Christophe Blefari

JANUARY 26, 2024

This is a fun part where students learn how to serve a simple classifier in production. Here a few takeaways in a messy way: Data products and organisational impacts Data engineers are still the limiting human resource. UX for data products — This is a presentation I really enjoyed by Claire Lebarz, VP data at Malt.

Google Cloud 130

Google Cloud Algorithm Data Java 130

Rockset

MARCH 18, 2024

That’s because successfully deploying an AI application requires retrieval augmented generation or “RAG” pipelines, processing real-time data streams, chunking data, generating embeddings, storing embeddings and running vector search. These pipelines take contextual data and feed it into an LLM to improve the relevancy of a response.

Cloud 64

Cloud Building Metadata Kafka 64

Lyft Engineering

MARCH 22, 2023

lyft2vec — Embeddings at Lyft Co-authors: Javen Xu , Hakan Baba and Adriana Deneault Intro Graph learning methods can reveal interesting insights that capture the underlying relational structures. Graph learning methods have many industry applications in areas such as product or content recommender systems and network analysis.

Algorithm 120

Algorithm Machine Learning Government Structured Data 120

Rockset

APRIL 18, 2023

To highlight these new capabilities, we built a search demo using OpenAI to create embeddings for Amazon product descriptions and Rockset to generate relevant search results. With vector search, we can leverage text embeddings to capture semantic associations across words, phrases and sentences to power more robust search experiences.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

Data Engineering Weekly

MARCH 17, 2024

link] Jarkko Moilanen: Exploring the Frontier of Data Products - Seeking Insights on Emerging Standards Data products are becoming a central part of the data pipeline strategy, bringing a new approach to building data as an asset. The potential of embedded analytics to enhance insights and efficiency across various contexts.

Data Engineering 70

Data Engineering Data Engineer Engineering Data Governance 70

Rockset

MARCH 28, 2024

Overview In this guide, you will: Gain a high-level understanding of vectors, embeddings, vector search, and vector databases, which will clarify the concepts we will build upon. Learn how to use the Rockset console with OpenAI embeddings to perform vector-similarity searches, forming the backbone of our recommender engine.

Systems 52

Systems Building Database Utilities 52

Data Engineering Podcast

APRIL 7, 2024

What are the challenges involved in retrofitting a semantic layer into a production data system? The Machine Learning Podcast helps you go from idea to production with machine learning. What are the challenges involved in retrofitting a semantic layer into a production data system? dbt, BI, warehouse marts, etc.)

Data Lake 162

Data Lake High Quality Data BI Data Workflow 162

LinkedIn Engineering

JUNE 20, 2023

Academia brings expertise on cutting-edge research on detection techniques, while industry possesses a wealth of data, experience with real-world challenges and limitations, and the ability to translate research into production at internet scale. For a baseline comparison, we also create a fixed linear embedding based on a Fourier analysis.

Media 132

Media Datasets Programming Engineering 132

The Pragmatic Engineer

APRIL 6, 2023

He shared: “I'd preface everything by saying that this is very much a v1 of our jobs product and we plan to iterate and build a lot more as we get feedback. We are super excited to start playing vector databases - ones that store and index vector embeddings we get from natural language processing models like OpenAI embeddings.

Software Engineer 188

Software Engineer Software Engineering Datasets Database 188

Snowflake

APRIL 10, 2024

Coda provides an innovative, all-in-one productivity platform that combines the functionality of docs, spreadsheets, and applications in a single, interactive workspace. Productivity tools today tend to be largely siloed, and connecting them to a common underlying data layer can be complex and hard to manage.

Finance 101

Finance Media Cloud Data 101

Snowflake

NOVEMBER 27, 2023

Hum transforms all content, people, topics and behaviors into embeddings, the native language of large language models, so that the LLMs at the core of Alchemist (Hum’s AI engine) can power features such as infinite topic affinities, audience deep search, and next-generation personalized content recommendations.

Raw Data 90

Raw Data Relational Database Consulting Architecture 90

DoorDash Engineering

APRIL 23, 2024

DoorDash’s retail catalog is a centralized dataset of essential product information for all products sold by new verticals merchants – merchants operating a business other than a restaurant, such as a grocery, a convenience store, or a liquor store. Better personalization.

Building 84

Building Retail Manufacturing Unstructured Data 84

Data Engineering Weekly

APRIL 28, 2024

Intuit: The Data Mesh Strategy Behind Intuit’s Global Financial Technology Platform The Data Product Builder platform is becoming increasingly important in enterprise data engineering. Intuit writes about the growing data mesh strategy and the strategic focus area for building data products.

Data Engineering 52

Data Engineering Data Engineer Engineering Hospitality 52

Simon Späti

SEPTEMBER 29, 2022

We design dimensions for drilling down reports by region, product, or whatever metrics we are interested in. What has changed is that we no longer use a singular business intelligence tool; different teams use different visualizations (BI, notebooks, and embedded analytics). We build dashboards with yearly and monthly aggregations.

BI 130

BI Business Intelligence Designing Building 130

Simon Späti

SEPTEMBER 29, 2022

We design dimensions for drilling down reports by region, product, or whatever metrics we are interested in. What has changed is that we no longer use a singular business intelligence tool; different teams use different visualizations (BI, notebooks, and embedded analytics). We build dashboards with yearly and monthly aggregations.

BI 130

BI Business Intelligence Designing Building 130

Knowledge Hut

MARCH 29, 2024

Work Experience: Lead Embedded Software Engineer at Rockwell Automation, OH Developed a 15% more efficient production system using new embedded software for Rockwell Automation, OH. A 10% improvement in productivity was achieved due to this system's 50% reduction in downtime. Honda R&D Americas, Inc.,

Software Engineer 52

Software Engineer Software Engineering Engineering Programming Language 52

Data Engineering Podcast

DECEMBER 11, 2022

Preamble This is a cross-over episode from our new show The Machine Learning Podcast , the show about going from idea to production with machine learning. Embedding vectors are a way to structure data in a way that is native to how models interpret and manipulate information. images, audio, video, etc.) images, audio, video, etc.)

Unstructured Data 100

Unstructured Data Machine Learning Data Engineering Data Engineer 100

Data Engineering Podcast

FEBRUARY 5, 2023

The Machine Learning Podcast helps you go from idea to production with machine learning. The Machine Learning Podcast helps you go from idea to production with machine learning. Don't forget to check out our other shows. __init__ covers the Python language, its community, and the innovative ways it is being used.

Data Engineering 147

Data Engineering Data Engineer Engineering PostgreSQL 147

Rockset

MARCH 27, 2024

Explo is an embedded analytics platform that extends Rockset’s database functionality into dashboards and data reports With Explo, product teams can embed white-label dashboards directly into their SaaS application or platform. Explo’s embedded integration with Rockset is seamless; and now, that has just gotten even easier.

Database 52

Database SQL Datasets Architecture 52

Towards Data Science

JULY 13, 2023

Specifically, we’ll cover pulling data from the web, creating text embeddings (vectors) and pushing them to a vector store. Now, let’s think about the above in a production context, and imagine a user is unsatisfied with the outputs of your application and you want to find the root cause of the issue. Stack overview. Image by authors.

Building 75

Building Database-centric Database Coding 75

Netflix Tech

JUNE 20, 2023

In the second approach, we show that a relatively simple, supervised sequential model (bidirectional LSTM or GRU) that uses rich, pretrained shot-level embeddings can outperform the current state-of-the-art baselines on our internal benchmarks. sluglines). Figure 1: a scene consists of a sequence of shots.

Machine Learning 101

Machine Learning Datasets Algorithm Architecture 101

Data Engineering Weekly

APRIL 7, 2024

link] Sponsored: Cloud Academy's Solution to Enhanced Embedded Analytics Cloud Academy, a SaaS e-learning platform, needed to deliver a seamless, highly available embedded analytics experience for their enterprise customers. I strongly believe the concept of Data Product will play a bigger role in data engineering.

Data Engineering 70

Data Engineering Data Engineer Engineering Unstructured Data 70

Knowledge Hut

MAY 3, 2024

Provide Complex Data Within Software and Apps Power BI takes input from various sources and provides that data in various contexts, including embedded within your own apps through API by Microsoft. It also provides a product value-add feature that is used to track and identify the key data sets regarding the product or service.

BI 94

BI Media Transportation MySQL 94

LinkedIn Engineering

AUGUST 23, 2023

To improve the performance and personalization of the feed, we have added the representation of sparse IDs as features to the recommendation algorithms which power these products. Additionally, embeddings can address the 'cold start' problem, which arises when there is limited information about new items or members.

Architecture 82

Architecture Deep Learning Datasets Machine Learning 82

Snowflake

NOVEMBER 1, 2023

Snowflake is excited to announce an innovative product lineup that brings our platform’s ease of use, security and governance to the GenAI world. This includes: Embed Text (in private preview soon)- This function transforms a given text input to vector embeddings using a user selected embedding model.

Government 97

Government SQL AWS Database 97

Rockset

OCTOBER 13, 2023

When building a vector search app, you’re going to end up managing a lot of vectors, also known as embeddings. A vector database not only stores embeddings but also facilitates such common search operations over them. Every item you want to search on will need to be processed to produce an embedding, as will every query.

Database 52

Database Metadata Algorithm Cloud 52

ThoughtSpot

JUNE 27, 2023

Why this matters in embedded analytics Many customers of ThoughtSpot Everywhere integrate the platform and its features into their own data products and applications. cl, we hope that it offers more flexibility in your unique embedding and authentication scenarios.

Cloud 80

Cloud Accessible Accessibility Coding 80

ThoughtSpot

FEBRUARY 23, 2023

With ThoughtSpot Everywhere for Startups, you can: Embed data and analytics into your products Whether you’re building an internal- or external-facing product or web-based application, embedded analytics gives users the personalized experiences they want. Don’t just take our world for it. Nue is a great example.

Food 69

Food Healthcare Finance Programming 69

Data Engineering Weekly

MARCH 24, 2024

OpenAI has more production deployments, but Google is catching on to it. link] Sponsored: Embedded Analytics and AI Chatbot on one semantic layer AI Chatbot for Embedded Analytics powered by a Semantic Layer Spine, a SaaS company, uses Cube's semantic layer to enhance customer-embedded analytics by incorporating an AI chatbot.

Data Engineering 62

Data Engineering Data Engineer Engineering Metadata 62

Data Engineering Podcast

AUGUST 6, 2022

These platforms store direct representations of the vector embeddings that machine learning models rely on for computing relevant predictions so that there is no additional processing required to go from input data to inference output. What are the use cases for a vector database and similarity search of vector embeddings?

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100