3 Best Practices for Finterlabs Machine Learning Infra
Introduction
Machine Learning (ML) infrastructure is the basis of ML models that are ready to be developed and deployed. The development of an infrastructure that supports the seamless training, testing, and deployment of models at an enterprise scale is as important to long-term viability as the ML models themselves. ML infrastructure supports machine learning workflows in every stage. It also helps the human resources team (data scientists, DevOps, and engineers) to manage and operate all kinds of resources and processes required to train and deploy neural network models. Here are 3 best practices for Finterlabs ML:
1. Map Reduce
Businesses must first identify and forecast future requirements for ML infrastructure. The next thing to consider is the hardware in developing ML infrastructure that impacts performances and expenses. For example, the Central Processing Unit (CPU) implements traditional ML models and the Graphics Processing Unit (GPU) implements deep learning models. These models require a great number of datasets within the infrastructure. Furthermore, the efficiency of CPUs and GPUs makes use of algorithms and functions that impact operations and cloud usage. These parts need extra attention as they affect the deployment. Hence, it is important to balance the underpowering and overpowering components while implementing ML infrastructure.
2. Determine Network and Storage Environment
Another best practice is to select the most suitable network and storage environments for ML infrastructure. A network environment ensures MLOps efficiency and seamless and reliable communication between the network and component. It also helps leverage networking abilities to store and process data.
Furthermore, ML infrastructure requires a sturdy storage environment to store a great number of datasets that are collected from diverse sources. This storage helps ML infrastructures in preventing delays, data ingestion, and executing complex training models.
3. Securing Data and Processes
Businesses must know that training and executing ML models require a great number of datasets. The data collected and used in these processes are confidential and valuable. Any data violation or manipulation could inflict serious consequences. Hence, it is important to develop, monitor, encrypt, and authorize data while implementing ML infrastructure. It also helps businesses understand and obey data compliances.
Furthermore, ML infrastructure requires a sturdy storage environment to store a great number of datasets that are collected from diverse sources. This storage helps ML infrastructures in preventing delays, data ingestion, and executing complex training models.