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Apache Spark : Master Big Data with PySpark and DataBricks

#artificialintelligence

This course is designed to help you develop the skill necessary to perform ETL operations in Databricks using pyspark, build production ready ML models, learn spark optimization techniques and master distributed computing. Big data engineers provide organizations with analyses that help them assess their performance, identify market demographics, and predict upcoming changes and market trends. Azure Databricks is a data analytics platform optimized for the Microsoft Azure cloud services platform. Azure Databricks offers three environments for developing data intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning. A data lakehouse is a data solution concept that combines elements of the data warehouse with those of the data lake.


Azure Databricks, industry-leading analytics platform powered by Apache Spark

@machinelearnbot

This blog post was co-authored by Ali Ghodsi, CEO, Databricks. The confluence of cloud, data, and AI is driving unprecedented change. The ability to utilize data and turn it into breakthrough insights is foundational to innovation today. Our goal is to empower organizations to unleash the power of data and reimagine possibilities that will improve our world. To enable this journey, we are excited to announce the general availability of Azure Databricks, a fast, easy, and collaborative Apache Spark -based analytics platform optimized for Azure.


Azure Databricks, industry-leading analytics platform powered by Apache Spark

#artificialintelligence

This blog post was co-authored by Ali Ghodsi, CEO, Databricks. The confluence of cloud, data, and AI is driving unprecedented change. The ability to utilize data and turn it into breakthrough insights is foundational to innovation today. Our goal is to empower organizations to unleash the power of data and reimagine possibilities that will improve our world. To enable this journey, we are excited to announce the general availability of Azure Databricks, a fast, easy, and collaborative Apache Spark -based analytics platform optimized for Azure.


How to Share and Control ML Model Access with MLflow Model Registry

#artificialintelligence

We are excited to announce new enterprise grade features for the MLflow Model Registry on Databricks. The Model Registry is now enabled by default for all customers using Databricks' Unified Analytics Platform. In this blog, we want to highlight the benefits of the Model Registry as a centralized hub for model management, how data teams across organizations can share and control access to their models, and touch upon how you can use Model Registry APIs for integration or inspection. MLflow already has the ability to track metrics, parameters, and artifacts as part of experiments; package models and reproducible ML projects; and deploy models to batch or real-time serving platforms. Built on these existing capabilities, the MLflow Model Registry [AWS] [Azure] provides a central repository to manage the model deployment lifecycle.


The One Minute AI - Azure Databricks Overview

#artificialintelligence

Welcome to a new series of short articles I am presenting about Artificial Intelligence specifically in the Azure AI stack. The objective is that you will learn about an Azure based AI service in no more than one minute and thus quickly get familiar with the entire stack over a short period of time. These are going short, easily digestible articles so let's get started! What is Azure Databricks Overview?