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Interaction With Autonomous Database via Docker Container

Docker Container WhaleIn this article, I will show you to access the Autonomous Database service, one of the database services offered on Oracle Cloud infrastructure, through a Docker image. I hope it will be a useful article in terms of awareness.

As we all follow, one of the indispensable components of the application development world is container technologies. Container technologies have long been the main factor that triggers the transformation in the world of application development with the opportunities and advantages it offers. For this reason, software developers continue to build their solutions on containers.

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Classification With XGBoost Algorithm in a Database

In this article, we will look at how to apply the XGBoost algorithm, one of the most popular ensemble learner methods, in a database. Hopefully, it will be a useful study in terms of awareness.

Advanced analytical applications can be developed using machine learning algorithms in Oracle database software since version 9i. As the database versions are renewed, new ones are added to these algorithm options. The current algorithm list that comes with Oracle 19c version is as follows.

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Quick and Easy Configuration of Oracle Data Science Cloud Service

Hello to everyone,

To use some features of Oracle Data Science Cloud Service (to save models, to read basic data about OCI, to establish ADW or Object Storage connections), you need to configure this service when you first turn it on. A description of this configuration is described in the getting-started.ipynb notebook that comes in the service. I prepared a .sh considering that some steps can be automated in this recipe. Through this article, I will explain how to use this .sh quickly.

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Deploying ML Models Using Container Technologies: FnProject

Machine learning is one of the most trending topics of our time. Almost every company and professionals/students related to the IT sector are working in this field and increasing their knowledge level day by day.

As the projects about machine learning start to become widespread, there are more and more innovations about the practices related to how these projects are transferred to production environments. In this article, I will make an example of how to transfer a machine learning model to production in the fastest and most effective way. I hope it will be a useful study in terms of awareness.

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Serverless Apache Spark: Data Flow Cloud Service

Apache Spark is a technology that is very close to becoming the industry standard among distributed big data processing platforms. It is possible to encounter Spark in almost every company working on big data. We can use this technology, which is widely used with the support of performance and many programming interfaces, in our on-premises systems as well as the interfaces opened by cloud providers.

In the past few weeks, Oracle added another one to its cloud services and launched the serverless Spark Execution Engine infrastructure on the Oracle Cloud infrastructure, and this service was designated as Data Flow. Now, users who want to use Spark can easily and quickly raise their Spark Execution Engines and deploy their applications to this environment.

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Oracle Data Science Cloud Service

Oracle launched Data Science Cloud Service recently. This service, which can be used over the cloud, is actually a virtual machine and contains many pieces of open source software. With this service, it is aimed to develop a ready-made environment for developers interested in data science, machine learning and artificial intelligence, and to concentrate only on the tasks they are interested in.

Data Science Cloud interfaceThe interface of the Oracle Data Science Cloud service has the Jupyter Notebook interface that users are accustomed to and includes all the features in local installation.

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Automatic Machine Learning (AutoML) Infrastructure — Oracle Data Science Cloud Service

In this article, I will talk about AutoML, one of the features that come with the Oracle Cloud Data Science Service, and I hope it will be a useful article in terms of awareness.

As it is known and mentioned in my previous articles, Oracle recently added a new service called Data Science to cloud services. This service has been offered to users as a platform where many libraries come pre-installed. This platform, which includes many features like prototype development, project development, model management, to the production of produced models, contains many new features. Undoubtedly, one of the most interesting and useful features is the AutoML feature.

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Connecting an Autonomous Data Warehouse With Python

In this article, I will connect to an Oracle database running in the cloud (Oracle Autonomous Data Warehouse) and make a simple regression application in python environment with a sample data taken from here.

First of all, I will make this application in Autonomous Data Warehouse (DB) which is offered as a service in Oracle Cloud. All I need is an Oracle Cloud account. You can get Autonomous Data Warehouse service, which is one of the services of Always Free (Oracle Free Tier), free of charge and you can provision and use it on the Cloud in minutes without any installation. You can follow the link for detailed information.

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Database Operations on Cassandra and Oracle Using Apache Spark

In this article, I will be doing operations that write and read to Cassandra database using Spark. I hope there will be a useful article in terms of awareness.

The rapid growth of data sources and data volumes has made it difficult to process for collected data. However, the need to process the data has increased. Following these needs and challenges, various solutions have been produced for rapid analysis and storage of big data. Spark is one of the common solutions used to process big data. Cassandra is one of the most widely used databases for storing and questioning big data. Now, we will try to use these two current technologies together.