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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.
Topics Include: Advanced ML Algorithms & EnsembleMethods Hyperparameter Tuning & Model Optimization AutoML & Real-Time MLSystems ExplainableAI & EthicalAI Time Series Forecasting & NLP Techniques Who Should Attend: ML Engineers, Data Scientists, and Technical Practitioners working on production-level ML solutions.
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.
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 dataingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.
1 DataIngestion (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 dataingestion, processing, model training, and deployment.
Prioritize Data Quality Implement robust data pipelines for dataingestion, cleaning, and transformation. Use tools like Apache Airflow to orchestrate these pipelines and ensure consistent data quality for model training and production use.
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