BigQuery ML vs Vertex AI

There is a widespread trend and belief in the technology industry that “The Future is Cloud”. It is estimated that all the physical computations will be carried out by cloud in the future through cloud computing. Cloud Platforms from Google, Amazon, and Microsoft have shown a solid and significant growth in cloud computing and infrastructure. This article delves into two of the biggest pillars of cloud-based services provided by Google Cloud i.e. Vertex AI and BigQuery. Both platforms have vast use cases and limitations. Before heading into the key differences between the two platforms, let us analyze each platform in brief.

Big Query Vs Vertex AI

What is Vertex AI?

A Vertex AI is a machine learning platform that provides tools for the deployment and management of machine learning models. To accelerate AI innovation, the vertex AI platform helps to provide a seamless and flexible environment. In simpler words, using vertex AI, the user gets a very simplified ML workflow aggregated in one central place.

Workflow of Vertex AI

• Ingestion – The main aim of this phase is data collection and data preparation for efficient use in machine learning models. Vertex AI Feature Store makes that data available for online serving scenarios.
• Analysis – This phase gathers analysis about the dataset such as what is the structure of the data, how much value can be derived from the dataset, and what extent of data in the dataset is noisy.
• Transformation – In this phase, the data is labeled and annotated through the vertex AI console. Transformation operations are performed through which noisy data is reduced to a larger extent.
• Training – In vertex AI, the training of the model can be either automated using AutoML where no code is supposed to be written, or custom mode where developers want to have more control over model architecture and code themselves.
• Modeling – This phase is more related to machine learning algorithms. The modeling process can be streamlined using vertex AI tools for advanced ML algorithms like regression, classification, clustering, etc.
• Evaluation – Upon the development of the model, the developers can assess the model’s functionalities and its optimization using vertex AI tools like Explainable AI, AuPRC, AuROC, etc.
• Deployment – The deployment of a model in vertex AI is done while keeping in mind that it provides the lowest possible latency and online predictions. The developers can use undeployed models for batch predictions.
• Prediction – Upon the successful deployment of the model, the developers can get the predictions using vertex AI command line interface (CLI) or API. It also has an in-built function ML.PREDICT to predict outcomes using the model.

The earlier 3 stages are all about data transformation and preparation. For the ease of developers, Vertex AI provides AutoML functionality, through which the developer does not need to write code for the model. All the coding based on the model requirements will be done by vertex AI. All the predictions and metrics results can be obtained through the command line interface or APIprovide in Vertex AI platform. However, the experienced developers may choose to create and code the model on their own. For them, Vertex AI also has custom features that provides them the environment to develop and deploy ML models. Alongside this, one of the biggest advantages of Vertex AI is scalability and less latency.

What is BigQuery?

BigQuery is a fully managed data warehouse and analytics platform. The biggest advantage of BigQuery over Xertex AI is its advanced querying capabilities and big data analysis over larger datasets. The 4 phases of model development through BigQuery are as follows:

Workflow of BigQuery

1. Ingestion – Similar to the vertex AI phase, Ingestion in BigQuery focuses on data collection and preparation. It can be done through loading a batch of data of individual records at a time.
2. Storage – Since BigQuery works on big data querying and analysis, the data is stored in structural tables. The storage is managed in highly available compute clusters with distributed memory shuffles.
3. Analysis – It supports various types of analysis like Business intelligence using BI Engine, Geospatial analysis using GoogleSQL, Machine Learning analysis using BigQuery ML, etc.
4. Visualization – BigQuery provides a free visualization tool called BI Engine Looker Studio for creating visualization. It includes big data visualization through charts, graphs, table schemas, statistical reports, etc.

BigQuery provides a fully managed data warehouse through which it takes care of the entire infrastructure. Thus, the user/developer only needs to focus on data analysis tasks. Through this feature, the developer can analyze the data up to Petabytes of scale. Some of the biggest use cases of the BigQuery platform comes in the field of Business intelligence (BI) and Data Mining. Alongside this, BigQuery is also used in performing market analysis, complex data processing, and machine learning tasks.

Difference between Vertex AI and BigQuery

Now let’s analyze the key differences between Vertex AI and BigQuery.

Parameters	Vertex AI	BigQuery
Definition	Vertex AI is a platform that provides tools and services for developing and deploying machine learning models.	BigQuery is a fully managed data warehouse and analytics platform for querying and analyzing large datasets.
Data Type	Both structured and unstructured data types can be processed in vertex AI such as text files, tables, and images.	Since BigQuery is majorly used in querying tasks, it operates on structured datasets like tables for performing SQL queries.
Skillset Required	Even if the developer is not skilled in ML coding, yet the model can be developed through AutoML functionality.	The usage of the BigQuery platform requires precise knowledge of SQL. Thus developers may not require advanced ML algorithms.
Core Functionality	A wide range of functionalities are provided from data preparation & transformation (AutoML) to estimation and analysis of the model after deployment.	BigQuery provides querying functionalities over larger datasets and advanced analytics operations including data visualization.
Languages	Vertex AI uses code models called codey APIs that support many languages like C, C++, Java, Python, Ruby, Swift, etc.	It uses a variant of SQL called BigQuery SQL for querying. It supports advanced analytics, data transformations, and data visualization.
Use Cases	Vertex AI has vast use cases in Machine Learning domains like Image Recognition, CNN, Natural Language Processing, etc.	BigQuery has vast uses in Business Intelligence, data mining, IoT analysis,marketing, and real time analysis.
Data Storage	Vertex AI has managed pipelines that help to automate and deploy ML workflow in a serverless manner and store artifacts using Vertex ML Metadata.	Data is stored in structural tables. The storage is managed in highly available compute clusters with distributed memory shuffles.
Cost	Model Code – $0.0005 per 1000 characters. Model training – $3.465 per node hour. Deployment – $2.002 per node hour. Forecasting – $0.1 per 1K data points	Physical storage – $0.04 per GiB per month. Querying (On demand)- $6.25 per TiB. Pay as you go – $0.04 / slot hour. Model Creation – $6.25 per TiB.

Conclusion

In summary, Vertex AI is primarily for machine learning tasks, while BigQuery is used for data analysis and warehousing tasks. Both of these platforms are the pillars of Google Cloud. They have vast use cases in multiple domains which include Business Intelligence, Real-time analysis, Machine Learning, CNN, Natural Language Processing, etc. However, vertex AI provides serverless functionalities which are not provided by BigQuery. The choice between these two platforms depends on the organizational preferences. It depends on the needs and requirements of the project whether ML algorithms or Analysis and querying is required. The precise use of these tools will ultimately boost the productivity and success of the organization.