What can we say about the IBM Cloud environment? What makes it unique and so exciting? To start, the IBM Cloud platform (formerly known as Bluemix) actually combines platform as a service (PaaS) with infrastructure as a service (IaaS) and, in addition, offers a collection or catalog of cloud services that you can integrate with PaaS and IaaS to efficiently build individual, enterprise, or even global innovative applications.

Some other key points on the IBM Cloud (from a developer perspective) include the following:

• You can develop in what is called a cloud without borders, which means that you can connect private services to the public IBM Cloud services that are available from IBM
• You can expect to see a continuously growing number of services and runtime frameworks being added and becoming available to you
• You can take an idea from start, to development...

Let's get started! To access the IBM Cloud, you can go to https://www.ibm.com/cloud where you will arrive at the Cloud sign-up/log-in page, which is shown in the following screenshot:

If you click on the blue Cloud sign-up/log-in link, you can create a free account (using your IBM ID) or log in (once you have set up your paid or free account).

After your ID has been authenticated, you will arrive at the IBM Cloud main page or the console, which is shown in the following screenshot:

The IBM Cloud console is the user interface that you use to manage all of your IBM Cloud resources. When you access the console, you can manage your account, log in, access the documentation, access the catalog, view the pricing information, get support, or check the status of IBM Cloud components.

After you log in, the menu bar contains a Navigation Menu icon and...

Through the IBM Cloud dashboard or console, you can view and work with IBM Cloud resources and Cloud Foundry service instances. We'll dive deeper into these in later chapters, but for now, you can think of a resource as anything that can be created, managed, and contained within a resource group. Some examples include apps, service instances, container clusters, storage volumes, and virtual servers.

In the following screenshot, you'll see the top portion of the console:

Rather than walking you through the entire list of functions and features here, we are going to focus on the areas of the cloud that will help jumpstart our ML project development. The IBM Cloud offers neat developer dashboards (accessible from the Navigate Menu icon in the upper left of the console page), each focusing on a different area of interest (such as Watson, Security, or Finance...

Selecting Watson from the list of starter kits offered opens the Build with Watson page, the upper portion of which is shown in the following screenshot:

This page is organized into sections, namely Overview, Starter Kits, Watson Services, Developer Resources, and Apps. If you click on Watson Services, then Browse Services, the current Watson-related services that are offered are presented as panels, as shown in the following screenshot:

At this point, we need to find a service instance we want to use. To select a service offering, you can click on the panel that you are interested in (for example, Machine Learning). To make sure that this is the offering you want, you can click on Details and read through the service description, including its features list and other relevant information (as shown in the following screenshot...

To build Watson-based projects, you'll want to access the IBM Watson Studio.

IBM Watson Studio is an integrated environment designed to make it easy to develop, train, and manage models, as well as deploy AI-powered applications. You can use the neural network modeler and deep learning experiments in Watson Studio to solve the most challenging and computationally intensive problems with clarity and ease. You'll need your IBM ID to set this up.

There are currently three versions of Watson Studio, namely Cloud, Desktop, and Local, each offering a solution based on where you want to perform your work:

• Watson Studio Cloud: Your models reside in the IBM public cloud
• Watson Studio Desktop: Your models reside...

Once the setup is done, you'll find that the Watson Studio Cloud format is similar to other IBM sites we've worked with (IBM Watson Analytics and the Cloud console, for example):

All of the versions of Watson Studio provide you with the environment and tools to solve your business problems by collaboratively working with data. You can choose the tools you need to analyze and visualize data, cleanse and shape data, ingest streaming data, or to create, train, and deploy ML models.

The architecture of Watson Studio is centered around the concept of a project. A project is where you organize your resources and work with data. The most important resources...

Simply put, to review what we already went over, to set up our first new project, we need to click on the IBM Watson link in the header area of the main page to navigate to the IBM Watson Studio home panel. We will then perform the following steps:

1. Click on New Project.

2. Choose a project type:
   • If you want to train complex neural networks using experiments, choose a Deep Learning project
   • For all other machine learning work, choose the Modeler project type

3. If you don't have any of the required services already, such as Watson Machine Learning and IBM Cloud Object Storage, new service instances are created.

At this point, we could dig further into each of the administrative areas of IBM Watson Studio, but the objective of this book is more outcome oriented and hands-on, so perhaps the best next step is to have a look at a small but concrete...

Let's get started with a simple project!

As always, you begin any project with a statement of the project's objectives. In this project, the objective is to use a simple text file (TripRuns.csv), which contains results from a professional driving services organization, to show how easy it is to create a simple visual analytics dashboard with IBM Watson Studio.

In this chapter, we got started by introducing the IBM Cloud environment and how to access it for the first time. In addition, we used the IBM Watson console to illustrate how a project is created.

Finally, we reviewed tasks such as adding a new dataset asset to a project and creating a simple analytics dashboard with our data in minutes, without any need for programming.

In the next chapter, we will look into feature extraction.