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

By Noam Harel In the ever-changing landscape of the pharmaceutical industry, the integration of generative artificial intelligence (AI) holds immense promise and potential alongside risk, patient and consumer safety and tight regulation. Generative AI refers to the ability of machines to autonomously create new and unique content, ideas, or solutions.

By Noam Harel In the fast-paced and ever-evolving business landscape, innovation has become the lifeblood of success. Yet, many organizations fail to harness the full potential of innovation due to a significant gap between their business units. This gap, like a hidden chasm, prevents the sharing of best practices, stifling growth and hindering progress.

An overview of common low-hanging fruits to help you get started with machine learning.

In our data-driven world, the landscape of product analytics is rapidly evolving. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), we're seeing a seismic shift in how businesses approach product development and enhancement. But how does AI and ML fit into product analytics, particularly for non-technical business leaders and marketers? And more importantly, what does this mean for the future? ‍

Generative AI stands out from other technological breakthroughs due to its remarkable velocity and unprecedented speed. In a matter of mere months since its initial emergence in the limelight, this cutting-edge innovation has already achieved scalability, aiming to attain substantial return on investment. However, it is imperative to effectively harness this formidable technology, ensuring that it can deploy on a large scale and yield outcomes that garner trust from your business stakeholders.

It seems like we are witnessing a new quantum leap of technological advancement, with Generative AI taking the world by storm earlier this year. Generative AI (GenAI) has emerged as a powerful tool that combines artificial intelligence with creativity, empowering machines to generate original content, such as images, music, and even text, that imitates human-like creativity from structured and unstructured data.

At Snowflake, we’re helping data scientists, data engineers, and application developers build faster and more efficiently in the Data Cloud. That’s why at our annual user conference, Snowflake Summit 2023, we unveiled new features that further extend data programmability in Snowflake for their language of choice, without having to compromise on governance.

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in.

A flexible, automated analytic workflow tool that integrates with BigQueryML allows no-code forecasting and model training.