The world of AI and analytics is evolving at a breakneck pace. Staying ahead requires more than just keeping up, it demands hands-on access to the latest innovations and insights from industry thought leaders. That’s precisely what ClouderaNOW, our quarterly virtual event series, is designed to deliver.
ClouderaNOW provides direct access to AI advancements, machine learning strategies, and real-world use cases. Through interactive demos, customer success stories, and live Q&A, attendees gain the knowledge and tools to turn AI potential into real business impact.
In our most recent ClouderaNOW series, we hosted five webinars exploring the latest trends in AI adoption. Here’s a recap of the key takeaways from our first event.
The State of AI Adoption
AI is no longer a futuristic concept but rather it’s a critical part of modern business strategy. According to Cloudera’s State of Enterprise AI survey, 50% of businesses already use Generative AI (GenAI). Even more notable: 0% reported having no plans to adopt it or actively ban it. This means every business, regardless of industry, is exploring AI in some capacity.
Jake Bengston, Cloudera’s global AI solution director, covered how many organizations approach AI adoption. They often begin their AI journey by leveraging managed services through external APIs such as ChatGPT, Claude, or Gemini. This initial phase allows companies to quickly test AI’s capabilities, often with chatbots, content generation, or internal workflow automation, without the overhead of building and managing AI infrastructure. It can be a quick way to show AI’s ROI.
However, as businesses start integrating AI into their workflows, they hit a key limitation: off-the-shelf models fail to fully align with industry-specific needs. To enhance AI performance and relevance, companies begin customizing models using techniques like prompt engineering, Retrieval-Augmented Generation (RAG), or fine-tuning to incorporate their proprietary data.
As AI matures within an organization, businesses often transition to open-source models like LLaMA, Gemma, or DeepSeek to increase control, enhance security, and reduce long-term costs. Doing so opens up more possibilities.
The Shift to Open-Source AI Models
Open-source AI is becoming the preferred choice for businesses that need customization, privacy, and cost control. By self-hosting models, companies can fine-tune AI for industry-specific applications while ensuring sensitive data remains secure. This is crucial as data-driven insights increasingly drive competitive advantage.
Cost is also a key factor. Managed AI services typically charge per-token fees, which can quickly scale and become unpredictable. Cloudera’s internal benchmarking found that hosting an open-source AI model within Amazon Web Services (AWS) can reduce costs by up to 40% compared to API-based alternatives. As businesses prioritize cost efficiency and data control, many are shifting toward custom AI solutions that balance performance, privacy, and sustainability.
As more businesses embrace open-source AI to gain greater control over their models and costs, the challenge shifts from simply adopting AI to efficiently deploying and scaling it. To bridge this gap, Cloudera provides Accelerators for ML Projects (AMPs), which are pre-built, one-click deployment solutions that streamline the transition from AI experimentation to production.
Accelerating AI with Cloudera’s AMPs
Many data scientists don’t build AI models from the ground up. Instead, they adapt existing solutions, which can lead to quality issues, integration challenges, and inefficiencies. Cloudera AMPs solve these problems by providing tested, production-ready AI accelerators that work seamlessly within Cloudera’s ecosystem. In addition to accelerating AI projects, Cloudera AMPs are fully open source and include deployment instructions for any environment, serving as further testament of Cloudera’s commitment to the open source community.
AMPs in Action
ClouderaNOW covered two key Cloudera AMPs helping enterprises reach AI production faster:
- Fine-Tuning Studio
Fine-tuning allows businesses to adapt large language models (LLMs) for specific tasks, but traditionally, this requires deep technical expertise. Cloudera’s Fine-Tuning Studio is a one-stop-shop studio application that covers the entire workflow and lifecycle of fine tuning, evaluating, and deploying fine-tuned LLMs in Cloudera’s AI Workbench
Fine-tuned models often outperform larger, generic AI models for specialized tasks while also reducing computational costs. Developers, data scientists, solution engineers, and all AI practitioners working within Cloudera’s AI ecosystem can easily organize data, models, training jobs, and evaluations related to fine tuning LLMs.
- RAG with Knowledge Graph
RAG enhances AI accuracy by integrating real-time, domain-specific data. However, standard RAG implementations rely solely on vector search, which can overlook important contextual relationships between pieces of information.
Cloudera’s RAG with Knowledge Graph AMP improves AI responses by combining vector search with Neo4j-powered knowledge graphs. This helps contextual understanding by establishing meaningful connections between data points. By prioritizing authoritative sources over purely semantic matches, it ensures AI-generated responses are more factually reliable and relevant to users' specific needs.
Cloudera’s AMPs help businesses transition from AI experimentation to real-world deployment. Rather than building models from scratch, organizations can leverage tested, ready-to-use solutions that seamlessly integrate with enterprise environments.
More Insights on Accelerating AI Adoption
To dive deeper into how Cloudera is helping businesses accelerate AI adoption, watch the full webinar here. Want to stay updated on the latest innovations? Sign up for upcoming ClouderaNOW events here.
