TheCodeWork

In the age of data-driven decision-making, selecting the appropriate data storage solution is highly crucial for businesses. Although two prominent options, Data lakes and Data warehouse may sound similar, but they offer distinct approaches to data management. However, like any important choice, such as data lake vs data warehouse, it involves careful considerations. As I was saying, a data lake is a storage repository that holds all of an organization’s data, whether structured or unstructured. On the other hand, a data warehouse contains only structured historical data processed for specific purposes. So, depending on requirements – understanding such storage techniques become crucial for building a robust data storage pipeline for businesses. ✅Read more: https://lnkd.in/gkCBMr2F #datalake #datawarehouse #dataanalytics #snowflake #technology #business

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𝐂𝐚𝐧 𝐓𝐞𝐜𝐡 𝐚𝐧𝐝 𝐓𝐚𝐥𝐞𝐧𝐭 𝐠𝐨 𝐡𝐚𝐧𝐝 𝐢𝐧 𝐡𝐚𝐧𝐝? Talent management starts by addressing workforce challenges, adapting to generational needs, leveraging technology in operations, and developing leadership for talent retention. Don’t you agree? At present, organizations are grappling with unprecedented 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬, 𝐢𝐧𝐜𝐥𝐮𝐝𝐢𝐧𝐠 𝐥𝐚𝐛𝐨𝐫 𝐦𝐚𝐫𝐤𝐞𝐭 𝐬𝐡𝐨𝐫𝐭𝐟𝐚𝐥𝐥𝐬 𝐚𝐧𝐝 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐧𝐞𝐞𝐝𝐬.  Talent management has become a primary concern for business leaders, with industry experts offering valuable guidance on shaping a future-ready workforce. They examine the impact of technology on operations, the importance of leadership and development opportunities in retaining talent, and innovative approaches to flexibility and career advancement within the manufacturing sector. Technology can significantly enhance productivity in the workforce. However, the full potential is often unrealized due to inadequate change management. > Effective change management involves developing tools and solutions in collaboration with the current workforce, who spend significant time in these roles and understand the challenges and opportunities. Engaging them from the beginning is crucial for maximizing the benefits of technology. > The second thing is that Gen Z, being what I would call digital natives, are eager to engage with technology and digital tools. This presents a significant opportunity for companies to enhance their value proposition by showcasing their investment in technology. Basically it’s the right time to use AI for work optimization. Are you onboard yet? ✅ Book a free consultation call to know more on this: https://lnkd.in/gutNhqyz #digitalinnovation #ai #artificialintelligence #technology #business

#Work #Efficiency #Operations #Entrepreneurs #Founders

As we know, yesterday, 𝐓𝐡𝐞 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐔𝐧𝐢𝐨𝐧 𝐁𝐮𝐝𝐠𝐞𝐭 𝟐𝟎𝟐𝟒, was presented by Finance Minister Nirmala Sitharaman. And as a part of the tech industry, we at TheCodeWork thought of jotting down the highlights related to 𝐭𝐞𝐜𝐡, 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐟𝐨𝐫 𝐲𝐨𝐮. > A sum of INR551 crore has been allocated towards “𝐈𝐧𝐝𝐢𝐚 𝐀𝐈” mission > 𝐃𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 by utilization of sectoral databases and technology tools under the Digital India Mission has been proposed > There will be 𝐃𝐢𝐠𝐢𝐭𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐚𝐱𝐩𝐚𝐲𝐞𝐫 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 to make all customs and Income Tax services paperless within the next two years > 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 for IBC (Insolvency and Bankruptcy Code) ecosystem to be set up to improve consistency, transparency and have a better oversight > 𝐄𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐞𝐱𝐩𝐨𝐫𝐭 𝐡𝐮𝐛𝐬 will be set up to facilitate access to international markets by micro, small and medium enterprises (MSMEs) and traditional artisans. > Development of population-scale 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐩𝐮𝐛𝐥𝐢𝐜 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 (𝐃𝐏𝐈) tools in areas of credit, ecommerce, health, law and justice, logistics, MSME service delivery, and urban governance. In conclusion, the technology landscape is rapidly evolving and becoming more skill-intensive. However, according to us, 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐮𝐝𝐠𝐞𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥 𝐨𝐟 𝐦𝐚𝐤𝐢𝐧𝐠 𝐈𝐧𝐝𝐢𝐚 𝐚𝐧 𝐀𝐈 𝐟𝐢𝐫𝐬𝐭 𝐧𝐚𝐭𝐢𝐨𝐧. ✅ Time to get going with your AI projects already. Need to dicuss operations on it? Book a free consultation call with us: https://lnkd.in/gutNhqyz #AI #artificialintelligence #budget #unionbudget #nirmalasitaraman #technology #business

#AI #Business #Startups #technology #LinkedInpolls

𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐬𝐜𝐚𝐥𝐞 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈? In our experience at TheCodeWork, organizations have been held back by a still maturing understanding of both how to evolve data capabilities to support gen AI cases at scale and how to use gen AI to improve data practices. ✅ It starts at the source: Improve your data While data quality has long been an important concern for data and AI leaders, the risks and costs of feeding poor data into gen AI models cannot be overstated, ranging from poor outcomes, costly fixes, and cyber breaches to a loss of user trust in the outputs. Traditional methods of ensuring data quality aren’t enough; leaders should consider the following ways of improving and expanding their source data. ✅ Obtain better and more-accurate source data from complex data types Organizations are struggling to handle the increased complexity of unstructured data sets. Tools have evolved to handle the relationship between different types and sources of data. For example, knowledge graphs can help capture complex relationships between entities, providing meaningful context for large language models (LLMs) and their downstream data sets. These kinds of capabilities make it easier to accurately map data points from unstructured to structured data. ✅ Create data when they aren’t available Some gen AI use cases are difficult to pursue because the required data are difficult to obtain and process, which is often an issue in healthcare, life sciences, or other sectors that have stringent data security regulations. To overcome these challenges, in some cases, a data engineer can manually generate a file to test the efficacy of a use case. But the process can be time-consuming and inefficient. In conclusion, data handling can be a complex task and it’s always better to consult with experts before implementing or concluding on the same. 📞 Book a free consultation call here: https://lnkd.in/gutNhqyz #AI #artificialintelligence #GenAI #technology #innovation #business

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Mistral AI and NVIDIA 𝐔𝐧𝐯𝐞𝐢𝐥 𝟏𝟐𝐁 𝐍𝐞𝐌𝐨 𝐌𝐨𝐝𝐞𝐥: 𝐀 𝐆𝐚𝐦𝐞-𝐂𝐡𝐚𝐧𝐠𝐞𝐫 𝐢𝐧 𝐀𝐈 Mistral AI and NVIDIA have introduced the 12B NeMo model, boasting over 12 billion parameters for enhanced language understanding and generation. Built on NVIDIA’s GPU architecture, it offers unparalleled efficiency and scalability. This model is set to revolutionize industries by driving innovation in customer service, healthcare, and finance. 𝐅𝐨𝐫 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞, 𝐢𝐭 𝐜𝐚𝐧 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐢𝐧 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐚𝐧𝐝 𝐞𝐧𝐚𝐛𝐥𝐞 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐢𝐧 𝐟𝐢𝐧𝐚𝐧𝐜𝐞. As AI technology evolves, the 12B NeMo model sets new standards for performance and application, creating unprecedented growth opportunities. Additionally, its ability to process complex queries and deliver actionable insights will significantly enhance decision-making and operational efficiency across sectors. Early tests show a 30% increase in processing speed and a 25% improvement in accuracy compared to previous models, highlighting its disruptive potential. #AI #ArtificialIntelligence #TechInnovation #NVIDIA #MachineLearning #NeMoModel

#AI #Business #Technology #TechSolutions #LinedInpolls