Generative AI is taking the world by storm as ChatGPT and Bard become household names, but organizations don’t know where to start. They worry about exposing their intellectual property, the costs of building their own models and what use cases to experiment with. This is no longer the situation with MontyCloud. Since Amazon Web Services (AWS) announced General Availability for Amazon Bedrock today, I can share a huge step forward in MontyCloud’s mission to democratize cloud. Our announcements in this blog help organizations of all sizes from SMBs to Enterprises, Managed Service Providers to Higher Education, and everyone in between. In minutes, MontyCloud’s Generative AI Blueprint and CloudOps Copilot, an Interactive AI CloudOps Agent, enable you to harness the power of generative AI and interactive AI safely in your business context without writing a line of code.
Build generative AI-powered applications on AWS that connect to your organization’s data securely within your environment. Maintain full visibility, control, and operational agility at every step. The blueprint provisions everything you need to start training your own models; you just select the data. Our initial tests show that organizations can start building their own Large Language Model (LLM) chat bots on AWS starting under $100 a month in AWS infrastructure costs! This means you don’t need to learn services like Amazon Bedrock or understand how to configure Amazon SageMaker, Amazon OpenSearch, etc. More importantly, you won’t worry about setting up the correct data protection and generative AI policy guardrails. With the Generative AI blueprint, you can:
Business Example
A sales leader wants to provide sellers with self-service enablement. The sales leader provisions an entire generative AI environment along with a private chatbot in minutes and uploads sales and technical documents in a MontyCloud project. The sales leader and sellers ask the chat bot questions that draw directly from internal documentation, with guaranteed privacy and confidentiality.
Developer Example
A developer wants to build an LLM powered application. The developer or Cloud Admin provisions the Generative AI Blueprint that creates an approved and secure endpoint to start writing code. The application securely accesses the endpoint via Amazon API Gateway. The endpoint delivers responses based on the vector embeddings created and stored securely within the MontyCloud project. Now, the developer can build secure LLM applications that are within the organization’s guardrails.
I’m not a solutions architect, but now I can safely provision a generative AI environment, upload data and start interacting with a chatbot in minutes with MontyCloud. What are you waiting for?
Don’t be the last one to implement generative AI for your organization. MontyCloud enables you to get started in minutes.
We invite you to try MontyCloud so you can innovate more, operate less and transform your business. The generative AI blueprint is gated so contact the MontyCloud team to get started.
This is the second blog in our Generative AI/Interactive AI Solutions by MontyCloud series. The other blogs in this series are linked below.
Blog 1 - THE FUTURE OF AUTONOMOUS CLOUDOPS: FROM GENERATIVE AI TO INTERACTIVE AI by Founders, Venkat Krishnamachari and Kannan Parthasarathy.