In today’s data-driven enterprises, the intersection of fiscal responsibility and technical innovation has never been more critical. As data processing costs continue to scale with business growth, building a FinOps culture within your Data Engineering team isn’t just about cost control, it’s about creating a mindset that views cost optimization as an integral part of technical excellence.

We’re thrilled to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP): “Summarization with Gemini from Vertex AI”. An AMP is a pre-built, high-quality minimal viable product (MVP) for Artificial Intelligence (AI) use cases that can be deployed in a single-click from Cloudera AI (CAI). AMPs are all about helping you quickly build performant AI applications. More on AMPs can be found here.

As organizations increasingly integrate AI into day-to-day operations, scaling AI solutions effectively becomes essential yet challenging. Many enterprises encounter bottlenecks related to data quality, model deployment, and infrastructure requirements that hinder scaling efforts. Cloudera tackles these challenges with the AI Inference service and tailored Solution Patterns developed by Cloudera’s Professional Services, empowering organizations to operationalize AI at scale across industries.

Artificial Intelligence promises to transform lives and business as we know it. But what does that future look like? The AI Forecast: Data and AI in the Cloud Era, sponsored by Cloudera, aims to take an objective look at the impact of AI on business, industry, and the world at large.

Welcome to the first installment of a series of posts discussing the recently announced Cloudera AI Inference service. Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. This is where the Cloudera AI Inference service comes in.

For more than a decade, Cloudera has been an ardent supporter and committee member of Apache NiFi, long recognizing its power and versatility for data ingestion, transformation, and delivery. Our customers rely on NiFi as well as the associated sub-projects (Apache MiNiFi and Registry) to connect to structured, unstructured, and multi-modal data from a variety of data sources – from edge devices to SaaS tools to server logs and change data capture streams.

Today enterprises can leverage the combination of Cloudera and Snowflake—two best-of-breed tools for ingestion, processing and consumption of data—for a single source of truth across all data, analytics, and AI workloads. But now AWS customers will gain more flexibility, data utility, and complexity, supporting the modern data architecture.

Many enterprises have heterogeneous data platforms and technology stacks across different business units or data domains. For decades, they have been struggling with scale, speed, and correctness required to derive timely, meaningful, and actionable insights from vast and diverse big data environments. Despite various architectural patterns and paradigms, they still end up with perpetual “data puddles” and silos in many non-interoperable data formats.

As organizations adopt a cloud-first infrastructure strategy, they must weigh a number of factors to determine whether or not a workload belongs in the cloud. Cost has been a key consideration in public cloud adoption from the start. Today, energy efficiency is gaining importance, not only for cutting costs but also as a vital step toward sustainable business practices. By optimizing energy consumption, companies can significantly reduce the cost of their infrastructure.