Large language models (LLMs) have become synonymous with cutting-edge AI, capable of generating realistic text, translating languages, and writing different kinds of creative content. But what if you could leverage this power on your own machine, with complete privacy and control? Running LLMs locally might seem daunting, but it’s becoming increasingly accessible. Here’s a breakdown of why you might consider it, and how it’s easier than you think: The Allure of Local LLMs

Artificial intelligence (AI) is rapidly transforming our world, but it comes with a hidden cost: carbon emissions. According to a recent study by the Allen Institute for AI, training a single large language model can produce up to 550 tons of carbon dioxide, equivalent to the emissions of five cars over their lifetime. This is because AI training requires massive amounts of computing power, which in turn relies on electricity generated by fossil fuels.

Here is an example of how to use BigQuery ML on a public dataset to create a logistic regression model to predict whether a user will click on an ad: # Import the BigQuery ML library from google.cloud import bigquery from google.cloud.bigquery import Model # Get the dataset and table dataset = bigquery.Dataset("bigquery-public-data.samples.churn") table = dataset.table("churn") # Create a model model = Model('my_model', model_type='logistic_regression', input_label_column='churn', input_features_columns=['tenure', 'contract', 'monthly_charges']) # Train the model model.

BigQuery ML and Vertex AI Generative AI (GenAI) are both machine learning (ML) services that can be used to build and deploy ML models. However, there are some key differences between the two services. BigQuery ML: BigQuery ML is a fully managed ML service that allows you to build and deploy ML models without having to manage any infrastructure. BigQuery ML uses the same machine learning algorithms as Vertex AI, but it does not offer the same level of flexibility or control.