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Announcing managed MCP servers with Unity Catalog and Mosaic AI Integration

databricks

Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data!

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

How fresh or real-time does the data need to be? What tools and data models best fit our requirements? Recommended actions: Clarify the business questions your pipeline will help answer Sketch a high-level architecture diagram to align technical and business stakeholders Choose tools and design data models accordingly (e.g.,

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From Hallucinations to Healing: Reducing Errors in AI for Healthcare

Towards AI

Last Updated on November 12, 2024 by Editorial Team Author(s): Prachi Tewari Originally published on Towards AI. Join thousands of data leaders on the AI newsletter. This member-only story is on us. Upgrade to access all of Medium. A single AI-generated error here could lead to serious consequences for patient health.

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Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

AWS Machine Learning Blog

Although QLoRA reduces computational requirements and memory footprint, FSDP, a data/model parallelism technique, will help shard the model across all eight GPUs (one ml.p4d.24xlarge 24xlarge ), enabling training the model even more efficiently.

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Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

AWS Machine Learning Blog

Make sure you’re updating the data model ( updateTrackListData function) to handle your custom fields. attr({ name: 'speaker', class: 'custom-dropdown-width' }); var speakerOptions = ['Speaker A', 'Speaker B', 'Multiple Speakers', 'Background Noise']; speakerOptions.forEach(function(option) { speakerDropdown.append($(' ').val(option).text(option));

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Integrate foundation models into your code with Amazon Bedrock

AWS Machine Learning Blog

Pass the prompt to the client: response = bedrock_runtime.invoke_model(**kwargs) response This will invoke the Amazon Bedrock model with the provided prompt and print the generated streaming body object response. I ate an ice-cream and swam in the sean nnProvide title between XML tagsnnAssistant:","max_tokens_to_sample":300,"temperature":0.5,"top_k":250,"top_p":1,"stop_sequences":["nnHuman:"],"anthropic_version":"bedrock-2023-05-31"}'}

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5 link visualization styles to showcase relationships

Cambridge Intelligence

Link styles arent just meant to look beautiful: they convey important information in a concise, efficient way Link visualization styles: best practice Our data visualization toolkit approach gives near-limitless styling options. Effective link styling should be an important consideration for your graph visualization data model.