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Tag: ML inference

  • Cloud Blog: 11 ways to reduce your Google Cloud compute costs today

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/compute/cost-saving-strategies-when-migrating-to-google-cloud-compute/ Source: Cloud Blog Title: 11 ways to reduce your Google Cloud compute costs today Feedly Summary: As the saying goes, “a penny saved is a penny earned," and this couldn’t be more true when it comes to cloud infrastructure. In today’s competitive business landscape, you need to maintain the performance to meet…

  • Cloud Blog: Connect Spark data pipelines to Gemini and other AI models with Dataproc ML library

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/data-analytics/gemini-and-vertex-ai-for-spark-with-dataproc-ml-library/ Source: Cloud Blog Title: Connect Spark data pipelines to Gemini and other AI models with Dataproc ML library Feedly Summary: Many data science teams rely on Apache Spark running on Dataproc managed clusters for powerful, large-scale data preparation. As these teams look to connect their data pipelines directly to machine learning models,…

  • AWS News Blog: Announcing Amazon EC2 M4 and M4 Pro Mac instances

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    Kurt Seifried
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    Source URL: https://aws.amazon.com/blogs/aws/announcing-amazon-ec2-m4-and-m4-pro-mac-instances/ Source: AWS News Blog Title: Announcing Amazon EC2 M4 and M4 Pro Mac instances Feedly Summary: AWS has launched new EC2 M4 and M4 Pro Mac instances based on Apple M4 Mac mini, offering improved performance over previous generations and featuring up to 48GB memory and 2TB storage for iOS/macOS development workloads.…

  • Cloud Blog: New GKE inference capabilities reduce costs, tail latency and increase throughput

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/understanding-new-gke-inference-capabilities/ Source: Cloud Blog Title: New GKE inference capabilities reduce costs, tail latency and increase throughput Feedly Summary: When it comes to AI, inference is where today’s generative AI models can solve real-world business problems. Google Kubernetes Engine (GKE) is seeing increasing adoption of gen AI inference. For example, customers like HubX run…

  • Cloud Blog: An SRE’s guide to optimizing ML systems with MLOps pipelines

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/devops-sre/applying-sre-principles-to-your-mlops-pipelines/ Source: Cloud Blog Title: An SRE’s guide to optimizing ML systems with MLOps pipelines Feedly Summary: Picture this: you’re an Site Reliability Engineer (SRE) responsible for the systems that power your company’s machine learning (ML) services. What do you do to ensure you have a reliable ML service, how do you know…

  • Cloud Blog: Announcing smaller machine types for A3 High VMs

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/compute/announcing-smaller-machine-types-for-a3-high-vms/ Source: Cloud Blog Title: Announcing smaller machine types for A3 High VMs Feedly Summary: Today, an increasing number of organizations are using GPUs to run inference1 on their AI/ML models. Since the number of GPUs needed to serve a single inference workload varies, organizations need more granularity in the number of GPUs…

  • The Register: Foundation model for tabular data slashes training from hours to seconds

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    Kurt Seifried
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    Source URL: https://www.theregister.com/2025/01/15/foundation_model_tabular_data/ Source: The Register Title: Foundation model for tabular data slashes training from hours to seconds Feedly Summary: Good ol’ spreadsheet data could benefit from ‘revolutionary’ approach to ML inferences Move over ChatGPT and DALL-E: Spreadsheet data is getting its own foundation machine learning model, allowing users to immediately make inferences about new…