IBM AI Courses

The Event Manager component of IBM Cloud Pak for Watson AIOps is a carrier-class service assurance system. It collects and consolidates events and alarms from a wide variety of IT environments in real time. These include servers, mainframes, Windows systems, applications, circuit switches, voice switches, IP routers, SNMP devices, network management applications, existing management systems and frameworks, among many others. IBM Cloud Pak for Watson AIOps Event Manager also adds intelligence to your events, allowing you to cast a wide net to ingest relevant data from any source, process it in an intelligent, automated way, analyze the data, see which applications or parts of the infrastructure are impacted, share it and even suggest guided steps to mitigate or resolve issues automatically.One key benefit of Event Manager's machine learning features is a reduction in the number of events. By detecting, correlating, grouping, and suppressing the 'noise' that IT systems generate, your operators can focus their attention on key events that represent actual problems. This 2-day course teaches you how to configure IBM Cloud Pak for Watson AIOps v3.2 Event Manager for productive use. Through hands-on lab activities, you learn how to configure a new installation of Event Manager to:Connect to event sources and enrich incoming eventsApply machine learning to find relationships among eventsAdd topology data to identify groups of connected resources and calculate root causeCreate automated fixes for known problems and match them to incoming problem eventsYou also get hands-on practice with other common configuration tasks, such as user management and database customization. This course focuses on IBM Cloud Pak for Watson AIOps Event Manager running on the Red Hat OpenShift Container Platform.

The IBM Cloud Pak for AIOps platform delivers visibility into performance data and dependencies across environments. It aims to enhance the efficiency of IT operations, reduce costs, and ensure that systems are robust and secure. This course provides instructions on how to do an air-gapped installation of IBM Cloud Pak for AIOps v4.3

This course introduces a case study, dataset, common machine learning algorithms, and developing a machine learning model with IBM Watson Studio.In the first module, you will be introduced to Amsel Fit, our case study, a fictional company that produces dietary products, supplements, and healthy foods. The company faces a drop in sales and decides to analyze its marketing approach and predict which customers will or will not be likely to continue buying products. Also, you will be introduced to the dataset that we will be using to develop a machine learning model. In the next module, you will be introduced to machine learning models including supervised, unsupervised learning that include classification and regression models, deep learning and reinforcement learning approaches.In the third module, based on module one and module two, you will develop a supervised machine learning model with the dataset provided to predict which customer will buy or will not buy again after a coupon is provided.

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.

In this course, the learner is guided through a realistic data science project. The project focuses on extracting business insights from large amounts of unstructured voice data, by using IBM Watson APIs. Starting from voice recorded customer reviews, the data are transcribed by using Watson Speech to Text and then are analyzed for emotion and keywords by using Watson Discovery. The structured results are stored in a tabular form and used to extract business insights in Watson Studio. The course focuses on hands-on coding exercises that provide an opportunity to work with Watson Studio, Watson Discovery, and Watson Speech to Text. The exercises guide the learner on importing data and manipulating in a Watson Studio project, activate instances of installed services, and authenticate API connections by using Jupyter Notebooks. The exercises also introduce how to configure Watson Speech to Text and create custom language models, work with Watson Discovery, and introduce enrichments. Also, every exercise demonstrates suitable data manipulation that results in saved structured data. The three hands-on exercises are included in this course, each focusing on a Jupyter Notebook and correspond to the three modules of the course. The necessary data, notebooks, and services are all available in the corresponding lab environment.

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.

IBM Cloud Pak for AIOps helps in innovating faster, reducing operational costs, and transforming IT operations (ITOps) across a changing landscape by providing a platform that delivers visibility into performance data and dependencies across environments. Embrace artificial intelligence, machine learning, and automation to help ITOps managers and Site Reliability Engineers (SREs) address incident management and remediation.This course is intended for help desk personnel and operators who use IBM Cloud Pak for AIOps (v4.5.1) to view, prioritize, and resolve alerts in their IT environment. Alerts viewer helps the operators in viewing the relationship between alerts in terms of time correlation, associated alerts on other devices, and historical patterns. Alerts viewer also helps in viewing the topological context of an alert and how topology changes in a time frame that includes the alert. 

The Event Manager component of IBM Cloud Pak for Watson AIOps is a carrier-class service assurance system. It collects and consolidates events and alarms from a wide variety of IT environments in real time. These include servers, mainframes, Windows systems, applications, circuit switches, voice switches, IP routers, SNMP devices, network management applications, existing management systems and frameworks, among many others. IBM Cloud Pak for Watson AIOps Event Manager also adds intelligence to your events, allowing you to cast a wide net to ingest relevant data from any source, process it in an intelligent, automated way, analyze the data, see which applications or parts of the infrastructure are impacted, share it and even suggest guided steps to mitigate or resolve issues automatically.One key benefit of Event Manager's machine learning features is a reduction in the number of events. By detecting, correlating, grouping, and suppressing the 'noise' that IT systems generate, your operators can focus their attention on key events that represent actual problems. This 2-day course teaches you how to configure IBM Cloud Pak for Watson AIOps v3.2 Event Manager for productive use. Through hands-on lab activities, you learn how to configure a new installation of Event Manager to:Connect to event sources and enrich incoming eventsApply machine learning to find relationships among eventsAdd topology data to identify groups of connected resources and calculate root causeCreate automated fixes for known problems and match them to incoming problem eventsYou also get hands-on practice with other common configuration tasks, such as user management and database customization. This course focuses on IBM Cloud Pak for Watson AIOps Event Manager running on the Red Hat OpenShift Container Platform.

This course teaches you how to use the Event Manager component of IBM Cloud Pak for AIOps v3.2 to view, categorize, understand, and resolve alarm conditions in your IT event to reduce the mean time to resolve (MTTR) those issues.

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.