TheSequence

MAY 21, 2023

📢 Event: apply(risk), the ML Engineering Community Conference for Building Risk & Fraud Detection Systems Want to connect with the ML engineering community and learn best practices from ML practitioners at Affirm, Remitly, Block, Tide, and more, on how to build risk and fraud detection systems?

Generative AI 64

Generative AI ML Engineer ETL LLM 64

The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. If you aren’t aware already, let’s introduce the concept of ETL.

ETL 52

ETL Data Drift Machine Learning ML 52

AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

ML 98

ML Machine Learning ETL Data Scientist 98

AWS Machine Learning Blog

JANUARY 5, 2024

Artificial intelligence (AI) and machine learning (ML) offerings from Amazon Web Services (AWS) , along with integrated monitoring and notification services, help organizations achieve the required level of automation, scalability, and model quality at optimal cost.

Data Science 110

Data Science Data Drift DevOps Auto-complete 110

AWS Machine Learning Blog

AUGUST 4, 2023

It eliminates tedious, costly, and error-prone ETL (extract, transform, and load) jobs. For example, you can use automated workflows that can adapt in an instant based on new data. We also provided an automated solution to deploy the SageMaker endpoints as an API using a SageMaker Projects template for Salesforce.

Data Scientist 73

Data Scientist ML ETL AI 73

The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier. What is an ETL data pipeline in ML? Let’s look at the importance of ETL pipelines in detail.

ETL 59

ETL ML Machine Learning Data Scientist 59

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Generative AI 103

Generative AI Prompt Engineer Prompt Engineering AI 103