Jinsung Choi’s Post

Great insight

📊 𝐀𝐈 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 🚀 In today’s data-driven world, 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 is evolving rapidly, and 𝐀𝐈 is playing a pivotal role in revolutionizing this field. Gone are the days when data pipelines and workflows required manual setups for processing terabytes of information. AI is stepping in to make these processes smarter, faster, and more efficient. 𝐇𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 AI-driven tools can 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐛𝐮𝐢𝐥𝐝 𝐚𝐧𝐝 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐄𝐓𝐋 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦, 𝐋𝐨𝐚𝐝) pipelines. They adapt to changes in data sources or formats in real-time, saving countless hours of manual intervention. 𝐒𝐦𝐚𝐫𝐭 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 AI enhances 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 by detecting patterns across disparate sources. It aligns schemas, resolves mismatched fields and improves data consistency. 𝐀𝐧𝐨𝐦𝐚𝐥𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 AI excels at finding irregularities in data pipelines, ensuring that only clean and valid data flows into analytics systems. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐇𝐞𝐚𝐥𝐢𝐧𝐠 AI-powered systems monitor data workflows in real-time. They can predict pipeline failures and even initiate automatic corrective actions. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐫𝐢𝐜𝐡𝐦𝐞𝐧𝐭 Through 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) and advanced analytics, AI enriches raw datasets by tagging, summarizing, and contextualizing data. 𝐃𝐚𝐭𝐚 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 AI identifies vulnerabilities, prevents unauthorized access, and safeguards sensitive information through predictive threat detection. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 & 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Scalability for enormous datasets. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: AI uses distributed architectures like Apache Kafka with advanced algorithms to maintain speed. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Complexity in managing unstructured data. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: AI-based systems excel at parsing and structuring unstructured formats like logs, images, and videos. Challenge: Trust in AI automation. Solution: Incorporating explainable AI (XAI) ensures transparency in AI-driven decisions. 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Data Engineers now spend more time on 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐚𝐧𝐝 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 rather than repetitive tasks. AI empowers them to focus on innovation, enabling businesses to gain insights faster and stay ahead in the competitive landscape. 💬 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘴𝘦𝘦 𝘈𝘐 𝘪𝘯𝘧𝘭𝘶𝘦𝘯𝘤𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘪𝘯 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘧𝘪𝘷𝘦 𝘺𝘦𝘢𝘳𝘴? Share your thoughts below! #AI #DataEngineering #Automation #BigData #MachineLearning #DataPipelines #Innovation

🔬Data Quality enhanced by AI............ OK apologies the title does yet again include AI but this time the angle of approach is a little different. As recently as last week I reposted about the challenges of Data Quality for AI adoption. However life is not a straight line and what if aspects of AI could be utilised to assist and help Data Quality to then assist AI? "Help me, help you" - Jerry McGuire.......... 🏈 I spoke recently with David Jayatillake, VP of AI at Cube who mentioned his previous post below and that entwining Data Quality and AI can have positive results that can then help sell AI adoption and benefit the longer term outcome. However the human factor from the start and perhaps the element that pushes the importance of Data Quality aside needs to be a priority. Thoughts? #dataquality #ai Dylan Anderson, Edosa Odaro, Rob Lancashire, Simon Sleight, Nicola Askham

#datatitbits An article that provides a glimpse into how AI will be utilized in data solutions in 2024. Summary : In the near future, AI database innovation is expected to concentrate on structuring data. Utilizing transformer model capabilities, similar to those used for document summarization and highlight extraction, this technology could be applied to: ▶️Scan requirements documentation for applications. ▶️Assist in data modeling by generating ▶️Entity-Relationship (E-R) diagrams. ▶️Perform schema generation. ▶️Create synthetic data that mirrors the characteristics of real data. Additionally, with the advancement in code generation and the ability to discern implicit data structures,AI could be leveraged to: ▶️Develop data transformation pipelines that streamline the process of converting data from one format or structure into another. #genai #dataanalytics #datagenai #dataengineering #dataai

📊 𝐀𝐈 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐭𝐡𝐞 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐌𝐨𝐝𝐞𝐫𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 🚀 In today’s data-driven world, 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 is evolving rapidly, and 𝐀𝐈 is playing a pivotal role in revolutionizing this field. Gone are the days when data pipelines and workflows required manual setups for processing terabytes of information. AI is stepping in to make these processes smarter, faster, and more efficient. 𝐇𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐬𝐢𝐠𝐧 AI-driven tools can 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐛𝐮𝐢𝐥𝐝 𝐚𝐧𝐝 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐄𝐓𝐋 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦, 𝐋𝐨𝐚𝐝) pipelines. They adapt to changes in data sources or formats in real-time, saving countless hours of manual intervention. 𝐒𝐦𝐚𝐫𝐭 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 AI enhances 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 by detecting patterns across disparate sources. It aligns schemas, resolves mismatched fields and improves data consistency. 𝐀𝐧𝐨𝐦𝐚𝐥𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 AI excels at finding irregularities in data pipelines, ensuring that only clean and valid data flows into analytics systems. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐇𝐞𝐚𝐥𝐢𝐧𝐠 AI-powered systems monitor data workflows in real-time. They can predict pipeline failures and even initiate automatic corrective actions. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐫𝐢𝐜𝐡𝐦𝐞𝐧𝐭 Through 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 (𝐍𝐋𝐏) and advanced analytics, AI enriches raw datasets by tagging, summarizing, and contextualizing data. 𝐃𝐚𝐭𝐚 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 AI identifies vulnerabilities, prevents unauthorized access, and safeguards sensitive information through predictive threat detection. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 & 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Scalability for enormous datasets. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: AI uses distributed architectures like Apache Kafka with advanced algorithms to maintain speed. 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Complexity in managing unstructured data. 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: AI-based systems excel at parsing and structuring unstructured formats like logs, images, and videos. Challenge: Trust in AI automation. Solution: Incorporating explainable AI (XAI) ensures transparency in AI-driven decisions. 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Data Engineers now spend more time on 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐚𝐧𝐝 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 rather than repetitive tasks. AI empowers them to focus on innovation, enabling businesses to gain insights faster and stay ahead in the competitive landscape. 💬 𝘏𝘰𝘸 𝘥𝘰 𝘺𝘰𝘶 𝘴𝘦𝘦 𝘈𝘐 𝘪𝘯𝘧𝘭𝘶𝘦𝘯𝘤𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘪𝘯 𝘵𝘩𝘦 𝘯𝘦𝘹𝘵 𝘧𝘪𝘷𝘦 𝘺𝘦𝘢𝘳𝘴? Share your thoughts below! #AI #DataEngineering #Automation #BigData #MachineLearning #DataPipelines #Innovation

🚀 Exciting developments in AI integration are on the horizon with Anthropic's **Model Context Protocol (MCP)**! This open-source solution is set to transform how AI systems access and interact with diverse data sources, enhancing the relevance and context of AI's outputs. MCP simplifies data integration, fostering interoperability while reducing the technical complexities that often hinder AI advancements. It's like providing AI with a universal key to unlock valuable insights from various platforms. With future remote server capabilities and enterprise-grade security on the way, MCP is positioning itself to revolutionize industries by enabling smoother, more secure data access. Its adoption could democratize AI, empowering smaller businesses to leverage sophisticated AI tools without the burden of custom integrations. The future is bright for organizations ready to embrace this standardized approach! Are you prepared to lead in this new AI landscape? 🌟 #AI #DataIntegration #MCP #Innovation #DigitalTransformation

🚀 AI Will Solve Data Quality 🚀 This week, I explore AI's role in revolutionizing data quality. 🌟 Imagine AI taking over these critical tasks: - Suggesting optimal data models during product engineering. 📊 - Auto-amending or gap-filling minority defective events. 🛠️ - Automatically determining data contracts and schemas. 📜 - Maintaining tests to prevent production deviations. ✅ - Summarizing prod failures and suggesting fixes. 🔍 - Smart alerting, deciding when to wake up engineers. 📱 - Proactively adjusting infrastructure to avoid failures. 💾 But here's the rub — the real change is about people, processes, and prioritization. Companies that prioritize data quality see engineered solutions minimize bugs and maximize output. Dashboards? Overused. Generative AI? The future of accessible, self-serve business data access. 🤖 Click into my blog post to discover how AI is transforming data quality and why prioritizing it is your golden ticket to seamless data operations and executive satisfaction. 🌐👇 https://lnkd.in/geH92w8h #AI #DataQuality #DataEngineering

𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐇𝐞𝐫𝐨 𝐁𝐞𝐡𝐢𝐧𝐝 𝐀𝐈 𝐚𝐧𝐝 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 AI and machine learning are changing sectors—healthcare, finance, manufacturing, and many more. Their ability to predict, automate, and deliver insights is extraordinary, yet AI brilliance often overshadows the crucial foundation that makes it all possible: data engineering. The unprecedented rate at which data is growing today has huge challenges in handling its volume, velocity, and variety. That is where the need for data engineering comes in and builds the infrastructure required to collect, transform, store, and deliver data for high-quality models behind AI and ML. The global big data and data engineering services market size is predicted to be about USD 276.37 billion in 2032 from the USD 75.55 billion recorded in the current year, growing at a CAGR of 17.6% during the forecast period. This growth highlights the increasing demand for expertise in managing the backbone of AI innovation. Here’s why Data Engineering is indispensable for AI and ML success: 1. 𝐃𝐚𝐭𝐚 𝐏𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧: - AI models rely on clean, structured, and reliable data. Data engineers work with ETL processes to extract, transform, and load the data so that models can be trained with high-quality datasets. 2. 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: - The massive datasets and real-time necessities require scalable pipelines for AI. Resilient architectures must be built by data engineers that can withstand enormous amounts of data while ensuring smooth operations for the training and deployment of models. 3. 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐃𝐢𝐯𝐞𝐫𝐬𝐞 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: - AI often relies on data from APIs, databases, IoT devices, and more. Data engineers create pipelines that unite all these fragmented sources into a cohesive dataset for machine learning. 4. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: - AI can only perform as well as its input data. By making quality checks, and ensuring compliance, and governance, data engineers maintain trust in the datasets powering decision-making. 5. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: - Repetitive jobs like ingestion and preprocessing of data? Robust pipelines and automation allow one to get to value sooner and increase performance while keeping costs down. C𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧 The glory goes to AI and ML, but data engineering ensures that the stage is set for their success. This won't be helpful even with the most sophisticated algorithms if data engineering is not done right. #DataEngineering #ArtificialIntelligence #MachineLearning #BigData #DataPipelines #DataScience #TechLeadership #DataQuality #Automation #ETL #DataIntegration #AIApplications #ScalableArchitecture #TechTrends #DigitalTransformation

🤖 𝘼𝙄 𝙞𝙣 𝘼𝙘𝙩𝙞𝙤𝙣 𝙎𝙚𝙧𝙞𝙚𝙨 🤖 - 𝗔 𝗱𝗮𝘁𝗮 𝗹𝗲𝗮𝗱𝗲𝗿’𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗴𝘂𝗶𝗱𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗴𝗲𝗻 𝗔𝗜: Data and AI leaders have been exploring generative AI (gen AI) use cases, revealing both potential value and challenges, particularly in managing data quality and integration. Key actions to overcome these barriers include improving data quality and readiness, using gen AI to create better data products, and addressing data-management considerations for reuse and scalability. Organizations should focus on automating evaluation methods, employing multimodal models, generating synthetic data, and using end-to-end approaches to data pipeline creation to enhance data practices and product development. To scale gen AI effectively, organizations must also adopt agent-based frameworks for better orchestration, ensure strong data security at every stage, and integrate coding best practices into gen AI outputs. Choosing the right large language models (LLMs) for specific coding tasks and automating code translation can facilitate smoother migration to modern cloud resources. By addressing these technical challenges and enhancing orchestration capabilities, data and AI leaders can move from gen AI pilots to scalable solutions that drive substantial value. Explore how MarketTecNexus’s AI Consulting and AIneuro Implementations can grow your business at MarketTecNexus.com I’m interested in your perspective on this topic, please comment below or DM me. Read the article: https://lnkd.in/gghTGveT #MarketTecNexus #AIinAction #InnovateWithAI #AIForLeaders #AIneuroscience

🤖 𝘼𝙄 𝙞𝙣 𝘼𝙘𝙩𝙞𝙤𝙣 𝙎𝙚𝙧𝙞𝙚𝙨 🤖 - 𝗔 𝗱𝗮𝘁𝗮 𝗹𝗲𝗮𝗱𝗲𝗿’𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗴𝘂𝗶𝗱𝗲 𝘁𝗼 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗴𝗲𝗻 𝗔𝗜: Data and AI leaders have been exploring generative AI (gen AI) use cases, revealing both potential value and challenges, particularly in managing data quality and integration. Key actions to overcome these barriers include improving data quality and readiness, using gen AI to create better data products, and addressing data-management considerations for reuse and scalability. Organizations should focus on automating evaluation methods, employing multimodal models, generating synthetic data, and using end-to-end approaches to data pipeline creation to enhance data practices and product development. To scale gen AI effectively, organizations must also adopt agent-based frameworks for better orchestration, ensure strong data security at every stage, and integrate coding best practices into gen AI outputs. Choosing the right large language models (LLMs) for specific coding tasks and automating code translation can facilitate smoother migration to modern cloud resources. By addressing these technical challenges and enhancing orchestration capabilities, data and AI leaders can move from gen AI pilots to scalable solutions that drive substantial value. Explore how MarketTecNexus’s AI Consulting and AIneuro Implementations can grow your business at MarketTecNexus.com I’m interested in your perspective on this topic, please comment below or DM me. Read the article: https://lnkd.in/eyxenpnm #MarketTecNexus #AIinAction #InnovateWithAI #AIForLeaders #AIneuroscience