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Foundational models at the edge

IBM Journey to AI blog

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.

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Introducing the Topic Tracks for ODSC East 2025: Spotlight on Gen AI, AI Agents, LLMs, & More

ODSC - Open Data Science

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.

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Up Your Machine Learning Game With These ODSC East 2024 Sessions

ODSC - Open Data Science

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.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

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.

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ML Pipeline Architecture Design Patterns (With 10 Real-World Examples)

The MLOps Blog

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.

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Strategies for Transitioning Your Career from Data Analyst to Data Scientist–2024

Pickl AI

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.