Machine Learning Archives - Tiger Analytics

Blog Tags: Machine Learning

GenAI for ITSM – 4 Ways LLMs Improve IT Ticket Handling and User Experience

Large Language Models (LLMs) are transforming IT service management by automating ticket categorization, improving prioritization, and speeding up resolutions. This article explores how LLMs enhance efficiency, empower users, and support agents in handling complex issues, all while streamlining workflows and improving response times.

Building Trusted Data: A Comprehensive Guide to Tiger Analytics’ Snowflake Native Data Quality Framework

Challenges in data quality are increasingly hindering organizations, with issues like poor integration, operational inefficiencies, and lost revenue opportunities. A 2024 report reveals that 67% of professionals don’t fully trust their data for decision-making. To tackle these problems, Tiger Analytics developed a Snowflake native Data Quality Framework, combining Snowpark, Great Expectations, and Streamlit. Explore how the framework ensures scalable, high-quality data for informed decision-making.

How to Simplify Data Profiling and Management with Snowpark and Streamlit

Learn why data quality is one of the most overlooked aspects of data management. While all models need good quality data to generate useful insights and patterns, data quality is especially important. In this blog, we explore how data profiling can help you understand your data quality. Discover how Tiger Analytics leverages Snowpark and Streamlit to simplify data profiling and management.

What is Data Observability Used For?

Learn how Data Observability can enhance your business by detecting crucial data anomalies early. Explore its applications in improving data quality and model reliability, and discover Tiger Analytics’ solution. Understand why this technology is attracting major investments and how it can enhance your operational efficiency and reduce costs.

Implementing Context Graphs: A 5-Point Framework for Transformative Business Insights

This comprehensive guide outlines three phases: establishing a Knowledge Graph, developing a Connected Context Graph, and integrating AI for auto-answers. Learn how this framework enables businesses to connect data points, discover patterns, and optimize processes. The article also presents a detailed roadmap for graph implementation and discusses the integration of Large Language Models with Knowledge Graphs.

Solving Merchant Identity Extraction in Finance: Snowpark’s Data Engineering Solution

Learn how a fintech leader solved merchant identification challenges using Snowpark and local testing. This case study showcases Tiger Analytics’ approach to complex data transformations, automated testing, and efficient development in financial data processing. Discover how these solutions enhanced fraud detection and revenue potential.

Power to the People: How GenAI Empowered Employees are Redefining Workplace Productivity

As GenAI becomes increasingly accessible, employees are transforming into AI-empowered superheroes. Organizations must focus on individualized rollouts, research tools, expert assistance, and readiness assessment frameworks to harness the full potential of GenAI and redefine workplace productivity.

Empowering BI through GenAI: How to address data-to-insights’ biggest bottlenecks

Explore how integrating generative AI (GenAI) and natural language processing (NLP) into business intelligence empowers organizations to unlock insights from data. GenAI addresses key bottlenecks: enabling personalized insights tailored to user roles, streamlining dashboard development, and facilitating seamless data updates. Solutions like Tiger Analytics’ Insights Pro leverage AI to democratize data accessibility, automate pattern discovery, and drive data-driven decision-making across industries.

Turning Conversational Data into Chat Intelligence with Ablation Analysis

Discover how Tiger Analytics harnesses Chat Intelligence through ablation analysis and deep learning models like BERT to transform conversational data into actionable insights, enhancing customer engagement and unlocking growth opportunities.

Invisible Threats, Visible Solutions: Integrating AWS Macie and Tiger Data Fabric for Ultimate Security

Data defenses are now fortified against potential breaches with the Tiger Data Fabric-AWS Macie integration, automating sensitive data discovery, evaluation, and protection in the data pipeline for enhanced security. Explore how to integrate AWS Macie into a data fabric.

Data Science Strategies for Effective Process System Maintenance

Industry understanding of managing planned maintenance is fairly mature. This article focuses on how Data Science can impact unplanned maintenance, which demands a differentiated approach to build insight and understanding around the process and subsystems.

Defining Financial Ethics: Transparency and Fairness in Financial Institutions’ use of AI and ML

While time, cost, and efficiency have seen drastic improvement thanks to AI/ML, concerns over transparency, accountability, and inclusivity prevail. This article provides important insight into how financial institutions can maintain a sense of clarity and inclusiveness.

Blog Tags: Machine Learning

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