# Hyperparameter optimization

Published articles for Hyperparameter optimization.

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## Introducing TabFM: A zero-shot foundation model for tabular data

DevFeed: [Introducing TabFM: A zero-shot foundation model for tabular data](<https://devfeed.tech/articles/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data-6829.md>)

Original publisher: [Read original article](<https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/>)

Published: 2026-06-30T10:26:00Z

Content type: article

Language: en

Sources: [The latest research from Google](<https://devfeed.tech/sources/the-latest-research-from-google.md>)

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Google](<https://devfeed.tech/topics/google.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Hyperparameter optimization](<https://devfeed.tech/topics/hyperparameter-optimization.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [BigQuery](<https://devfeed.tech/topics/bigquery.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>)

Tags: [bigquery](<https://devfeed.tech/tags/bigquery.md>), [classification](<https://devfeed.tech/tags/classification.md>), [data](<https://devfeed.tech/tags/data.md>), [data-management](<https://devfeed.tech/tags/data-management.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [github](<https://devfeed.tech/tags/github.md>), [google](<https://devfeed.tech/tags/google.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [hyperparameter-optimization](<https://devfeed.tech/tags/hyperparameter-optimization.md>), [machine-intelligence](<https://devfeed.tech/tags/machine-intelligence.md>), [model](<https://devfeed.tech/tags/model.md>), [product](<https://devfeed.tech/tags/product.md>), [zero-shot](<https://devfeed.tech/tags/zero-shot.md>)

### AI overview

Google Research introduces TabFM, a zero-shot foundation model for tabular-data classification and regression. It frames prediction as in-context learning, reducing the need for dataset-specific training, hyperparameter optimization, and feature engineering, with availability through Hugging Face, GitHub, and BigQuery.

### Source excerpt

Data Management

## Making LLMs faster without sacrificing accuracy

DevFeed: [Making LLMs faster without sacrificing accuracy](<https://devfeed.tech/articles/making-llms-faster-without-sacrificing-accuracy-7603.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/making-llms-faster-without-sacrificing-accuracy>)

Author: Tao Yu; Youngsuk Park

Published: 2026-05-15T13:00:00Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [web applications](<https://devfeed.tech/topics/web-applications.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [chinchilla-scaling-law](<https://devfeed.tech/tags/chinchilla-scaling-law.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [grouped-query-attention](<https://devfeed.tech/tags/grouped-query-attention.md>), [hyperparameter-optimization](<https://devfeed.tech/tags/hyperparameter-optimization.md>), [iclr-2026](<https://devfeed.tech/tags/iclr-2026.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-efficiency](<https://devfeed.tech/tags/inference-efficiency.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm-optimization](<https://devfeed.tech/tags/llm-optimization.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [network-architectures](<https://devfeed.tech/tags/network-architectures.md>), [scaling-laws](<https://devfeed.tech/tags/scaling-laws.md>), [training](<https://devfeed.tech/tags/training.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

The article presents scaling laws that connect LLM architectural choices to the tradeoff between accuracy and efficiency. It describes how these choices can improve inference throughput without reducing accuracy.

### Source excerpt

A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.

## Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience

DevFeed: [Kubeflow SDK v0.4.0: Model Registry, SparkConnect, and Enhanced Developer Experience](<https://devfeed.tech/articles/kubeflow-sdk-v0-4-0-model-registry-sparkconnect-and-enhanced-developer-experience-17610.md>)

Original publisher: [Read original article](<https://blog.kubeflow.org/kubeflow-sdk-0.4.0-release/>)

Author: Kubeflow SDK Team

Published: 2026-03-19T05:00:00Z

Content type: release

Language: en

Sources: [Kubeflow](<https://devfeed.tech/sources/kubeflow.md>)

Topics: [SDKs](<https://devfeed.tech/topics/sdks.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Apache Spark](<https://devfeed.tech/topics/spark.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [Hyperparameter optimization](<https://devfeed.tech/topics/hyperparameter-optimization.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Python](<https://devfeed.tech/topics/python.md>)

Tags: [apache-spark](<https://devfeed.tech/tags/apache-spark.md>), [data-processing](<https://devfeed.tech/tags/data-processing.md>), [hyperparameter-optimization](<https://devfeed.tech/tags/hyperparameter-optimization.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [python](<https://devfeed.tech/tags/python.md>), [release](<https://devfeed.tech/tags/release.md>), [sdk](<https://devfeed.tech/tags/sdk.md>)

### AI overview

Kubeflow SDK v0.4.0 introduces a Model Registry Client, SparkClient with SparkConnect support, namespaced TrainingRuntimes, dataset and model initializers, and new documentation. The release targets a unified Python interface for AI workloads on Kubernetes across data processing, model management, and ML pipelines.

### Source excerpt

Explore the full documentation at sdk.kubeflow.org