Machine Learning Infrastructure: The Bridge Between Software Engineering and AI

Dec 22, 2023 ·

Machine learning infra is a term used to describe the infrastructure, tools, and services required to build and deploy machine learning models. It includes hardware such as servers and GPUs, cloud-based platforms, development environments, and machine learning libraries. It also incorporates data pipelines, tools for data ingestion and transformation, and model deployment options.

Hardware plays an important role in machine learning infra. Servers and GPUs are necessary for training deep learning models, while specialized chips like ASICs are better suited for inference tasks. Cloud-based platforms provide a cost-effective way to access various resources without investing in dedicated hardware.

Development environments are the tools used to create ML models. Popular choices include frameworks such as TensorFlow, PyTorch, and Keras, as well as distributed computing platforms like Spark. Libraries are used to extend the functionality of these frameworks; examples include OpenCV, scikit-learn, and Theano.

Data pipelines are used to ingest data from various sources and transform it into the format required for training models. This process can be automated with tools like Apache Airflow and Luigi. Model deployment is the process of putting trained models into production and there are many options available such as containers, serverless platforms, and edge devices.

In summary, machine learning infra consists of the hardware, cloud-based platforms, development environments, libraries, data pipelines, and model deployment options used to build and deploy ML models. The hardware, such as servers and GPUs, are required to train models, while cloud-based platforms allow for ease of access to various resources. Development environments such as frameworks and libraries are needed for creating ML models, while data pipelines and model deployment technologies are used to put the models into production.