ODSC - Open Data Science

MARCH 27, 2023

Scaling AI/ML Workloads with Ray Kai Fricke | Senior Software Engineer | Anyscale Inc. In this session, you will learn how explainability can help you identify poor model performance or bias, as well as discuss the most commonly used algorithms, how they work, and how to get started using them. Why is it important?

Machine Learning 98

Machine Learning Data Ingestion Explainability ML 98

Towards AI

MARCH 28, 2024

Meme shared by ghost_in_the_machine TAI Curated section Article of the week The Design Shift: Building Applications in the Era of Large Language Models by Jun Li A new trend has recently reshaped our approach to building software applications: the rise of large language models (LLMs) and their integration into software development.

Large Language Models 75

Large Language Models Prompt Engineer Prompt Engineering AI 75

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. In particular, you’ll explore Google’s Vertex AI for both no-code and low-code ML model training, and Google’s Colab, a free Jupyter Notebook service.

Machine Learning 52

Machine Learning Data Science Python ML 52

AWS Machine Learning Blog

APRIL 26, 2023

Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. Connect with him on LinkedIn.

ML 88

ML Machine Learning Data Science Data Drift 88

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 Quality Data Scientist 134

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Is it a black-box model, or can the decisions be explained?

ML 98

ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good software engineering practices. My Story DevOps Engineers Who they are?

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69