You may need to accomplish a lot of things to ensure that you are joining the bandwagon of the fast-growing businesses that leverage the benefits of machine learning. One significant action among them, of course, is setting up your enterprise database to fit machine learning needs.
Why machine learning?
You may have already heard about the myriad of applications with machine learning in the new like the computers creating music or art all by themselves using it. However, what excites the business world about machine learning is its ability to use historical data to identify trends and patterns and provide insightful suggestions on the best future action course.
Machine learning is ideally suited for this purpose as it can process massive volumes of data and handle big computing tasks with algorithms. The computers with machine learning capabilities can learn and explore the data to the ore to unravel the hidden information. Ideally, machine learning can enable businesses to accomplish things like:
- Improving retail sales with recommendation algorithms
- Anticipate the possibility of any equipment/machine breakdown by using a prediction algorithm
- Detection of possible fraudulence through the anomaly detection algorithm
These are just some basic functionality, and machine learning can do more advanced data-related tasks that traditional analytics may not be able to.
Machine Learning based on your databases
Incorporating machine learning into the business database means faster results and better predications. As of late, some databases already come with machine learning capabilities. This means that you need not have to go out and gather an add-on data science platform or have to learn using Hadoop, but you can still reap the benefit of machine learning. You also need not have to learn how to use the data lakes to use a database that comes with machine learning in it.
But hard-core machine learning is not the way how all such DBMS providers do things; however, still many of those make things easier for you as below:
- Do many things simultaneously with the existing data while upholding control over data by using the database as a single source of truth.
- Experiment with the machine learning products which come packed with the DBs and are optimized. With the use of apt products, you may even perform some advanced experimentation without acquiring a data scientist’s skills.
- Use machine learning in the DB to build instant solutions for tasks like fraud detection and customer behavior predictions to identify more selling opportunities.
- Make it easier for various teams to use the machine learning models to operationalize the machine learning approach much easier than ever.
Before discussing the advantages of using the existing data for machine learning, let us first explore the data science process, which will reap the best benefits of machine learning. For any support you need in remote database administration and data management, feel free to touch base with the RemoteDBA.com experts.
Data science for machine learning
When you work on your database, the actual assumption is that data is already out there so that you don’t have to extract it from other sources, which means you are cutting off the most time-consuming step. Further to it, the middle stages of data management for machine learning involves
- Understanding the data and preparing it for our data science project
- Building the data model
- Test and evaluate the model for efficacy.
In a real-time project, the above steps may have to repeat many times for perfection. Once achieving a satisfactory test result, this model needed to be deployed further to use the available data. This may be the most challenging step involved in the entire project. You need to decide what type of hardware it runs on, how it may access the required data, there is a need to convert the code to another language, and whether it has to be accessed from another API, etc.
While you build machine learning capabilities in the given database, you run it in the same database itself. There is no need for any code conversion but can call the model from a simpler SQL statement. Additionally, you may also find a way to expose it as a REST API, too, if needed. By taking up this approach, you may significantly simplify the activities up to 40% to build and deploy the model. This is where you can maximize the value of your available data.
Database machine learning benefits
Now, let us explore some benefits of machine learning in the database.
You know the data in hand well. For anyone starting up with the data in the database versus the data in the data, the lake can be like a kid in the candy shop. In a given database, the data is mostly clean, well-managed, and you can also apply the analytical techniques directly on to it.
If you are using a database with machine learning, it means that you may already have people who know about it and work on it. If you are planning for it now, then instead of hiring many professionals, each of whom is experts in one of the five software platforms you plan to adopt for the machine learning workflow, hire one or two who are well-versed with your business ecosystem.
You save a lot of time
It may often take many hours to many days to move your data to a new platform where all the machine learning algorithms reside. So, doing this introduction with all complexity involved, there is a chance of possible data loss. So, even by taking more time for it, it makes more sense to move your algorithms over to the database where they can leverage your database’s power to run quicker and efficiently use the DB to get access to the data on other databases. Ideally, pairing the machine learning with your existing DB makes more sense, which is must faster and saves a lot of time and effort for you.
You also get assured results on planning your database with machine learning capabilities. Incorporating machine learning with your existing database means that you can minimize the number of steps you need to make it efficient and leverage the benefits with an easier-to-operationalize model of machine learning.