Nicolay Gerold

5. Prioritizing Quality Data From The Start

Source:https://pair.withgoogle.com/guidebook/patterns

The foundation of any successful AI project is good data. Poor data management can lead to “data cascades,” where one issue leads to another, impacting the user experience. Investing in robust data practices early on can prevent these pitfalls.

Aim For:... See more

Connect external data

to LLMs , no matter the source.

The universal retrieval engine for LLMs to access unstructured data from any source.

Most commonly, ETL means moving data from some source system (e.g. a production database, Slack API) into an analytical data warehouse (e.g. Snowflake) where the data is easier to combine and analyze. Most data teams use a vendor like Fivetran or an orchestration platform like Airflow to do this.

Modal is a great solution for ETL if you are... See more

Why gRPC?

gRPC is a modern open source high performance Remote Procedure Call (RPC) framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication. It is also applicable in last mile of distributed computing to connect... See more

Overview

Dropbase is a developer-first platform to build internal web apps with just Python. It lets you easily import your existing Python libraries and scripts so you don’t have to rewrite them to fit our framework.

Build apps by selecting UI components from a list and binding them to data fetcher functions or Python scripts. Use State & Context... See more

Causal Ranker: A Causal Adaptation Framework for Recommendation Models

Jeong-Yoon Lee, Sudeep Das

Most machine learning algorithms used in personalization and search, including deep learning algorithms, are purely associative. They learn from the correlations between features and outcomes how to best predict a target.

In many scenarios, going beyond... See more

𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝗼𝗳 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗮𝗻 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗟𝗠 𝗲𝘅𝗶𝘀t ↓

- 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘦𝘥 𝘱𝘳𝘦-𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨: utilize domain-specific data to apply the same pre-training process (next token prediction) on the pre-trained (base) model

- 𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵𝘪𝘰𝘯 𝘧𝘪𝘯𝘦-𝘵𝘶𝘯𝘪𝘯𝘨: the pre-trained (base) model is fine-tuned on a Q&A dataset to learn to answer questions

- 𝘚𝘪𝘯𝘨𝘭𝘦-𝘵𝘢𝘴𝘬 𝘧𝘪𝘯𝘦-𝘵𝘶𝘯𝘪𝘯𝘨: the... See more

MLServer aims to provide an easy way to start serving your machine learning models through a REST and gRPC interface, fully compliant with KFServing's V2 Dataplane spec. Watch a quick video introducing the project here.

• Multi-model serving, letting users run multiple models within the same process.


• Ability to run inference in parallel for vertical

... See more