Microsoft to Retire AI-3022 Azure AI Search Instructor-Led Course in 2026, No Replacement Planned

What Changed: Microsoft Retires AI-3022 Instructor-Led Course

Microsoft announced on May 8, 2026, that instructor-led course AI-3022, titled Implement knowledge mining with Azure AI Search, will retire on May 29, 2026. The course is part of Microsoft's Applied Skills credential program, designed to validate hands-on proficiency in specific Azure workloads. No direct replacement course has been announced, a departure from Microsoft's typical practice of updating training content to align with product changes.

The course covers end-to-end implementation of knowledge mining workloads using Azure AI Search, formerly known as Azure Cognitive Search. Knowledge mining refers to the process of extracting structured insights from unstructured content such as PDFs, images, videos, and emails, then making that data searchable and actionable for business users. Typical modules include configuring data sources, defining skill sets to process content, creating indexes, and building query solutions for enterprise search scenarios.

The linked self-paced learning path for this course remains accessible at Implement knowledge mining with Azure Cognitive Search as of the announcement date, but the instructor-led delivery format, which includes live instruction, hands-on labs, and instructor feedback, will be discontinued permanently after the retirement date. Microsoft notes that newly announced course retirements may take time to appear on the central Course retirement | Microsoft Learn page, so learners should track announcements via the ILT Communications Blog for real-time updates.

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Microsoft attributes the retirement to the broader evolution of the Azure AI product portfolio. The company states that focus has shifted toward generative AI, multimodal experiences that process text, image, and audio inputs together, and more integrated application patterns that reflect how modern cloud solutions are built. Standalone knowledge mining is no longer a priority workload, as Microsoft now positions Azure AI Search primarily as a component of larger generative AI systems, such as retrieval-augmented generation (RAG) pipelines, rather than a standalone enterprise search tool.

Provider Comparison: How Cloud Vendors Handle Training Retirement

This retirement stands out when compared to the training update practices of other major cloud providers, including AWS and Google Cloud. All three vendors regularly retire training content when underlying services are deprecated, updated, or when product priorities shift, but their approaches to replacement content differ significantly.

AWS maintains a public Course Retirement page that lists all upcoming retirements 6 to 12 months in advance, with direct links to replacement courses whenever possible. For example, when AWS deprecated Amazon Lex V1 in favor of Lex V2, the company retired all V1-related instructor-led and self-paced courses, then launched updated V2 courses that cover the same core competencies with new product features. AWS rarely retires a course without a direct replacement, as the company prioritizes minimizing disruption for learners and enterprise customers with existing upskilling plans.

Google Cloud follows a similar pattern, with a dedicated Training Course Retirement page that provides retirement dates, replacement course recommendations, and migration guidance for labs and credentials. When Google updated its Vertex AI offering to prioritize generative AI workloads, it retired legacy machine learning training courses and launched a new Generative AI on Google Cloud learning path that integrates updated search, model training, and deployment content. Google typically bundles retired course content into newer, broader learning paths rather than leaving learners without a structured alternative.

Microsoft's decision to retire AI-3022 with no replacement is unusual for the cloud training market. While Microsoft has retired courses without replacements in the past for deprecated services, Azure AI Search is not a deprecated service, it remains a core component of the Azure AI portfolio. The retirement instead reflects a strategic decision to stop training customers on legacy implementation patterns for a supported product. This creates a gap for learners who prefer instructor-led training for Azure AI Search knowledge mining, as they will need to rely on self-paced content, third-party training, or piece together relevant modules from Microsoft's newer generative AI courses, such as the Implement RAG with Azure AI Search learning path, which covers Azure AI Search in the context of modern gen AI apps.

Business Impact: What Teams and Organizations Need to Do

The retirement affects three key groups: individual learners, enterprise IT teams, and cloud solutions providers advising Azure customers.

Individual learners who have already enrolled in AI-3022 sessions scheduled before May 29, 2026, can complete the course and earn the Applied Skills credential as planned. Learners who have not yet enrolled but intended to take the course must register for a session before the retirement date, as no new sessions will be added after that date. The Applied Skills credential associated with AI-3022 will no longer be available after retirement, so learners pursuing this specific validation of knowledge mining skills must complete all requirements before May 29, 2026. The self-paced learning path will remain available, but it does not include the live instruction or proctored lab components of the instructor-led course, and it does not award the Applied Skills credential.

Enterprise IT teams with upskilling plans that include AI-3022 need to audit their training calendars immediately. Teams that have purchased bulk ILT seats for the course should contact their Microsoft training provider or Microsoft support to request refunds for unused seats, or transfer credits to other Azure AI instructor-led courses. For teams that require instructor-led training on Azure AI Search, third-party training providers such as Pluralsight, A Cloud Guru, and Global Knowledge may offer equivalent content, though these courses will not align with Microsoft's official Applied Skills credential.

Organizations running production knowledge mining workloads on Azure AI Search should view this retirement as a signal to review their implementation patterns. Microsoft's shift away from dedicated knowledge mining training indicates that the company will prioritize development and support for gen AI-integrated search patterns over legacy standalone knowledge mining features. Teams using older skill sets, custom extractors, or on-premises data source connectors that are not part of the new multimodal, integrated app patterns should plan to migrate their workloads to supported configurations to avoid future compatibility issues. This may involve refactoring search solutions to integrate with Azure OpenAI Service for RAG, adding multimodal processing for image and audio content, or adopting managed skill sets that align with Microsoft's current product roadmap.

For cloud consultants advising clients on Azure AI strategy, this retirement reinforces the need to align client workloads with Microsoft's long-term product priorities. Recommending legacy implementation patterns for Azure AI Search after May 2026 will expose clients to unplanned migration costs and skill gaps, as instructor-led training for those patterns will no longer be available. Consultants should instead guide clients toward modern, gen AI-integrated search architectures, and adjust upskilling recommendations to prioritize Microsoft's newer generative AI learning paths, which include updated Azure AI Search content that aligns with current product direction.

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Key Takeaways for Azure AI Users

• Complete all AI-3022 instructor-led sessions and associated credential requirements before May 29, 2026, to avoid losing access to the Applied Skills validation.
• Use the self-paced Implement knowledge mining with Azure Cognitive Search learning path for self-directed study, but note that it does not offer instructor-led support or official credentials after the course retires.
• Compare training options across cloud providers if your organization uses multi-cloud AI strategies, as AWS and Google Cloud offer more consistent replacement training for updated AI services.
• Audit production Azure AI Search workloads to ensure they align with Microsoft's current focus on generative AI, multimodal processing, and integrated application patterns, to avoid future compatibility and skill gaps.