Cursos de IBM Cloud

This course provides technical professionals with the needed skills to administer IBM Cloud Pak for Business Automation. The course begins with an introduction to IBM Cloud Pak for Business Automation (CP4BA). You manage platform security for CP4BA and work with Business Teams. You explore the Red Hat OpenShift Container Platform (RHOCP) web console for container management such as Cluster Inventory, Events, CPU, and Memory. You examine the available observe options such as Metrics, Alerts, and Dashboards. You scale the application deployment. You customize the Cloud Pak Platform UI. To troubleshoot common Cloud Pak cluster problems, you get logs for nodes, cluster, and pods. You view the logs for pods by running commands and examine the logs in the RHOCP console. You learn how to troubleshoot the Container Engine and the pods that fail to start. The course concludes with the collection of MustGather files to submit it to the IBM Support team for troubleshooting. 

This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning.

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.  After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.

This course offers solution architects, data stewards, and data quality analysts more experience with IBM Knowledge Catalog for IBM Cloud Pak for Data. You learn to tune and customize governance workflows and artifacts to enhance the quality, findability, and protection of data. You gain skills in enriching metadata, building data quality rules, customizing asset properties, and configuring reports and dashboards.

This course offers solution architects, data stewards, and data quality analysts more experience with IBM Knowledge Catalog for IBM Cloud Pak for Data. You learn to tune and customize governance workflows and artifacts to enhance the quality, findability, and protection of data. You gain skills in enriching metadata, building data quality rules, customizing asset properties, and configuring reports and dashboards.

This learning offering tells a comprehensive story of Cloud Pak for Data, and how you can extend the functions with services and integrations. You explore some of the services, and see how they enable effective collaboration across an organization. In this course, you use Watson Knowledge Catalog, Watson Query, and Watson Studio (including Data Refinery and AutoAI). You also examine some of the external data sets and industry accelerators that are available on the platform.

This course offers solution architects, data stewards, and data quality analysts more experience with IBM Knowledge Catalog for IBM Cloud Pak for Data. You learn to tune and customize governance workflows and artifacts to enhance the quality, findability, and protection of data. You gain skills in enriching metadata, building data quality rules, customizing asset properties, and configuring reports and dashboards.

This course provides Solution Architects an introduction to the basics of IBM Knowledge Catalog on IBM Cloud Pak for Data. You learn to access IBM Knowledge Catalog through the service, and gain skills in creating catalogs, populating them with assets, managing the assets in the catalog through a governance framework, and governing virtualized data. 

This course is designed for professional administrators of Cloud Pak for Data, and it is intended for practicing the administration skills. The purpose of this course is to explain the most important administration activities that are related to Cloud Pak for Data environment. From the prepared Exercise Guide, you learn step by step how to perform each task. You verify the acquired knowledge by completing the designated hands-on exercises.  In this course, you follow Emily, the Cloud Pak for Data administrator in a fictional financial services corporation and you learn how to perform administration tasks.

This course offers data engineers more experience with watsonx.data on Red Hat OpenShift Container Platform. You gain knowledge in areas such as user and group management, access controls within watsonx.data, integrating IBM Knowledge Catalog with watsonx.data, and what the integration with IBM Knowledge Catalog provides. You gain skills in access control, IBM Knowledge Catalog policies, and how to manage watsonx.data after integration with IBM Knowledge Catalog is in place. The course is designed to build upon the watsonx.data intermediate course and goes into more depth in the data governance area of watsonx.data. Integration between watsonx.data and IBM Knowledge Catalog allows you to manage data within the Data Lakehouse and other data sources with a single data governance product. This integration ensures that all data is governed within a centralized data governance product.

This course offers data engineers more experience with watsonx.data on Red Hat OpenShift Container Platform. You learn to gain knowledge in the areas such as infrastructure management, data management, running SQL statements. You gain skills in ingesting data, creating schemas, running queries that combines data from multiple data sources, and rolling back a table to previous point in time. 

This course offers solution architects more experience with IBM Knowledge Catalog for IBM Cloud Pak for Data. You learn to tune and customize governance workflows and artifacts to enhance data protection, quality, and findability. You gain skills in enriching metadata, building data quality rules, customizing asset properties, and governing virtual data.

Installing IBM Cloud Pak for Data on-premises for the first time can be a daunting task. This course aims to demystify and streamline that process for learners new to the Cloud Pak for Data ecosystem. This course follows Emily, a Cloud Pak for Data administrator. Emily is assigned to install the Cloud Pak for Data platform on a Red Hat OpenShift Container Platform cluster that has been provisioned to her by the Red Hat OpenShift administrator. Emily is new in the world of Cloud Pak for Data and has never done this before. Throughout this course, Emily plans out the installation of IBM Cloud Pak for Data and additional services, prepares the cluster, and installs the IBM Cloud Pak for Data platform. Later, Emily also upgrades IBM Cloud Pak for Data 4.8.x to a newer version. Together with Emily, you learn step-by-step how to perform each task in this course. Ultimately, you also verify your newly acquired knowledge in a series of hands-on exercises performed in a custom-made course lab environment as well as in a final assessment. 

This learning offering tells a comprehensive story of Cloud Pak for Data, and how you can extend the functions with services and integrations. You explore some of the services, and see how they enable effective collaboration across an organization. In this course, you use IBM Knowledge Catalog, Watson Query, and Watson Studio (including Data Refinery and AutoAI). You also examine some of the industry accelerators that are available on the platform.

This course provides Solution Architects an introduction to the basics of IBM Knowledge Catalog for IBM Cloud Pak for Data. You learn to access IBM Knowledge Catalog through the service, and gain skills in creating catalogs, populating them with assets, and then managing the assets in the catalog through a governance framework.

This course helps you explore the IBM Cloud Pak for Data monitoring and alerting framework. In this course, you customize alert forwarding, configure the monitoring stack so that it sends Cloud Pak for Data metrics to Prometheus.  You perform routine Cloud Pak for Data monitoring, which leads to investigating and resolving issues in your environment. You also deploy scripts that use platform APIs, and enhance the monitoring feature by introducing custom monitors.

This learning offering tells a comprehensive story of Cloud Pak for Data, and how you can extend the functions with services and integrations. You explore some of the services, and see how they enable effective collaboration across an organization. In this course, you use Watson Knowledge Catalog, Watson Query, and Watson Studio (including Data Refinery and AutoAI). You also examine some of the external data sets and industry accelerators that are available on the platform.