Data and artificial intelligence are two of the most prominent terms in this day and age. The rapid development of the technology surrounding data and AI has changed how we approach problems related to them. One of the best ways to deal with AI and data is with the help of cloud computing. Thanks to cloud computing, we can easily simplify a majority of tasks.
One such fabulous cloud computing technology that offers the best solutions to dealing with both AI and data lakes is IBM Cloud. In this article, we will cover the basics of how to get started and comfortable with the IBM cloud environment and how to perform numerous applications and utilities. Some of the options available on IBM Cloud allow users to develop high-quality projects with ease. Let us begin by exploring these features and characteristics from scratch.
Clicking the ‘create resource’ menu option will direct you to the following page. Here, you can select resource options like Cloud Foundry or the Watson Studio for performing your required functions and operations.
I will quickly demonstrate how you can set up your Watson Studio. Click on the icon as shown below to enter the next step of your creation process.
Click on Watson Studio, and you will be asked to choose your preferred plan depending on your monetary limits. I have chosen the free pricing option, which is suitable for most simple tasks. It is a great starting point for first-time users of the IBM Cloud services.
However, we will use the IBM Cloud Command Line Interface (CLI) to perform these functions and build our data lake and object storage with Jupyter Notebooks and Watson Studio directly.
Now, with the help of IBM Cloud’s object storage, data lakes, and Jupyter Notebooks, I will demonstrate how to perform the operations of data lakes and AI. We can utilize object storage and data lakes to store large quantities of information as raw data files. Once we link the query data to the object storage with SQL queries, we can implement the refining and analysis of the data in the Jupyter Notebooks of the IBM Watson Studio.
Below are some code samples to get started with working on AI and data lakes. In the first few code blocks, we are implementing certain essential services to get started with the various functionalities.
ibmcloud target –cf
Let us now initialize all the default groups for the working process.
ibmcloud resource groups
ibmcloud target -g <your-default-resource-group>
The next few steps include creating all the necessary elements for operating on data lakes, object storage, and the Jupyter Notebooks. The first command will create the data lake in the cloud with the object storage features. The second command will create additional SQL queries to operate on the data. Finally, the third command will create the IBM Watson Studio.
ibmcloud resource service-instance-create data-lake-cos cloud-object-storage lite global
ibmcloud resource service-instance-create data-lake-sql sql-query lite us-south
ibmcloud resource service-instance-create data-lake-studio data-science-experience free-v1 us-south
After this, you can work on uploading and working data elements by combining SQL queries with Jupyter Notebooks.
In this article, we have discussed the high potential that IBM Cloud holds for solving problems related to AI and data science. It offers numerous tools and technologies to users to explore and build high-level projects. The best part about working with IBM Cloud is that there are many tutorial guides, certifications, and resources available to each individual to benefit from and utilize.
For further information and reading, I recommend checking out a few article resources. You can check out articles on working with object storage and Jupyter Notebooks to gain more insight into how you can handle data objects. I also highly recommend checking out the reference article on the IBM Cloud Data Lake website to understand this topic better. You can also check out an IBM Cloud YouTube series.