GenAI Accelerator: Consulting services

Our GenAI Accelerator consulting services offers comprehensive resources for productionizing your GenAI applications. Expert AI leaders from Weights & Biases—who built the AI platform used by the world’s foundation model builders such as OpenAI and Meta—help you create GenAI apps that perform reliably, scale efficiently, remain compliant with regulations, and deliver a compelling user experience.

Our team will guide you through everything required, including infrastructure scalability, model optimization, observability, monitoring, and implementing guardrails. Sign up today to tap into the experts who helped deploy AI at scale for thousands of enterprises like Toyota and NVIDIA.

James Cham
Partner – Bloomberg Beta

“GenAI Accelerator was transformational for our team. Weights & Biases’s depth of knowledge on GenAI is unmatched—it’s clear why nearly every foundation model is built on their platform. Highly recommended for anyone serious about leveraging GenAI in their business.”

David Rogier
CEO – MasterClass

“The support we’ve received from Weights & Biases has been exceptional. They have been invaluable as thought partners and guides throughout our journey. Their deep expertise not only allowed us to anticipate potential roadblocks well in advance but also equipped us with well-considered solutions. Thanks to their guidance, we were able to bring the product to market more swiftly and with fewer challenges.”

Consulting services

Get hands-on support as you develop and deploy your GenAI apps. We offer project-based advisory services with flexible timelines spanning one month to a year. We advise on design decisions or can handle the entire deployment on your behalf. Consulting services are designed to ensure reliable, scalable, and responsible deployment of GenAI models in real-world settings. We can engage on any GenAI topic. Explore the topics below, and pick the ones you need the most.

Prompts and model tuning


Develop a library of reusable, enterprise-level prompt templates, implement continuous prompt optimization, and manage prompt versions to ensure consistent model interactions and keep end-users happy. Know when to set up fine-tuning models. 

 

Observability and iteration


Implement AI observability solutions to support model refinement and develop insights into the decision making process. Set up logging for input prompts, output responses, and latency, and establish a process for continuous improvement. Monitor over time to maintain reliablity.

Deployment and rollout


Plan production rollout strategy and set up CI/CD pipelines for model and application updates. Implement robust error handling and fallback mechanisms to ensure robust and reliable systems.

Security and compliance


Establish strong authentication and access controls to protect against data breaches. Set up encryption and conduct regular security audits and penetration testing to safeguard data. Implement secure API endpoints to prevent.

Quality assurance and evaluation


Automate test generation through a systematic evaluation framework to ensure maximum test coverage. Establish continuous monitor and spot issues early. Align model performance with business objectives.

 

Infrastructure optimization


Implement cloud-agnostic infrastructure that optimizes resources, maximizes performance, and reduces costs. Use top techniques to reduce model size, improve speed, and manage data efficiently. 

Safety and responsible AI


Establish hallucination prevention techniques, safety filters, and content moderation systems. Identify potential vulnerabilities and set up toxicity checks to ensure user safety and protect brand reputation.

 

Data management


Build workflows to improve data quality and keep model data up-to-date. Support data privacy and security requirements and set up data versioning and lineage tracking.

 

User experience and education


Design an intuitive user interface, clear documentation, and implement user feedback to improve user adoption and satisfaction. Build educational resources to ensure effective use of your GenAI app.

Performance and cost optimization


Implement caching mechanisms and auto-scaling to optimize resource allocation and maintain performance during peak usage. Monitor and optimize costs associated with model inference and implement rate limiting and usage quotas as needed to prevent abuse.

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