The retail industry has been shaped and fundamentally transformed by disruptive technologies in the past decade. From AI assisted customer service experiences to advanced robotics in operations, retailers are pursuing new technologies to address margin strains and rising customer expectations.

Marketing and sales is undergoing a profound transformation as generative AI (gen AI) paves the way for advancements and innovation. With gen AI, businesses are rethinking their approaches to customer experience, productivity, revenue, and growth in both the B2B and the B2C domains.

Data Integration plays a crucial role in enhancing the performance and accuracy of machine learning models. In today's rapidly evolving digital landscape, businesses and organizations are constantly generating vast amounts of data from various sources. However, the true power of this data can only be unleashed when it is seamlessly integrated and prepared for machine learning.

MLOps accelerates the ML model deployment process to make it more efficient and scalable. This is done through automation and additional techniques that help streamline the process. Looking to improve your MLOps knowledge and processes? You’ve come to the right place. In this blog post, we detail the steps you need to take to build and run a successful MLOps pipeline.

The nursing profession is in crisis. According to McKinsey, over 30% of surveyed nurses said they may leave their current patient care jobs in the next year, and for inpatient nurses it’s higher at 45%. Meanwhile, the average professional tenure of nurses dropped from 3.6 years to 2.8 years between 2020 and 2023. These alarming trends have healthcare systems on red alert. Ninety-four percent of surveyed health system senior executives said the nursing shortage is critical.

Generative AI has already had a massive impact on business and society, igniting innovation while delivering ROI and real economic value. According to research by QuantumBlack, AI by McKinsey, titled “The economic potential of generative AI”, generative AI use cases have the potential to add $2.6T to $4.4T annually to the global economy. This potential spans more than 60 use cases across all industries.

We all know that organizations face a huge challenge in extracting valuable insights from vast amounts of data. Chief Data Officers (CDOs) and Chief Data Analytics Officers (CDAOs) play a key role in this process, as they are responsible for managing and leveraging organizational data to drive sustainable and responsible growth. One technology that has revolutionized the way they unlock value from business data is generative artificial intelligence (AI).

The world of knowledge work is undergoing a profound transformation as generative AI emerges as a powerful force driving innovation, efficiency, and productivity. With its ability to analyze vast amounts of data, generate insights, and streamline complex tasks, generative AI is reshaping the way professionals work and unlocking new possibilities. It also raises fears of replacing knowledge workers with Generative AI.

In today’s rapidly evolving technological landscape, generative artificial intelligence (AI) has emerged as a powerful tool for various industries, and it seems like enterprises are fast to adopt it. Generative AI refers to the use of machine learning algorithms to generate original and creative content such as images, text, or music.

With custom holiday modeling features, BigQuery users can build more powerful and accurate time-series forecasting models using BigQuery ML.