AWS Machine Learning Blog

JANUARY 26, 2024

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. Data interactions: Monitor data quality and usage Generative AI models such as LLMs generally work well because they have been trained on a large amount of data.

Generative AI 122

Generative AI LLM ML AI 122

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. These skilled professionals are tasked with building and deploying models that improve the quality and efficiency of BMW’s business processes and enable informed leadership decisions.

ML 95

ML DevOps Data Scientist ML Engineer 95

AWS Machine Learning Blog

MAY 15, 2024

Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). The workflow allowed the Amazon Ads team to experiment with different foundation models and configurations through blind A/B testing to ensure that feedback to the generated images is unbiased. with minimal effort.

Generative AI 90

Generative AI AI AI Machine Learning 90

AWS Machine Learning Blog

APRIL 29, 2024

We then show how to set up the infrastructure stack you need to take your own data assets and pre-train or fine-tune a state-of-the-art Llama2 model on Trainium hardware. How Metaflow integrates with Trainium From a Metaflow developer perspective, using Trainium is similar to other accelerators.

ML 99

ML Python Data Scientist Deep Learning 99

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Several models fail and many just reach the production stage.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

The MLOps Blog

JULY 18, 2023

Build vs. open-source : which conversational AI model should you choose? Stephen: It seems like a lot of models and sequences. And even on the operation side of things, is there a separate operations team, and then you have your research or ml engineers doing these pipelines and stuff?

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

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 69

Machine Learning Data Scientist ML Metadata 69