AWS Machine Learning Blog

FEBRUARY 27, 2024

However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. The model will be approved by designated data scientists to deploy the model for use in production. Furthermore, several new functionalities have been added in an automated fashion to our ML setup.

Machine Learning 140

Machine Learning DevOps Data Scientist Data Quality 140

ODSC - Open Data Science

MARCH 27, 2023

ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Some of the questions you’ll explore include How much documentation is appropriate?

Machine Learning 98

Machine Learning Data Ingestion Deep Learning Explainability 98

AWS Machine Learning Blog

OCTOBER 2, 2024

Model transparency – Although achieving full transparency in generative AI models remains challenging, organizations can take several steps to enhance model transparency and explainability: Provide model cards on the model’s intended use, performance, capabilities, and potential biases.

Generative AI 133

Generative AI Data Ingestion AI AI 133

AWS Machine Learning Blog

JUNE 27, 2023

They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 126

DevOps ML Machine Learning ML Engineer 126

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. We recognize that customers have different starting points.

ML 126

ML Machine Learning Data Science Data Drift 126

ODSC - Open Data Science

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows.

Machine Learning 52

Machine Learning Data Science Python Data Ingestion 52

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134