This course guides you through the most important administration activities that are related to the Cloud Pak for Data environment.  You will recall the infrastructure of a Red Hat OpenShift cluster where Cloud Pak for Data runs, and you will learn how to manage this cluster. You will learn about multitenancy, tethered projects, the Cloud Pak for Data installation procedure, and prerequisites for various Cloud Pak for Data installation scenarios. A significant part of this course refers to tasks that an administrator must complete, including setting up an LDAP connection for user and group management, defining resource quotas and limits, and scaling services.  

Advanced Document Search with IBM Watson Discovery explores intelligent document processing at a deeper level. You learn how to create regular expressions, import rule-based models, customize query results, conduct web crawling, and teach the domain language to Watson Discovery to enhance the relevance and accuracy of your results.

This course introduces you to common NLP tasks, Smart Document Understanding, customization of the query results, teach the domain language to Watson Discovery to improve the relevancy and accuracy of the results.

Watson Discovery is a cognitive search and content analytics engine that you can add to applications to identify patterns, trends, and actionable insights to drive better decision-making.

This course takes the data scientist (or business analyst) on a journey from the creation of several machine learning models to its deployment and testing. Various tools and services as well as programming and graphical user interfaces are used in the process. The course ends with the sharing of assets on GitHub and a brief discussion on governance and stewardship. 

This learning offering will tell a holistic story of Cloud Pak for Data including collaboration across an organization, which is key in this platform. Applicable to all personas. Multiple use cases will provide understanding of how organizations can benefit from Cloud Pak for Data. A variety of features will also be explored, providing students with the insight on how to use the platform. This learning is relevant for Cloud Pak for Data and Cloud Pak for Data System.

The course takes the learner through the journey of learning about Kubernetes and OpenShift as the platform for Cloud Pak for Data. The course focuses on the Day 2 Operations of managing the Cloud Pak for Data platform once it has been installed. The course goes through the administration of the OpenShift web console, the Cloud Pak for Data web client and the cluster, including scaling services, backup and restore, and disaster recovery. Additional Day 2 Operations include troubleshooting the components of the cluster, monitoring, and metering.  The scope of this course is the administrative capabilities of Cloud Pak for Data and the related OpenShift capabilities required to perform those functions.  For deeper learning on OpenShift administration, refer to the Red Hat OpenShift Administration course.  

NOTE: This offering is part of the Digital Learning Subscription and provides the learner with the course content as soon as they are developed. The course is divided into units, which are released as soon as they are ready allowing for immediate learning. The learner can subscribe to the course forum to be notified when a new unit is released.

This offering provides analysts with an introduction to create embedded dashboards in Cloud Pak for Data, as well as dashboards and reports using the Cloud Pak for Data's Cognos Analytics service.

This course is designed to teach data scientists, Operational Research specialists, and business analysts how to build, run and deploy basic optimization models using IBM Decision Optimization for IBM Cloud Pak for Data. You will learn how to create projects to manage your resources, use the user-friendly Model Builder interface to build, run and visualize models, use different methods to formulate models, including using the Modeling Assistant for formulate your model in natural language. You will also learn how to import and use python notebooks to create your models, and how to deploy the solution to end users within Cloud Pak for Data.

This course is designed to teach data scientists, Operational Research specialists, and business analysts how to build, run and deploy basic optimization models using IBM Decision Optimization on IBM Cloud Pak for Data. You will learn how to create projects to manage your resources, use the user-friendly Model Builder interface to build, run and visualize models, use different methods to formulate models, including using the Modeling Assistant for formulate your model in natural language. You will also learn how to import and use python notebooks to create your models, and how to deploy the solution to end users within Cloud Pak for Data.

SPSS Modeler is available on IBM Cloud Pak for Data V2.5. This course reviews the basics of how to import, explore, and prepare data, and introduces the student to machine learning models with SPSS Modeler on Cloud Pak for Data.

This offering provides analysts an introduction to the basics of IBM Watson Knowledge Catalog for IBM Cloud Pak for Data. Learners will be introduced to the Cloud Pak for Data product layout and functionality and the role of services within the product. They will learn to access the Watson Knowledge Catalog through the service, and gain skills in creating catalogs, populating them with assets, and then managing the assets in the catalog.

This course goes through the stages of a data science project from importing data to deployment, using services in Watson Studio and Watson Machine Learning for Cloud Pak for Data.

This course provides guidelines to prepare your environment for IBM Cloud Pak for Data and to successfully deploy the software. From the attached demonstrations, you will learn step by step how to perform each task. You will verify the acquired knowledge by completing the designated hands-on exercises.

This course introduces a case study, data set, machine learning concepts, and developing a machine learning model with Watson Studio.

Initially, you will be introduced to the case study and the challenges company facing, and the company data set. Next, you will be introduced to supervised, unsupervised learning, deep and reinforcement learning algorithms. Finally, you will develop a supervised machine learning model IBM Watson Studio with the dataset provided using Python.

In this course, you learn about common issues that can occur when working as a Cloud Pak for Data administrator. You learn how to troubleshoot them, and verify your acquired knowledge by completing the corresponding hands-on exercises.

These scenarios are general recommendations that might differ depending on your environment, workload, and installed services.