AWS Machine Learning Blog

AUGUST 20, 2024

You can call the SageMaker ListWorkteams or DescribeWorkteam APIs to view workteams’ metadata, including the WorkerAccessConfiguration. Abhinay Sandeboina is a Engineering Manager at AWS Human In The Loop (HIL). He has been in AWS for over 2 years and his teams are responsible for managing ML platform services.

Software Engineer 102

Software Engineer ML Machine Learning Metadata 102

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Only prompt engineering is necessary for better results.

Generative AI 120

Generative AI Prompt Engineer Prompt Engineering ML 120

AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. The main challenges were integrating a preprocessing step and accommodating two model artifacts per model definition.

Generative AI 73

Generative AI Auto-complete AI Modeling Machine Learning 73

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” That is definitely a problem. So does that mean feature selection is no longer necessary? And I can get us started here.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” That is definitely a problem. So does that mean feature selection is no longer necessary? And I can get us started here.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” That is definitely a problem. So does that mean feature selection is no longer necessary? And I can get us started here.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

AWS Machine Learning Blog

JUNE 8, 2023

A session stores metadata and application-specific data known as session attributes. Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. Prompts function as a form of context that helps direct the model toward generating relevant responses.

Generative AI 83

Generative AI LLM Machine Learning Large Language Models 83

The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. I term it as a feature definition store. I also recently found out, you are the CEO of DAGWorks.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: You definitely got the details correct. I joined FeatureForm last October, and before that, I was with Mailchimp on their ML platform team. I definitely don’t think I’m an influencer. I see so many of these job seekers, especially on the MLOps side or the ML engineer side.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MARCH 15, 2023

Cost and resource requirements There are several cost-related constraints we had to consider when we ventured into the ML model deployment journey Data storage costs: Storing the data used to train and test the model, as well as any new data used for prediction, can add to the cost of deployment. S3 buckets. Redshift, S3, and so on.

ETL 52

ETL Data Drift Machine Learning ML 52

The MLOps Blog

MARCH 28, 2023

quality attributes) and metadata enrichment (e.g., Each time they modify the code, the definition of the pipeline changes. Regarding other teams, they may approach testing ML models differently, especially in tabular ML use cases, by testing on sub-populations of the data.

Machine Learning 59

Machine Learning Data Scientist Automation ML 59

The MLOps Blog

MARCH 21, 2023

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. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

JANUARY 13, 2023

Proper AWS Identity and Access Management (IAM) role definition for the experimentation workspace was hard to define. To mitigate this, TR switched to using more customer-managed policies and referencing them in the workspace role definition. TR overcame this error by orchestrating the workflows using Step Functions. Model deployment.

ML 85

ML Data Scientist Machine Learning Metadata 85

AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists collaborate with ML engineers to transition code from notebooks to repositories, creating ML pipelines using Amazon SageMaker Pipelines, which connect various processing steps and tasks, including pre-processing, training, evaluation, and post-processing, all while continually incorporating new production data.

LLM 99

LLM Data Scientist Algorithm Generative AI 99