MLOps and Databricks!
Find out what is MLOPS, their importance, and how to deploy using Databricks!
This publication is a set of summaries, posts, links, and videos about mlops and using the Databricks solution for mlops.
After all, what is Databricks?
Databricks is a cloud-based data analysis and engineering platform offering a unified Big Data processing approach. It was founded in 2013 by the creators of Apache Spark, a large-wide-source Big Data processing mechanism.
Databricks provides a collaborative working space for data engineers, data scientists, and business analysts to process and analyze large data sets. It offers a set of tools and services that facilitate the creation, training, and deployment of large-scale machine-learning models. Databricks also supports a variety of data sources, including structured data, unstructured data, and streaming data.
One of Databricks’ main features is its ability to take advantage of Apache Spark, which allows faster data processing and analysis through distributed computing. Databricks also provides various integrated services such as data visualization, data intake, and data governance.
In short, Databricks is a cloud-based data analysis and data engineering platform that provides a unified approach to processing and analyzing Big Data with a set of tools and services that facilitate the creation, training, and implementation of learning models. Of machine on scale.
Example: Live of a data pipeline at Databricks with Luan Moreno and Matheus Oliveira:
Machine Learning Operations (MLOPs) is a set of practices that combines machine engineering and machine learning principles to enable the development, deployment, and management of scale machine learning models.
MLOps automates the entire machine learning life cycle, from data preparation and model training to model deployment and monitoring…