on any infrastructure. This bundled package is known as a container and stored as an image.

Container Platforms provide orchestration that automates the provisioning, deployment, networking, scaling, availability, and lifecycle management of containers. Excellent use-cases include Cloud Migration, Microservice adoption and IoT devices. Containerization of Machine Learning modeling processes leads to an optimized workflow that can re-use the same overhead.

Combining the MLOps workflow pattern with Microservices frames the technologies that can be used to accomplish preparing an AI Platform.

Container Platform in the Lab

Defining the development of the IoTAttic Labs has outlined the use of automation to build both Software and Machine Learning models. There are 2 main functions of this automation, MLOps and the automation of software development that can infer the models. Our AI Platform requires:

  • Ability to gather data

  • Workspaces to conduct model experiments

  • Model Registry

  • Deployment of Models/Endpoints

  • Model Monitoring