Hugging Face is a popular model repository that provides simplified tools for building, training and deploying ML models. The growing adoption of Hugging Face usage among data professionals, alongside the increasing global need to become more efficient and sustainable when developing and deploying ML models, make Hugging Face an important technology and platform to learn and master.

We recently had a chance to catch up with Heather Grebe, Senior Data Scientist at Daupler, which offers Daupler RMS, a 311 response management system, used by more than 200 cities and service organizations across North America and internationally. This platform helps utilities, public works, and other service organizations coordinate and document response efforts while reducing workload and collecting insights into response operations.

BigQuery ML reduces data to AI barrier by making it easy to manage the end-to-end lifecycle from exploration to operationalizing ML models using SQL.

It’s no secret that advancements like AI and machine learning (ML) can have a major impact on business operations. In Cloudera’s recent report Limitless: The Positive Power of AI, we found that 87% of business decision makers are achieving success through existing ML programs. Among the top benefits of ML, 59% of decision makers cite time savings, 54% cite cost savings, and 42% believe ML enables employees to focus on innovation as opposed to manual tasks.

Most commonly, data teams have worked with structured data. Unstructured data, which includes images, documents, and videos, will account for up to 80 percent of data by 2025. However, organizations currently use only a small percentage of this data to derive useful insights. One of main ways to extract value from unstructured data is by applying ML to the data.