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 Explainability ML 98

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 117

Machine Learning DevOps Data Quality Data Scientist 117

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 ML 52

ODSC - Open Data Science

JANUARY 10, 2024

Causal AI: from Data to Action Dr. Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. Sign me up! Register for ODSC East today to save 60% on any pass.

Large Language Models 52

Large Language Models LLM Deep Learning ML 52

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. Conduct exploratory analysis and data preparation.

ML 93

ML Machine Learning Data Science Data Drift 93

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

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. Data preprocessing.

ML 98

ML Machine Learning Metadata Automation 98