Contextual AI and WEKA Partner for Enterprise Class AI Activation
The TechArena are fans of what WEKA is delivering in the market, and we’ve covered their data platform since last year as the company has unveiled innovations to help speed enterprise adoption of AI in market. It was, therefore, no surprise to us to see Contextual AI select WEKA as a strategic partner for delivery of enterprise AI services on Google Cloud this week. Contextual AI has made a name for itself with RAG 2.0, delivering enterprises fine-tuned models that provide foundational tools for enterprises wanting to build and customize specialized AI applications.
When you consider the application of contextual language models, a step beyond traditional RAG with integration across pre-training, fine tuning, and alignment with human feedback. Traditional RAG models uses an off-the-shelf model embedding, vector database for retrieval, and a distinct language model for generation, stitched together through an orchestration framework and therefore limiting both the final value of the application and difficulty and efficiency in delivery of the model.
RAG 2.0 was delivered, you guessed it, by the same leadership team that first delivered RAG at Facebook AI Research, so they know a bit about the RAG approach and how to make it better. RAG 2.0 has delivered proof of benefit across various industry benchmarks showcasing improved accuracy vs. RAG and Vanilla RAG models based on GPT-4 and Mixtral). That’s cool stuff.
When you consider how much enterprises are focusing on RAG implementations to bring AI to the mainstream, you can understand why Contextual AI is garnering significant attention for delivery of its solution to customers. And when you consider the scale of data required for RAG 2.0, WEKA emerges as a perfect partner to support data pipeline requirements.
WEKA has delivered a fantastic data management platform to optimize GPU utilization delivering peak efficiency in model performance as well as value to the customer funding the Google Cloud instance. Contextual AI has deployed a total of 100TB thus far of WEKA data platform capacity to fuel data requirements and has seen increased developer productivity and faster model training times with the new solution. The delta in performance is eye-opening with a stated 3X performance gain across key AI use cases and 4X faster AI model checkpointing. They’ve done all this while reducing cost 38% per terabyte.
What’s the TechArena take? We love stories about taking technology innovation and actually implementing to customer value. What Contextual AI and WEKA are delivering here are useful tools that enterprises can tap today for real adoption of AI within their environments. We can’t wait to see more advancement on the contextual language models as the industry at large continues its bandwagon of support of RAG type frameworks for AI fine tuning and deployment. And we’re delighted to see WEKA’s data management solutions continue to garner momentum in market for this once-in-a-lifetime moment of AI-era computing advancement.
The TechArena are fans of what WEKA is delivering in the market, and we’ve covered their data platform since last year as the company has unveiled innovations to help speed enterprise adoption of AI in market. It was, therefore, no surprise to us to see Contextual AI select WEKA as a strategic partner for delivery of enterprise AI services on Google Cloud this week. Contextual AI has made a name for itself with RAG 2.0, delivering enterprises fine-tuned models that provide foundational tools for enterprises wanting to build and customize specialized AI applications.
When you consider the application of contextual language models, a step beyond traditional RAG with integration across pre-training, fine tuning, and alignment with human feedback. Traditional RAG models uses an off-the-shelf model embedding, vector database for retrieval, and a distinct language model for generation, stitched together through an orchestration framework and therefore limiting both the final value of the application and difficulty and efficiency in delivery of the model.
RAG 2.0 was delivered, you guessed it, by the same leadership team that first delivered RAG at Facebook AI Research, so they know a bit about the RAG approach and how to make it better. RAG 2.0 has delivered proof of benefit across various industry benchmarks showcasing improved accuracy vs. RAG and Vanilla RAG models based on GPT-4 and Mixtral). That’s cool stuff.
When you consider how much enterprises are focusing on RAG implementations to bring AI to the mainstream, you can understand why Contextual AI is garnering significant attention for delivery of its solution to customers. And when you consider the scale of data required for RAG 2.0, WEKA emerges as a perfect partner to support data pipeline requirements.
WEKA has delivered a fantastic data management platform to optimize GPU utilization delivering peak efficiency in model performance as well as value to the customer funding the Google Cloud instance. Contextual AI has deployed a total of 100TB thus far of WEKA data platform capacity to fuel data requirements and has seen increased developer productivity and faster model training times with the new solution. The delta in performance is eye-opening with a stated 3X performance gain across key AI use cases and 4X faster AI model checkpointing. They’ve done all this while reducing cost 38% per terabyte.
What’s the TechArena take? We love stories about taking technology innovation and actually implementing to customer value. What Contextual AI and WEKA are delivering here are useful tools that enterprises can tap today for real adoption of AI within their environments. We can’t wait to see more advancement on the contextual language models as the industry at large continues its bandwagon of support of RAG type frameworks for AI fine tuning and deployment. And we’re delighted to see WEKA’s data management solutions continue to garner momentum in market for this once-in-a-lifetime moment of AI-era computing advancement.
