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在此請求邀請。

2023 年是采用生成式人工智能和基礎模型的一年。

然而，隨著組織競相將人工智能引入其工作流程的前沿和中心，他們意識到讓數(shù)據(jù)事務井然有序是多么重要。

雖然公司始終了解高質(zhì)量數(shù)據(jù)在業(yè)務成功中的作用，但新一代人工智能的興起增強了其價值，確保它成為每個人的焦點。

現(xiàn)在，隨著我們進入 2024 年，這將帶來更大的新一代人工智能故事，領先的行業(yè)專家和供應商分享了他們對未來幾個月數(shù)據(jù)生態(tài)系統(tǒng)不同方面如何發(fā)展的預測。

1.關系型將擺脫SQL

“無論是利用現(xiàn)代邊緣、物聯(lián)網(wǎng)智能應用程序還是生成式人工智能來發(fā)展業(yè)務，企業(yè)在 2024 年都不乏大膽的計劃。所有這些計劃都依賴于對企業(yè)數(shù)據(jù)的安全訪問。

數(shù)據(jù)庫基礎設施將不斷發(fā)展以支持新一代應用程序。

對于操作應用程序來說，雖然 SQL 數(shù)據(jù)庫的使用仍然很流行，但現(xiàn)代應用程序的動態(tài)特性將鼓勵開發(fā)人員尋找替代方案。

符合現(xiàn)代開發(fā)人員 CICD 工作流程并支持嚴格序列化事務以及跨集合的關系連接的文檔關系數(shù)據(jù)庫將成為許多項目的卓越解決方案。

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在分析方面，很明顯大型語言模型需要詳細的上下文才能高精度運行。

基于矢量數(shù)據(jù)庫的檢索增強生成 (RAG) 將在 2024 年成為主流。此外，業(yè)務概念和復雜文檔將被構建為知識圖，以提供人工智能解決方案所需的上下文。

到 2024 年，關系知識圖譜將作為一種新的數(shù)據(jù)庫架構來支持這一點?！?/p>

– Bob Muglia，F(xiàn)auna 執(zhí)行主席、Snowflake 前首席執(zhí)行官

2.矢量數(shù)據(jù)庫將成為最搶手的技術

“In 2024, vector databases will become the most sought-after technology to acquire. In an era where data-driven insights fuel innovation, vector databases have swiftly gained prominence due to their prowess in handling high-dimensional data and facilitating complex similarity searches. Whether for recommendation systems, image recognition, natural language processing, financial forecasting, or other AI-driven ventures, understanding the top vector databases will be critical for software development across industries.”

“As new applications get built from the ground up with AI …, vector databases will play an increasingly important role in the tech stack, just as application databases have in the past. Teams will need scalable, easy-to-use and operationally simple vector data storage as they seek to create AI-enabled products with new LLM-powered capabilities.”

– Ratnesh Singh Parihar, principal architect at Talentica Software, and Avthar Sewrathan, GM for AI and vector at Timescale

3. Fishing for LLM gold in enterprise data lakes

“There’s no shortage of statistics on how much information the average enterprise stores — it can be anywhere in the high hundreds of petabytes for large corporations. Yet many companies report that they’re mining less than half that information (largely structured data) for actionable insights. In 2024, businesses will begin using generative AI to make use of that untamed data by putting it to work building and customizing LLMs. With AI-powered supercomputing, businesses will begin mining their unstructured data — including chats, videos and code — to expand their generative AI development into training multimodal models. This leap beyond the ability to mine tables and other structured data will let companies deliver more specific answers to questions and find new opportunities. That includes helping detect anomalies on health scans, uncovering emerging trends in retail and making business operations safer.”

– Charlie Boyle, vice president of DGX Systems, Nvidia

4. Companies without sophisticated enough automation to power AI will feel the burn

“As businesses implement AI to maintain their competitive edge, many will feel the effects of their disorganized data infrastructure more acutely. The effects of bad data (or not enough data) will be compounded when the stakes are raised from simply serving up bad information on a dashboard to potentially automating the wrong decisions and behaviors based on that data. It’s only a matter of time before someone without strong data infrastructure and governance puts generative AI in a mission-critical context and suffers from a loss in accuracy.”

– Sean Knapp, CEO of Ascend.io

5. Cloud FinOps teams will optimize their data pipelines

“Confronted with the reality of run-away spending in the cloud this year, in 2024, true cross-organization partnerships will be required to identify unnecessary spending, with both finance and engineering teams playing critical roles. In Ascend’s annual research, 48% of respondents cited plans to optimize their data pipelines to reduce cloud computing costs, with 89% of those respondents expecting the number of pipelines to grow in the next 12 months. It will be imperative next year to leverage platforms that pinpoint where extra spending is occurring in data pipelines and push back with rapid demonstrations of cost optimizations to avoid misguided mandates from above.”

– Sean Knapp, CEO of Ascend.io

6. Intent data will become a must-have for go-to-market teams

“In 2024, intent data will no longer be a ‘nice-to-have’ for go-to-market