Digital Badge Program Watson

This credential earner has completed instructor-led learning for administering Watson Knowledge Catalog at an advanced level. The earner can manage user roles, permissions, data connections, data discovery, metadata, catalogs, assets, and governance artifacts, and work with relevant APIs.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of 6XL640G Advanced Administration for IBM Watson Knowledge Catalog (V4.5) instructor-led training course

This credential earner has completed instructor-led learning for intelligent document processing at a deeper level. They can create regular expressions, import rule-based models, customize query results, conduct web crawling, and teach the domain language to Watson Discovery to enhance relevance and accuracy.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L152G Advanced Intelligent Document Processing with IBM Watson Discovery instructor-led training course

The successful badge earner can outline basic NLP tasks and language models, define rule-based models and machine learning models in NLP, and define information retrieval and information extraction. The earner can apply smart document understanding, modify display of query results, and show how to improve the accuracy of query results.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L148G Basics of Intelligent Document Processing with IBM Watson Discovery instructor-led training course

This credential earner has completed instructor-led learning for Building Advanced Intelligent Virtual Agents in IBM watsonx Assistant. The earner can explain the functions and architecture of watsonx Assistant, create new actions, upload existing actions, edit them, download as backup, and debug existing actions.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L165G Building Advanced Intelligent Virtual Agents in IBM watsonx Assistant instructor-led training course

This credential earner has completed instructor-led learning for Building Intelligent Virtual Agents with IBM Watson Assistant. The earner can plan and build a virtual agent, create basic and advanced conversational flows that fulfill users’ requests, leverage out-of-the-box artificial intelligence features, and tie into analytics dashboards for monitoring and troubleshooting.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L167G Building Intelligent Virtual Agents with IBM Watson Assistant instructor-led training course

This credential earner has completed instructor-led learning for Creating Voice Interfaces with Watson Speech to Text and Text to Speech. The earner can explain the underlying science behind the technology of Watson Speech to Text and Text to Speech as developed by IBM, leverage the API methods for calling speech services, customize, and deploy speech prototypes to suit a unique domain language, and integrate voice capabilities to an existing IBM Watson Assistant agent.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L162G Creating Voice Interfaces with Watson Speech to Text and Text to Speech instructor-led training course

The successful badge earner can use the tools and services from IBM Watson Studio to build, deploy, and test machine learning models. The earner can access the relevant data sets, build machine learning models by using various editors and graphical user interfaces, deploy the models, and test them for correct operation. The earner can also be able to share the assets with colleagues.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L149G Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio instructor-led training course

The successful badge earner can use the tools and services from IBM Watson Studio to build, deploy, and test machine learning models. The earner can access the relevant data sets, build machine learning models by using various editors and graphical user interfaces, deploy the models, and test them for correct operation. The earner can also be able to share the assets with colleagues.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L549G Creating, Testing, and Deploying Machine Learning Models with IBM Watson Studio V4.8 instructor-led training course

This credential earner has completed instructor-led learning for Enterprise Catalog Management and Data Protection with Watson Knowledge Catalog on IBM Cloud Pak for Data 4.0.x. The earner can access Watson Knowledge Catalog, create catalogs, populate them with assets, assess asset quality, and manage the assets in the catalog through a governance framework.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of 6XL534G Enterprise Catalog Management and Data Protection with Watson Knowledge Catalog on IBM Cloud Pak for Data 4.0.x instructor-led training course

This badge earner has demonstrated their ability to use IBM watsonx.ai to transform large amounts of raw, synthetic data into consumable, high-quality information that is ready for analytics, or prompt tuning foundation models.

Course Objectives

Badge Criteria and Activities

Complete the following course:

This badge earner has gained more experience with IBM watsonx.data 2.0.2. They have developed knowledge in areas such as infrastructure management, data management, and running SQL statements. The earner has demonstrated skills in ingesting data, creating schemas, running queries that combines data from multiple data sources, and rolling back a table to a previous point in time.

Course Objectives

Badge Criteria and Activities

IBM watsonx.data 2.0.2: Modernize Your Data Warehouse and Streamline Data Analytics

This credential earner has completed instructor-led learning for IBM watsonx.data on IBM Cloud Pak for Data 4.8: Modernize Your Data Warehouse and Streamline Data Analytics. The earner can use the watsonx.data console to collect, store, query, and analyze data, extend the functions of watsonx.data, manage infrastructure access and data access policies, use Presto watsonx.data to run SQL queries, and create different database connections to migrate data to watsonx.data.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of 6XL946G IBM watsonx.data on IBM Cloud Pak for Data 4.8: Modernize Your Data Warehouse and Streamline Data Analytics instructor-led training course

This badge earner can work with IBM watsonx.governance to develop a predictive machine learning model from data, track its lifecycle from development to production in an AI use case, capture its details in Factsheets, and evaluate for fairness using Watson OpenScale.

Course Objectives

Badge Criteria and Activities

Complete the following course:

This credential earner has completed instructor-led learning for Watson Assistant, focusing on actions that are built with steps. They can distinguish among agents, explain the capabilities of Watson Assistant, and summarize the basics of planning and building an intelligent virtual agent.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L151G Introduction to intelligent virtual agents with IBM Watson Assistant instructor-led training course

The successful badge earner can distinguish between supervised and unsupervised machine learning, define deep learning and reinforcement learning, and demonstrate the basic functions of Watson Studio for machine learning. The earner can list the model development steps, develop a Random Forest model, and evaluate the results.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L160G Learn the Basics of Machine Learning with IBM Watson Studio instructor-led training course

This badge holder has demonstrated understanding of the core features and functionality of Watson Explorer Analytical Components. Recipient is able to use the content analytics, annotator, and content mining functionality. This primary functionality is found in an Analytics collections crawling, parsing, indexing, annotating, and searching components.

Course Objectives

Badge Criteria and Activities

Badge earners have successfully achieved class objectives demonstrated by progress in lab exercises, attendance, participation in question & answer sessions, and/or assessments associated with:

This credential earner has completed instructor-led learning for Mastering Document Retrieval with IBM Watson Discovery API. The earner can build sophisticated document retrieval systems by using the Watson Discovery API.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L163G Mastering Document Retrieval with IBM Watson Discovery API instructor-led training course

This credential earner has completed instructor-led learning for Use IBM Watson APIs to Get Structured Data from Unstructured Text and Voice. The earner can extract business insights from large amounts of unstructured voice data, store structured results in a tabular form, and use them to extract business insights.

Course Objectives

Badge Criteria and Activities

Attendance and successful completion of W7L156G Use IBM Watson APIs to Get Structured Data from Unstructured Text and Voice instructor-led training course

This badge earner understands how to leverage Watson Discovery to explore, analyze, and query their documents at an advanced Level. They understand how to ingest documents, integrate domain knowledge into their documents through custom enrichments, build a custom Entity Extractor, fine-tune document queries, and combine Watson Discovery with watsonx Assistant for conversational search.

Course Objectives

Badge Criteria and Activities

Watson Discovery: Advanced Intelligent Document Processing

This badge earner understands how to leverage Watson Discovery’s API to explore, analyze, and query their documents at an advanced Level. They understand how to ingest documents, integrate domain knowledge into their documents through custom enrichments, fine-tune document queries, and extract their query results programmatically.

Course Objectives

Badge Criteria and Activities

Watson Discovery: Mastering Document Retrieval with Watson Discovery API

This badge earner has demonstrated they can solve RAG and other use cases, by using the tools and frameworks available in watsonx.ai, such as prompt engineering, prompt tuning and LangChain.

Course Objectives

Badge Criteria and Activities

Complete the instructor-led course W7L171G - IBM watsonx.ai with RAG and LangChain.