Wondering how to integrate AI into a database? Do the following:
1. Form an experienced team to plan the project to integrate artificial intelligence into a database
You need to plan effectively to integrate AI into a database, and the project requires plenty of effort. Onboard an experienced team for project planning.
A PM (project manager) should lead this team and create a project plan. You need a business analyst (BA) and a software architect on the team.
2. Review examples of databases and data warehouse services that have integrated AI and machine learning
The following are a few examples of database management systems that have integrated AI and ML. Review these and understand how they went about these integrations.
A. Amazon Redshift
This is a managed data warehouse service designed to process large volumes of data. You can use your existing business intelligence (BI) tools along with Amazon Redshift.
Users in organizations can use SQL commands to create, test, train, and deploy ML models on Amazon Redshift. They can use the SQL command named “CREATE MODEL” to define the data to use and the target column.
Amazon Redshift uses SageMaker Autopilot for training the ML model. It uses an encrypted Amazon S3 bucket residing in the same geographic zone to store all the information.
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The AutoML training follows. Amazon Redshift picks the best ML model. It registers this model as a prediction SQL function. You can use an SQL “SELECT” query to invoke the model.
B. Google Cloud BigQuery
Google Cloud BigQuery is a managed data warehouse service that can process massive volumes of data. You can create, test, train, and train ML models in BigQuery. You need to use BigQuery ML for this.
BigQuery ML can do a lot, e.g.:
- One can use it to implement logistic regression for classifying data.
- You can implement K-means clustering for segmenting bad data at large-scale without human intervention.
- BigQuery ML uses neural networks based on TensorFlow.
- Organizations can use BigQuery ML for time-series forecasts.
The cloud computing capabilities of Google play a key role in making BigQuery ML powerful.
3. Analyze how integrating artificial intelligence and machine learning into your database can help
Data management is a key factor in an artificial intelligence, machine learning, or data science project. An effective data management strategy should try to reduce latency.
AI/ML/data science projects process vast data sets. If you need transmission of such massive data sets, then you will face latency.
You want data stored as close as possible to the computing infrastructure where you build AI/ML models. Can your database system support AI and machine learning? Then you can build models without large-scale data transmission. This is why you need databases supporting AI and ML services.
4. Finalize business and non-functional requirements for integrating AI into databases
The BA should hold detailed discussions with the business stakeholders in your organization. That will help with requirements analysis.
The BA should understand the objectives of your organization for integrating AI into databases. E.g., you might want to assist data scientists with better solutions including data visualization tools. Alternatively, you might want to save time while analyzing vast amounts of data.
The BA should document the requirements clearly. Similarly, the architect should document the NFRs. The PM should implement a sound requirements management process.
5. Select the right approach for creating AI integrations for databases
You can use one of the two following approaches for integrating AI and ML into your databases
Option 1. Use MindsDB
MindsDB is a well-known solution that supports several popular databases. It also supports several leading business intelligence (BI) tools. You can use machine learning capabilities in your database with the help of MindsDB. Its cloud capabilities help you with managing data.
MindsDB offers such data integration for leading database management systems like MariaDB, MySQL, PostgreSQL, ClickHouse, Microsoft SQL Server, and Snowflake. It supports BI tools like SAS, Qlik Sense, Microsoft Power BI, Looker, and Domo.
MindsDB supports AutoML. You can use the MindsDB Studio to use AutoML from an SQL INSERT statement. You can use a Python API call too.
After implementing an ML model, you can save it as a database table. Subsequently, you can use the MindsDB Studio to call it from an SQL SELECT statement.
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Apart from the above-mentioned convenience of using SQL queries, MindsDB offers extensive documentation. You can also view its GitHub repository to learn more.
Do you need to code your own custom AI/ML modules in addition to using MindsDB? You can use Python for that.
Option 2. Develop custom integrations from scratch
The other alternative is to develop AI integrations into databases from scratch. Obviously, this option doesn’t impose any limits on customization.
However, this is a highly complex approach involving database systems programming as well as AI/ML development. This approach involves the management of the entire gamut of integrating AI/ML into databases, e.g.:
- Data acquisition;
- Data engineering;
- Means to store data;
- Data processing pipelines;
- Preparing and transforming raw data;
- Data security;
- Processing vast data sets efficiently;
- Developing modules for extracting insights from data;
- Creating services for generating predictions from data;
- Managing parallel processing involving multiple machines.
6. Decide on a technology stack if you plan to create custom integrations of AI into databases
This section of the article is relevant only if you plan to code the entire AI integration from scratch. Enterprises can sometimes have customized requirements for integrating AI/ML into their databases. They should then plan the technology stack too.
We already recommended that you use Python to code AI and ML modules. Custom development of AI integrations into DB requires database system-related coding too.
Focus on the programming language that’s used for building the database system used in your enterprise. For e.g., many of the popular databases like Oracle, MySQL, PostgreSQL, and MongoDB are written in C++. To take another example, Microsoft SQL Server is built using C and C++.
7. Plan the project
The PM should focus on the following aspects while planning the project:
- Software development methodologies like agile;
- Project’s technical environment;
- Human resource management;
- Risk management;
- Issues management;
- Project tasks and their inter-dependencies;
- Project schedule, milestones, and iterations;
- Communications management;
- Quality management;
- Cost management.
8. Hire developers
Hire AI developers with Python skills, testers, and DevOps engineers if you use the MindsDB approach. For the custom development approach, you need people for the following additional roles:
- UI (user interface) designers;
- Database systems programmers with relevant programming language skills.
Do the following:
A. Choose a hiring platform
Note that a project to integrate AI/ML into databases can be highly complex. You should hire competent people from DevTeam.Space for such projects.
One might think of hiring freelancers, however, that’s risky for a complex project. Freelancers work part-time, and you often don’t get enough effort from them. You might find it hard to manage the work of a freelancer.
Freelance platforms don’t offer project management support. Freelancers might leave your project mid-way, and you will need to find replacement developers.
We at DevTeam.Space offers full-time developers. Our developers are skilled, experienced, and motivated. They are trained in our AI-powered agile processes. A reputed software development company, DevTeam.Space also offers project management support.
B. Conduct interviews
You chose a hiring platform and posted your job requirements. Interview the candidates. You can use our interview questions, e.g.:
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Ask questions that help you assess the hands-on skills of developers. Check how they solved problems in their past projects. Explain your project requirements and ask candidates how they would approach such a project.
C. Onboard developers
Onboard the new team members effectively. Do the following:
- Explain the project requirements and technical solutions.
- Share the important project documents with the new developers.
- Grant access.
- Introduce the new developers to your existing team.
- Explain the project schedule, milestones, and work approval processes.
- Set up a communication process.
- Establish accountability with the new team members.
The PM should take the lead here. The architect should support the PM adequately.
9. Execute the project
Irrespective of the approach you use, you have a few common tasks concerning data management. These are as follows:
- Acquiring raw data;
- Managing data storage;
- Preparing data for use with ML algorithms;
- Keeping separate data sets for training and testing.
Do the following if you use MindsDB:
- For the MindsDB documentation to implement AI/ML integrations into your databases;
- Run the relevant algorithms, e.g., linear regression, random sampling algorithms, deep learning algorithms, etc.;
- Build ML models and test them;
- Train ML models and deploy them.
Custom development will involve the following broad activities;
- Designing the user interface (UI);
- Developing and testing AI/ML modules including the implementation of ML algorithms;
- Integrating these AI/ML modules into databases;
- Building ML models and testing them;
- Training ML Models;
- Deploying the enhanced version of the database with AI/ML integrations.
The architect should lead the team, and the PM should manage the project.
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An AI database integration project can be very complex. You need highly expert developers to integrate artificial intelligence and machine learning into a database. Extensive planning and preparation are important for the integration of AI into database software systems. You need developers that know databases as well as AI like the back of their hands!
DevTeam.Space is one of the few companies that can offer developers with the relevant expertise that you need. Our developers are skilled, experienced, and motivated. We train them in our AI-powered agile process.
Wondering how DevTeam.Space can help you with your AI databases integration project? Fill out the DevTeam.Space product specification form. One of our dedicated account managers will soon contact you.
DevTeam.Space developers have deep knowledge of AI, tables in databases, public cloud, private cloud, and machine learning models. Our developers are experienced and motivated. They are trained in our world-class development processes. We can help your organization adequately.
DevTeam.Space programmers have extensive exposure to AI, ML, NLP, data science, data lakes, data warehouses, analytics, and other relevant areas. They are also highly focused on quality, therefore, you always get supportable and maintainable code.
DevTeam.Space provides far more than just expert developers. We provide comprehensive project management support. You get complementary support from a dedicated tech account manager when you hire developers from us.