Data Platform, Data Science and ML Engineer - Artificial Intelligence Zone

MakeBlobs + Fictional Synthetic Data, Adding Data to Domain-Specific LLMs, and What Tech Layoffs…

ODSC - Open Data Science

DECEMBER 7, 2023

How to Add Domain-Specific Knowledge to an LLM Based on Your Data In this article, we will explore one of several strategies and techniques to infuse domain knowledge into LLMs, allowing them to perform at their best within specific professional contexts by adding chunks of documentation into an LLM as context when injecting the query.

Data Scientist

Data Scientist Explainable AI Data Science Python

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

AWS Machine Learning Blog

OCTOBER 20, 2023

Data scientists search and pull features from the central feature store catalog, build models through experiments, and select the best model for promotion. Data scientists create and share new features into the central feature store catalog for reuse.

ML

ML Data Scientist ML Engineer Data Science

Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

ML

ML DevOps Data Scientist ML Engineer

Webinars

Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Usage-Based Monetization Musts: A Roadmap for Sustainable Revenue Growth

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

Machine Learning

Machine Learning DevOps Data Scientist Data Quality

Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning

Machine Learning Data Drift Data Science Data Scientist

The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

Large Language Models

Large Language Models Data Scientist Machine Learning Computer Vision

The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

Large Language Models

Large Language Models Data Scientist Machine Learning Computer Vision

Designing resilient cities at Arup using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

SEPTEMBER 18, 2023

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. The results of SUEWS are then visualized, in this case with Arup’s existing geospatial data platform.

Data Scientist

Data Scientist ML Machine Learning ML Engineer

Learnings From Building the ML Platform at Mailchimp

The MLOps Blog

OCTOBER 3, 2023

So I was able to get from growth hacking to data analytics, then data analytics to data science, and then data science to MLOps. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. How do I get this model in production?

ML

ML Data Scientist Machine Learning Data Science

Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And so that’s where we got started as a cloud data warehouse.

ML

ML Python Machine Learning Data Scientist

Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

MAY 26, 2023

What’s really important in the before part is having production-grade machine learning data pipelines that can feed your model training and inference processes. And that’s really key for taking data science experiments into production. And so that’s where we got started as a cloud data warehouse.

ML

ML Python Machine Learning Data Scientist

Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. As you’ve been running the ML data platform team, how do you do that?

ML

ML Data Scientist Software Engineer Python

Xavier Conort, Co-Founder and CPO of FeatureByte – Interview Series

Unite.AI

JUNE 28, 2023

Xavier Conort is a visionary data scientist with more than 25 years of data experience. He began his career as an actuary in the insurance industry before transitioning to data science. He’s a top-ranked Kaggle competitor and was the Chief Data Scientist at DataRobot before co-founding FeatureByte.

Data Scientist

Data Scientist Machine Learning Data Science ML Engineer

Definite Guide to Building a Machine Learning Platform

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning

Machine Learning Data Scientist ML Metadata

Artificial Intelligence Zone

MakeBlobs + Fictional Synthetic Data, Adding Data to Domain-Specific LLMs, and What Tech Layoffs…

Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

Webinars

Trending Sources

Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

Webinars

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

Machine Learning Operations (MLOPs) with Azure Machine Learning

The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

The Future of Data-Centric AI Day 2: Snorkel Flow and Beyond

Designing resilient cities at Arup using Amazon SageMaker geospatial capabilities

Learnings From Building the ML Platform at Mailchimp

Snowflake Snowpark: cloud SQL and Python ML pipelines

Snowflake Snowpark: cloud SQL and Python ML pipelines

Learnings From Building the ML Platform at Stitch Fix

Xavier Conort, Co-Founder and CPO of FeatureByte – Interview Series

Definite Guide to Building a Machine Learning Platform

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