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Classify Images of Clouds in the Cloud with AutoML Vision

AutoML Vision helps developers with limited ML expertise train high quality image recognition models. Once you upload images to the AutoML UI, you can train a model that will be immediately available on GCP for generating predictions via an easy to use REST API. In this lab you will upload images to Cloud Storage and use them to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.). Set up AutoML Vision AutoML Vision provides an interface for all the steps in training an image classification model and generating predictions on it. Start by enabling the Cloud … Continue reading Classify Images of Clouds in the Cloud with AutoML Vision

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Machine Learning with Spark on Google Cloud Dataproc

In this post you will learn how to implement logistic regression using a machine learning library for Apache Spark running on a Google Cloud Dataproc cluster to develop a model for data from a multivariable dataset. Google Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simple, cost-efficient way. Cloud Dataproc easily integrates with other Google Cloud Platform (GCP) services, giving you a powerful and complete platform for data processing, analytics and machine learning . Apache Spark is an analytics engine for large scale data processing. Logistic regression is available as a … Continue reading Machine Learning with Spark on Google Cloud Dataproc

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Azure Platform for Data Engineers(part-2)

Explore data types: Azure provides many data platform technologies to meet the needs of common data varieties. It’s worth reminding ourselves of the two broad types of data: structured data and nonstructured data. Structured data In relational database systems like Microsoft SQL Server, Azure SQL Database, and Azure SQL Data Warehouse, data structure is defined at design time. Data structure is designed in the form of tables. This means it’s designed before any information is loaded into the system. The data structure includes the relational model, table structure, column width, and data types. Relational systems react slowly to changes in … Continue reading Azure Platform for Data Engineers(part-2)

Azure Platform for Data Engineers(part-1)

Over the last 30 years, we’ve seen an exponential increase in the number of devices and software that generate data to meet current business and user needs. Businesses store, interpret, manage, transform, process, aggregate, and report this data to interested parties. These parties include internal management, investors, business partners, regulators, and consumers. Data consumers view data on PCs, tablets, and mobile devices that are either connected or disconnected. Consumers both generate and use data. They do this in the workplace and during leisure time with social media applications. Business stakeholders use data to make business decisions. Consumers use data to … Continue reading Azure Platform for Data Engineers(part-1)

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Cloud Engineering: Creating a Virtual Machine

Introduction: Google Compute Engine lets you create virtual machines running different operating systems, including multiple flavors of Linux (Debian, Ubuntu, Suse, Red Hat, CoreOS) and Windows Server, on Google infrastructure. You can run thousands of virtual CPUs on a system that has been designed to be fast and to offer strong consistency of performance. Here, you’ll learn how to create virtual machine instances of various machine types using the Google Cloud Platform (GCP) Console and using the gcloud command line. You’ll also learn how to connect an NGINX web server to your virtual machine. You should type the commands to reinforce their … Continue reading Cloud Engineering: Creating a Virtual Machine

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Get To Know About Big Data Analytics

Storing and Accessing Data, Comparison An RDBMS system keeps your table definitions (that is, the schema) in a data dictionary, which is tightly coupled with your tables: it’s always kept in exact alignment, accurately describing the tables you create. This tight coupling also means that the schema governs what is allowed to be stored as data. These systems are called schema on write because the schema is applied before the data is stored. Databases manage all insertions and updates, and they typically throw an error if you try to do something like insert a character string value into a numeric column. If the data doesn’t fit … Continue reading Get To Know About Big Data Analytics

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How to Use AutoML and Vision API in GCP

What is the Vision API and what can it do? The vision API is an API that uses machine learning and other Google services to extract information from images. The sorts of predictions that it can currently make include but are not limited to the following list: Label Detection, which is used to detect the presence of certain broad classes of objects within images Text Detection, which can be used to extract text from images, a process that also is referred to as OCR Safe Search Detection, which can be used to check if an image is safe to serve … Continue reading How to Use AutoML and Vision API in GCP

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Developers Guide into Neo4j(present and future of database)

As a developer, you will create Neo4j Databases, add and update data in them, and query the data. When you learn to use Neo4j as a developer, you have three options⎼ Neo4j Desktop, Neo4j Aura, or Neo4j Sandbox. In this module you will learn how to use each of these development environments and select the option that is best for your needs while you are learning about Neo4j. Many graph-enabled applications have been developed and deployed using Neo4j’s Community Edition (free). If your enterprise requires production features such as failover, clustering, monitoring, advanced access control, secure routing, etc. you will … Continue reading Developers Guide into Neo4j(present and future of database)

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insights from E-Commerce retail data set

We are using Bigquery as our data warehouse solution and using standard SQL as query language . For dataset we use Google’s Google Analytics logs of an merchants website. You need to enable your bigquery account which has a daily limit and there after it is cost effective. Click Navigation menu > BigQuery. Click Done. BigQuery public datasets are not displayed by default in the BigQuery web UI. To open the public datasets project, open in a new browser window. In the left pane, in the Resource section, click data-to-insights. In the right pane, click Pin Project. Explore ecommerce data Problem :  Your data analyst team exported … Continue reading insights from E-Commerce retail data set

BigQuery ML(move your model towards data and not data towards model)

Overview BigQuery ML enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data. BigQuery ML functionality is available by using: The BigQuery web UI The bq command-line tool The BigQuery REST API An external tool such as a Jupyter notebook or business intelligence platform Data Analyst is a Machine learning engineer now? Machine learning on large data sets requires extensive programming and knowledge of ML frameworks. These … Continue reading BigQuery ML(move your model towards data and not data towards model)