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International Contact Centre Operations Tips & Best Practices

Callminer

Use call recordings and ongoing training to nurture emotional competence among agents. Training agents to excel at their positions falls largely on teaching them to calmly coax positive results from negative situations. Emotional intelligence can be trained most effectively by refocusing your agents’ attention on their own behaviors.

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Outbound Call Center Tips & Best Practices

Callminer

Outbound call centers depend on skilled and well-trained agents as much as useful software to consistently meet business goals. Call centers that implement agent performance management solutions equip their agents with the ongoing coaching and training needed to perform at their best. Challenges Outbound Call Centers Face.

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Best Practices of Great Coaches

The Northridge Group

Frontline excellence training, a dedicated training program for managers focused on effective coaching, associate development, business outcomes and improved customer experience, can help managers enhance their coaching skills. Accountability is essential for coaches as well as associates.

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Scheduling Software for Call Centers: Buying Tips & Best Practices

Callminer

“What type of service, support, and training is offered? That’s why it’s important to make use of the best tools available for the job.” ” – 15 Best Practices For Effective Call Center Management , Sling. Best Practices for Leveraging Your Call Center’s Scheduling Software.

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6 Killer Applications for Artificial Intelligence in the Customer Engagement Contact Center

If Artificial Intelligence for businesses is a red-hot topic in C-suites, AI for customer engagement and contact center customer service is white hot. This white paper covers specific areas in this domain that offer potential for transformational ROI, and a fast, zero-risk way to innovate with AI.

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Security best practices to consider while fine-tuning models in Amazon Bedrock

AWS Machine Learning

Fine-tuning pre-trained language models allows organizations to customize and optimize the models for their specific use cases, providing better performance and more accurate outputs tailored to their unique data and requirements. Model customization in Amazon Bedrock involves the following actions: Create training and validation datasets.

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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning

Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical. The size of an LLM and its training data is a double-edged sword: it brings modeling quality, but entails infrastructure challenges.