The application of AI in healthcare allows for the identification of complex patterns in patient data, improving diagnostic accuracy, treatment personalization, and operational efficiency [1]. Healthcare providers are increasingly leveraging predictive analytics to foresee health outcomes, enabling earlier interventions and more targeted care [2][26]. For instance, AI models have proven effective in identifying high-risk patients and optimizing preventive care strategies [3]. Diabetes, a major global health challenge, requires early detection and preventive care. Predictive models built using accessible tools like BigQuery ML can help healthcare professionals identify at-risk individuals efficiently. Cloud computing serves as a critical tool for AI and ML in healthcare, addressing many of the technical and infrastructural challenges associated with large-scale data analysis. With scalable infrastructure, cloud platforms allow healthcare providers to process and store vast amounts of data, facilitating AI-driven insights without the need of extensive on-site resources [4].

Summary: NHANES, the National Health and Nutrition Examination Survey, is a program of studies led by the Centers for Disease Control and Prevention (CDC) designed to assess the health and nutritional status of adults and children in the United States (U.S.). NHANES data is frequently used by biostatisticians and clinical scientists to study health trends across the U.S., but every analysis requires extensive data management and cleaning before use and this repetitive data engineering collectively costs valuable research time and decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that automates the data engineering and management aspects of working with NHANES data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less than $15/yr of ongoing costs for hosting the NHANES data, all while providing researchers with clean data tables that can readily be integrated for large-scale analyses. We provide examples of leveraging BigQuery ML to carry out the process of selecting data, integrating data, training machine learning and statistical models, and generating results all from a single SQL-like query. NHANES-GCP is designed to enhance the reproducibility of analyses and create a well-engineered NHANES data resource for statistics, machine learning, and fine-tuning Large Language Models (LLMs). Availability and implementation" NHANES-GCP is available at https://github.com/In-Vivo-Group/NHANES-GCP

We recently announced BigQuery support for sparse features which help users to store and process the sparse features efficiently while working with them. That functionality enables users to represent sparse tensors and train machine learning models directly in the BigQuery environment. Being able to represent sparse tensors is a useful feature because sparse tensors are used extensively in encoding schemes like TF-IDF as part of data pre-processing in NLP applications and for pre-processing images with a lot of dark pixels in computer vision applications. There are numerous applications of sparse features such as text generation and sentiment analysis. In this blog, we'll demonstrate how to perform sentiment analysis with the space features in BigQuery ML by training and inferencing machine learning models using a public dataset.

Now, depending on your use case, You can identify the type of machine learning model you need (We will not be talking about the core ML concepts and how to choose the ML model for the use case in this article.). Once we have the type of ML model that we need, we can use the GoogleSQL queries to create the model. The syntax for creating the model can be found here. For example, to create a logistic regression model using the Google Analytics sample dataset for BigQuery. The following GoogleSQL query is used to create the model you use to predict whether a website visitor will make a transaction.

We describe the new BigQuery and BigQuery ML (BQML) components now available for Vertex AI Pipelines, enabling data scientists and ML engineers to orchestrate and automate any BigQuery and BigQuery ML functions. We also showed an end-to-end example of using the components for demand forecasting involving BigQuery ML and Vertex AI Pipelines.

The services that will work best for you will depend on your specific use case and your team's level of expertise. Because it takes a lot of effort and ML expertise to build and maintain high quality ML models, a general rule of thumb is to use pretrained models or AI solutions whenever possible -- that is, when they fit your use case. If your data is structured, and it's in BigQuery, and your users are already comfortable with SQL, then choose BigQuery ML. If you realize that your use case requires writing your own model code, then use custom training options in Vertex AI. Let's look at your options in some more detail.

We'll leave the actual model creation and optimization processes to the experts: BigQuery ML and AutoML Tables. Even better, we'll train two different models and select the one that performs better with our dataset. Before we dive into the pipeline, let's take a quick look at the tools we'll rely on for model development: BigQuery ML (BQML) lets you create and execute machine learning models in BigQuery using standard SQL queries while leveraging BigQuery's petabyte scale. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills. AutoML Tables is even more hands-off.

Business Intelligence (BI) descriptive approach has shifted toward a more predictive and prescriptive analysis. Based on these changes, the analytic framework has been revised to include a data science layer. The combination of traditional business intelligence and data science is seen as the future of the field. Consequently, emerging cloud-based services are now presented as one integrated service. They merge different technologies, including data warehouse, machine learning framework, and visualization tool in order to facilitate access to both data analysts and data scientists.[3]

Machine learning continues to become more available daily, and one exciting development is the straightforward availability of machine learning models since data is at the essence of any machine learning problem. Such data is used for the training, validation, and testing of models, and the performance reports of a machine learning model need to be calculated on the independent test data rather than the training or validation tests. Lastly, the data needs to be split so that all three datasets, like training, test, and validation, can have related statistical characteristics. The first crucial step in a standard machine learning workflow after data cleansing is training -- the method of passing training data to a model to learn to identify patterns. After training, the subsequent step is testing, where we examine how the model performs on data outside of the training set. This workflow is known as model evaluation.

In my August 2020 article, "How to choose a cloud Machine Learning platform," my first guideline for choosing a platform was, "Be close to your data." Keeping the code near the data is necessary to keep the latency low, since the speed of light limits transmission speeds. After all, machine learning -- especially deep learning -- tends to go through all your data multiple times (each time through is called an epoch). I said at the time that the ideal case for very large data sets is to build the model where the data already resides, so that no mass data transmission is needed. Several databases support that to a limited extent.