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Foundational models at the edge

IBM Journey to AI blog

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. What are large language models?

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Up Your Machine Learning Game With These ODSC East 2024 Sessions

ODSC - Open Data Science

Causal AI: from Data to Action Dr. 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. No-Code and Low-Code AI: A Practical Project Driven Approach to ML ​​Gwendolyn D.

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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., 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.,

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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.