Today we are excited to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP) for PDF document analysis, “Document Analysis with Command R and FAISS”, leveraging Cohere’s Command R Large Language Model (LLM), the Cohere Toolkit for retrieval augmented generation (RAG) applications, and Facebook’s AI Similarity Search (FAISS).

As technology evolves, the skills gap within IT departments continues to widen. New technologies are being adopted more quickly to keep pace with business changes, necessitating a constant update of skills. Yet, fully outsourcing these skills isn’t always the solution, as they are a core part of any IT organization. To effectively address this issue, we need a strategy that focuses on continually closing the skills gap.

BigQuery data preparation is an AI-first solution that streamlines and simplifies the data preparation process as part of Gemini in BigQuery.

From artificial intelligence (AI) to machine learning (ML) to conversational chatbots, the tools we use to interact with and consume information are rapidly changing thanks to powerful new technologies that make understanding our data more accessible than ever. One particularly influential field is natural language technology (NLT) and its branches.

Being able to create and share insightful data-led stories to support your dashboards and reports is a critical capability in today’s modern workplace. You want your data to be accessible for more people, and ensure everyone gets value from your investments. Data storytelling tools bring valuable context to the ‘why’ behind the results, while inspiring your audience to care about and act on insights.

As we transition into the next era of information technology, organizations face increasing pressure to deliver value to customers more rapidly. As well as higher quality, flexible access to data and applications and uncompromised security. Concurrently, they must manage growing complexity, including increasingly distributed hybrid cloud architectures and data sprawl – and accompanying costs – in order to remain competitive.

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI. One of the most important innovations in data management is open table formats, specifically Apache Iceberg, which fundamentally transforms the way data teams manage operational metadata in the data lake.

In recent years, machine learning operations (MLOps) have become the standard practice for developing, deploying, and managing machine learning models. MLOps standardizes processes and workflows for faster, scalable, and risk-free model deployment, centralizing model management, automating CI/CD for deployment, providing continuous monitoring, and ensuring governance and release best practices.

Companies are under pressure to deliver faster innovation, enabled by cloud-based data analytics and AI. In order to deliver faster business value, data teams are looking to achieve speed and scale through data and AI pipeline performance and efficiency. A recent MIT Technology Review Insights report finds that 72% of technology leaders agree that data challenges are the most likely factor to jeopardize AI/ML goals.

Modern data analytics platforms like Databricks have become indispensable for many companies. However Databricks costs can become unpredictable. This article gives four steps to help companies like yours predict Databricks costs and save your company time and money.