IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) have taken the field of AI by storm. Scale and accelerate the impact of AI There are several steps to building and deploying a foundational model (FM). IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere.

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems Explainable AI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.

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

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., The next section delves into these architectural patterns, exploring how they are leveraged in machine learning pipelines to streamline data ingestion, processing, model training, and deployment.

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

Pickl AI

MAY 15, 2024

Prioritize Data Quality Implement robust data pipelines for data ingestion, cleaning, and transformation. Use tools like Apache Airflow to orchestrate these pipelines and ensure consistent data quality for model training and production use.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52