IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

ODSC - Open Data Science

FEBRUARY 25, 2025

Generative AI TrackBuild the Future with GenAI Generative AI has captured the worlds attention with tools like ChatGPT, DALL-E, and Stable Diffusion revolutionizing how we create content and automate tasks. This track will cover the latest best practices for managing AI models from development to deployment.

Data Scientist 52

Data Scientist Large Language Models Machine Learning ML Engineer 52

ODSC - Open Data Science

FEBRUARY 22, 2024

Stripling, PhD | Lead AI & ML Content Developer | Google Cloud In a no-code or low-code world you don’t have to have mastered coding to deploy machine learning models. 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

NVIDIA

MARCH 18, 2025

While a traditional data center typically handles diverse workloads and is built for general-purpose computing, AI factories are optimized to create value from AI. They orchestrate the entire AI lifecycle from data ingestion to training, fine-tuning and, most critically, high-volume inference.

AI 61

AI AI Data Platform Data Ingestion 61

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

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g., TensorFlow, PyTorch) 5 Model Evaluation (e.g., Scikit-learn, MLflow) 6 Model Deployment (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g.,

ML 52

ML Machine Learning Data Ingestion Deep Learning 52