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

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 225

Large Language Models DevOps AI Modeling Automation 225

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

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