Muhammad Umair

umair894

AI & ML interests

AI Consultant | Engineer | Global Trainer |PhD Scholar |6+ Years of Experience: Specializing in building AI-powered SaaS solutions and delivering innovative AI, generative AI, and multimodal automation use cases. I've successfully led and contributed to dozens of projects, creating impactful proofs of concept (PoCs) that drive business value. With expertise in large language models (LLMs), computer vision, NLP, automation, and AI system design, I help companies unlock the power of AI to transform ideas into actionable solutions. As a global AI trainer, I empower organizations and individuals with the tools and knowledge to harness AI’s potential. Let’s build the future of AI together!

Recent Activity

reacted to singhsidhukuldeep's post with πŸ‘ 1 day ago
Excited to share groundbreaking research from @Baidu_Inc on enterprise information search! The team has developed EICopilot, a revolutionary agent-based solution that transforms how we explore enterprise data in large-scale knowledge graphs. >> Technical Innovation EICopilot leverages Large Language Models to interpret natural language queries and automatically generates Gremlin scripts for enterprise data exploration. The system processes hundreds of millions of nodes and billions of edges in real-time, handling complex enterprise relationships with remarkable precision. Key Technical Components: - Advanced data pre-processing pipeline that builds vector databases of representative queries - Novel query masking strategy that significantly improves intent recognition - Comprehensive reasoning pipeline combining Chain-of-Thought with In-context learning - Named Entity Recognition and Natural Language Processing Customization for precise entity matching - Schema Linking Module for efficient graph database query generation >> Performance Metrics The results are impressive - EICopilot achieves a syntax error rate as low as 10% and execution correctness up to 82.14%. The system handles 5000+ daily active users, demonstrating its robustness in real-world applications. >> Implementation Details The system uses Apache TinkerPop for graph database construction and employs sophisticated disambiguation processes, including anaphora resolution and entity retrieval. The architecture includes both offline and online phases, with continuous learning from user interactions to improve query accuracy. Kudos to the research team from Baidu Inc., South China University of Technology, and other collaborating institutions for this significant advancement in enterprise information retrieval technology.
published a Space 1 day ago
umair894/finetuningunsloth
liked a Space 2 days ago
deepseek-ai/Janus-Pro-7B
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