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MLOps and the evolution of data science

IBM Journey to AI blog

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. All code for this post is available in the GitHub repo.

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How to Choose MLOps Tools: In-Depth Guide for 2024

DagsHub

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. MLOps tools play a pivotal role in every stage of the machine learning lifecycle. What is MLOps?

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. Pay-as-you-go pricing makes it easy to scale when needed.

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Train An Emotion Recognition Model Using Multiple Datasets-Part 1

Mlearning.ai

We then go over all the project components and processes, from data preparation, model training, and experiment tracking to model evaluation, to equip you with the skills to construct your own emotion recognition model. The first way is to actually generate more data using ImageDataGenerator.

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

AWS Machine Learning Blog

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker.

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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

Flipboard

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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