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达泰库:吃你的蛋糕也吃它与Dataiku+数据砖.pdf

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1、Have Your Cake and Eat it Too with Dataiku and DatabricksAmanda MilbergSenior Partner Solutions Engineer,DataikuDatabricks2023Session ObjectiveProvide an overview of Dataiku Highlight the seamless feature integrations of Dataiku and DatabricksIllustrate how utilizing these two technologies provide a

2、 fool proof recipe for AI applications and data driven success Providing a high level overview of what are Large Language Models(LLMs)Introduce the use case we will be walking through todayOutline step-by-step how to customize a LLM on your own data with low computational resourcesDemocratize our LL

3、M to the enterprise through a no-code Dataiku ApplicationOutline the components of the RAFT framework for Generative AI Deploy our LLM in a secure,governed environment Gather the IngredientsFollow a RecipeThe Icing on The CakeHighlight how Dataiku and Databricks is a winning formula for data excelle

4、nce1_DAIS_Title_SlideGather the IngredientsHow Dataiku and Databricks provide a fool-proof recipeWhat is Dataiku?Skill Agnostic Analytics Workbench to Scale Data&AI Initiatives Data EngineerBusiness AnalystData AnalystAnalytics LeaderData ScientistHigh codeLow codeNo codeCentralized Analytics Workbe

5、nchCloud Agnostic/Hybrid CloudData Access&CatalogingData Preparation&Analysis(Auto)Machine LearningProduction DeploymentAnalyze,aggregate,and transform data with visual or code interfacesDevelop,deploy,and monitor machine learning models in a single common environmentSchedule,deploy,and monitor data

6、 products across the enterpriseData Product Development&GovernanceConnect to ANY data source(Cloud,On Prem,ERP,CRM,etc)with or without codeEveryone working together in a common analytics workbench with a visual interface allows for collaboration,reuse,and scaleDecrease the marginal cost of data&clou

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本文主要介绍了Dataiku和Databricks的集成使用,提供了一个无代码的数据科学平台,用于定制和部署大型语言模型(LLM)。文章强调了Dataiku和Databricks结合使用可以为AI应用和数据驱动的成功提供一个可靠的食谱。Dataiku是一个技能无关的 analytics workbench,可扩展数据和AI倡议,而Databricks提供了计算和存储能力。文章提到,LLM可以革命性地提高自然语言处理的效率,并且可以显著提高员工的工作效率。通过使用Dataiku和Databricks,可以创建一个完全在企业内部基础设施上运行的、无需外部数据移动的Open Source LLM模型。此外,文章还介绍了一种称为Retrieve-Then-Read Pipeline的方法,它可以查询向量存储以检索与问题 semantically 相似的事实,并将这些事实纳入问题回答提示中,然后查询LLM以生成答案。最后,文章强调了在AI系统开发和部署过程中,确保治理、安全、可靠、透明和可解释性的重要性。
"如何通过Dataiku和Databricks实现AI应用的无忧开发?" "如何利用开源LLM模型为企业打造问答系统?" "如何通过Dataiku应用程序将LLM模型安全地 democratize至企业内部?"
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