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Accelerating sustainable modernization with Green IT Analyzer on AWS

IBM Journey to AI blog

Businesses are increasingly embracing data-intensive workloads, including high-performance computing, artificial intelligence (AI) and machine learning (ML). This situation triggered an auto-scaling rule set to activate at 80% CPU utilization. Due to the auto-scaling of the new EC2 instances, an additional t2.large

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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality. Auto-constructed data lineage : Helps visualize the flow of data through systems without the need for complex hand-coded solutions.

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Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. As the adoption of machine learning in various industries continues to grow, the demand for robust MLOps tools has also increased. What is MLOps?

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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. We recently developed four more new models.

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. DynamoDB is used to store the pet attributes.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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Carl Froggett, CIO of Deep Instinct – Interview Series

Unite.AI

Most cybersecurity tools leverage machine learning (ML) models that present several shortcomings to security teams when it comes to preventing threats. ML solutions also require heavy human intervention and are trained on small data sets, exposing them to human bias and error. Like other AI and ML models, our model trains on data.