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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.

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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

Be sure to check out his talk, “ How to Practice Data-Centric AI and Have AI Improve its Own Dataset ,” there! Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

With Azure Machine Learning, data scientists can leverage pre-built models, automate machine learning tasks, and seamlessly integrate with other Azure services, making it an efficient and scalable solution for machine learning projects in the cloud. Can you see the complete model lineage with data/models/experiments used downstream?

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LLMOps: What It Is, Why It Matters, and How to Implement It

The MLOps Blog

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. LLMOps is key to turning LLMs into scalable, production-ready AI tools.

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How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

AWS Machine Learning Blog

This workflow will be foundational to our unstructured data-based machine learning applications as it will enable us to minimize human labeling effort, deliver strong model performance quickly, and adapt to data drift.” – Jon Nelson, Senior Manager of Data Science and Machine Learning at United Airlines.