Leveraging AMPs for machine learning

The data and AI industries are constantly evolving, and it’s been several years full of innovation. Even less experienced technical professionals can now access pre-built technologies that accelerate the time from ideation to production. As a result, employers no longer have to invest large sums to develop their own foundational models. They can instead leverage the expertise of others across the globe in pursuit of their own goals.

However, the road to AI victory can be bumpy. Such a large-scale reliance on third-party AI solutions creates risk for modern enterprises. It’s hard for any one person or a small team to thoroughly evaluate every tool or model. Yet, today’s data scientists and AI engineers are expected to move quickly and create value. The problem is that it’s not always clear how to strike a balance between speed and caution when it comes to adopting cutting-edge AI.

As a result, many companies are now more exposed to security vulnerabilities, legal risks, and potential downstream costs. Explainability is also still a serious issue in AI, and companies are overwhelmed by the volume and variety of data they must manage. Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. It takes a highly sophisticated ML operation to build and maintain effective AI applications internally. The alternative is to take advantage of more end-to-end, purpose-built ML solutions from trusted enterprise AI brands.

Introducing Cloudera AMPs

To help data scientists and AI engineers, Cloudera has released several new Accelerators for LL Projects (AMPs). Cloudera’s AMPs are pre-built ML prototypes that users can deploy with a single click within Cloudera The new AMPs address common pain points across the ML lifecycle and enable data scientists and AI engineers to launch production-ready ML use cases quickly that follow industry best practices.

Rather than pursue enterprise AI initiatives with a combination of black box ML tools, Cloudera AMPs enable companies to centralize ML operations around a trusted AI leader. They reduce development time, increase cost-effectiveness for AI projects, and accelerate time to value without incurring the risks typically associated with third-party AI solutions. Each Cloudera AMP is a self-contained prototype that users can deploy within their own environments and are open-source projects, demonstrating the company’s commitment to serving the broader open-source ML community.

Let’s dive into Cloudera’s latest AMPs:

•  PromptBrew

The PromptBrew AMP is an AI assistant designed to help AI engineers create better prompts for LLMs. Many developers struggle to communicate effectively with their underlying LLMs, so the PromptBrew AMP bridges this skill gap by giving users suggestions on how to write and optimize prompts for their company’s use cases.

•  RAG with Knowledge Graph on CML

The RAG with Knowledge Graph AMP showcases how using knowledge graphs in conjunction with Retrieval-augmented generation can enhance LLM outputs even further. RAG is an increasingly popular approach for improving LLM inferences, and the RAG with Knowledge Graph AMP takes this further by empowering users to maximize RAG system performance.

•  Chat with Your Documents

The Chat with Your Documents AMP allows AI engineers to feed internal documents to instruction-following LLMs that can then surface relevant information to users through a chat-like interface. It guides users through training and deploying an informed chatbot, which can often take a lot of time and effort.

•  Fine-Tuning Studio

Lastly, the Fine-tuning Studio AMP simplifies the process of developing specialized LLMs for certain use cases. It allows data scientists to focus pre-existing models around specific tasks within a single ecosystem to manage, refine, and evaluate LLM performance.

A clearer path to ML success

With Cloudera AMPs, data scientists and AI engineers don’t have to take a leap of faith when adopting new ML tools and models. They can lean on AMPs to mitigate MLOps risks and guide them to long-term AI success. AMPs are catalysts to fast-track AI projects from concept to reality with pre-built solutions and working examples, ensuring that use cases are dependable and cost effective while reducing development time. Businesses no longer need to pour time and money into building everything in-house, companies can move fast in today’s hyper-competitive business landscape.

For more on Cloudera’s AMPs, click here.