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Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

AWS Machine Learning

In this post, we share comprehensive best practices and scientific insights for fine-tuning Meta Llama 3.2 Our recommendations are based on extensive experiments using public benchmark datasets across various vision-language tasks, including visual question answering, image captioning, and chart interpretation and understanding.

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QBR Meeting Agendas: Best Practices for QBR Prep

Totango

Best practices for effective QBR meetings. For SaaS B2B clients, QBR meetings tend to focus on assessing value as measured by KPI performance benchmarks. Generate a report summarizing KPIs benchmarks from the last QBR and progress toward them. Use Benchmarking Data. How to run your meeting. Demonstrate ROI.

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Customer Success in SaaS: A Complete Guide & Best Practices

Totango

The software service industry presents unique challenges for customer success management while also creating unique opportunities that call for specific strategies. SaaS success outcomes can be defined in terms of measurable digital benchmarks. Customer success in SaaS differs from CS in other industries.

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Cohere Embed multimodal embeddings model is now available on Amazon SageMaker JumpStart

AWS Machine Learning

Cohere Embed 3 makes it simple to locate specific UI mockups, visual templates, and presentation slides based on a text description. All text-to-image benchmarks are evaluated using Recall@5 ; text-to-text benchmarks are evaluated using NDCG@10. Generic text-to-image benchmark accuracy is based on Flickr and CoCo.

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Best Practices to Ensure a Successful Quarterly Business Review

Totango

Use benchmarking data. QBR Best Practices . Here are a few very simple best practices to follow to help make sure your next QBR is successful and engaging. . People don’t want to just be presented to; they want an engaging conversation. Invite the right people. Encourage collaboration.

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Improve Amazon Nova migration performance with data-aware prompt optimization

AWS Machine Learning

To mitigate this challenge, thorough model evaluation, benchmarking, and data-aware optimization are essential, to compare the Amazon Nova models performance against the model used before the migration, and optimize the prompts on Amazon Nova to align performance with that of the previous workload or improve upon them.

Metrics 84
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2020 Call Center Metrics: 6 Key Metrics for Your Call Center Dashboard

Callminer

To get the most out of this metric, use it to inform budgeting and infrastructure-related decisions as opposed to using it for agent benchmarking purposes. By harnessing the information each of the above metrics presents you with, it is possible to consistently improve your call center’s performance over time.