According to DeepLearning.AI, implementing Document AI workflows is critical for robust information retrieval, especially when migrating operations to cloud environments. Their new guide, developed in partnership with LandingAI, demonstrates how to use Retrieval-Augmented Generation (RAG) with agents for advanced document parsing and extraction, a step often overlooked in document processing. The guide also explores practical integration with AWS services such as S3, Lambda, and Bedrock, enabling businesses to build scalable, production-ready document pipelines. As reported by DeepLearning.AI, this approach streamlines document automation and supports enterprise-scale deployment.

According to DeepLearning.AI on Twitter, extracting and analyzing data from formats like PDFs, PowerPoints, and Word Documents remains a major challenge due to the lack of machine-readable structures. Without these, automated search and large-scale analysis are nearly impossible. DeepLearning.AI is partnering with LandingAI to offer a course focused on leveraging AI for document processing, highlighting how AI-driven solutions can transform traditional document workflows for businesses. As reported by DeepLearning.AI, this development points to significant business opportunities in automating document data extraction and analysis using AI.

According to DeepLearningAI on Twitter, a new course in collaboration with LandingAI titled 'Document AI: From OCR to Agentic Doc Extraction' is being launched to help users automate the process of extracting and reformatting data from documents. The course promises to teach participants how to use advanced OCR and AI-driven document extraction tools, which can significantly reduce manual data entry and streamline business workflows. As reported by DeepLearningAI, this education initiative targets professionals seeking to leverage document AI for enhanced productivity and operational efficiency.

According to DeepLearning.AI, their new course with LandingAI, 'Document AI: From OCR to Agentic Doc Extraction,' teaches users to extract information from complex documents, including those with handwritten formulas, nested captions, and overlapping watermarks. The curriculum covers practical applications of optical character recognition, layout detection, and advanced document reading, offering professionals actionable skills for automating data extraction in business workflows. As reported by DeepLearning.AI on Twitter, this course addresses growing industry needs for intelligent, agent-driven document processing.

According to DeepLearning.AI, traditional OCR is limited to character recognition and cannot interpret structural elements such as headers, totals, or checkboxes commonly found in tables, invoices, and forms (source: DeepLearning.AI, Jan 24, 2026). To address these shortcomings, their new course with LandingAI, 'Document AI: From OCR to Agentic Doc Extraction,' teaches how to build AI agents that decompose documents, apply specialized tools, and accurately map information to structured formats. This approach enables practical automation of complex document workflows and presents significant business opportunities for enterprises seeking to streamline data extraction and document processing using cutting-edge AI solutions.

According to Andrew Ng (@AndrewYNg), LandingAI has launched a new course titled 'Document AI: From OCR to Agentic Doc Extraction,' taught by David Park and Andrea Kropp (source: Andrew Ng on Twitter, Jan 14, 2026). The course addresses the widespread challenge of extracting structured data from unstructured documents such as PDFs and JPEGs. It covers practical techniques for building agentic document extraction systems using advanced optical character recognition (OCR) and AI-driven automation. This initiative offers concrete business opportunities for enterprises dealing with large volumes of document-based data, helping them automate workflows, improve data accuracy, and enable faster decision-making through AI-powered document processing (source: Andrew Ng on Twitter, Jan 14, 2026).

According to @DeepLearningAI, the new course 'Document AI: From OCR to Agentic Doc Extraction' developed with LandingAI introduces Agentic Document Extraction (ADE), which surpasses traditional OCR by enabling AI models to interpret documents as visual objects. This approach allows extraction of structured data, including tables, charts, and reading order, outputting formats like Markdown and JSON mapped to specific regions on the page. The course, taught by David Park and Andrea Kropp, demonstrates practical applications of ADE for business automation, document analytics, and workflow integration, offering significant efficiency improvements over legacy OCR solutions (source: @DeepLearningAI, Jan 14, 2026).