Remove product workflows
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Building ETL Pipelines With Generative AI

Data Engineering Podcast

Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Can you describe what a typical workflow of using AI to build ETL workflows looks like?

Building 162
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What Is Kanban In Agile Values, Principles, Benefits & Career

Knowledge Hut

The certified Kanban training courses, designed for different levels, allow the program managers, delivery managers, project managers, software product developers and business analysts etc to choose the best and to boost up their career growth. What is Kanban? Collaboration: It encourages consistent improvement.

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AI debugging at Meta with HawkEye

Engineering at Meta

HawkEye is the powerful toolkit used internally at Meta for monitoring, observability, and debuggability of the end-to-end machine learning (ML) workflow that powers ML-based products. HawkEye supports recommendation and ranking models across several products at Meta. HawkEye’s debugging workflows. HawkEye’s components.

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Snowflake’s New Python API Empowers Data Engineers to Build Modern Data Pipelines with Ease

Snowflake

In today’s data-driven world, developer productivity is essential for organizations to build effective and reliable products, accelerate time to value, and fuel ongoing innovation. This allows your applications to handle large data sets and complex workflows efficiently.

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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. This framework explains how application enhancements can extend your product offerings. How much value could you add? Brought to you by Logi Analytics.

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Snowflake Startup Challenge 2024: Announcing the 10 Semi-Finalists

Snowflake

Innova-Q Focusing on food safety and quality, Innova-Q ’s Quality Performance Forecast Application delivers near real-time insights into product and manufacturing process performance so companies can assess and address product risks before they affect public safety, operational effectiveness or direct costs. SignalFlare.ai

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How Data Engineering Teams Power Machine Learning With Feature Platforms

Data Engineering Podcast

Summary Feature engineering is a crucial aspect of the machine learning workflow. In this episode Razi Raziuddin shares how data engineering teams can support the machine learning workflow through the development and support of systems that empower data scientists and ML engineers to build and maintain their own features.

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5 Early Indicators Your Embedded Analytics Will Fail

Many application teams leave embedded analytics to languish until something—an unhappy customer, plummeting revenue, a spike in customer churn—demands change. But by then, it may be too late. In this White Paper, Logi Analytics has identified 5 tell-tale signs your project is moving from “nice to have” to “needed yesterday.".

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.