Machine learning is an integral part of our lives used every day through image or speech recognition. This technology has made many processes simpler such as checking deposits on our smartphones and suggesting followers on social media platforms.
Though, in the business landscape, it has completely revolutionized operations by getting the most out of large data sets. Many new functions like predictive analysis and streamlining information in database development is all possible through machine learning.
Machine learning in databases is very useful because it saves time, resources and makes workflow efficient. With enhanced automation, many sectors like banking and healthcare can enjoy better stability, security and efficiency.
The role of machine learning in databases
Databases are extremely useful in storing and retrieving a large amount of information easily. When we visit a medical institute or even check our account details at a retail store, the technology being used to access our information is the database.
With technological changes and a boom in large data, businesses rely on artificial intelligence for decision making and recommendations. Machine learning in databases adapt to new data without any manual effort and can systematically analyze and calculate large volumes of data. It also eliminates the possibility of human error and boosts overall accuracy.
Machine learning has equipped businesses to make robust models that can analyze complex data and offer accurate results in real-time. Database development has been revolutionized by this Artificial Intelligence (AI) technology that offers affordable data storage and more powerful processing. It also helps build better models and strategies that can have a positive long-term impact on a company. If a business wants to do well in the rapidly transforming market, filled with new techniques and technologies, then it must implement machine learning in databases.
What can machine learning do?
Machine learning is a powerful tool in itself that has been instrumental in various fields. IBM has used this technology for the treatment of lung cancer patients at New York’s Memorial Sloan-Kettering Cancer Center and has collaborated with education company, Pearson, to help college students.
Even the Government makes use of machine learning for logistic management, to improve Cybersecurity and to predict possible future scenarios. Healthcare has a wide use of machine learning in databases as well, since it can be very useful in predicting illness and even larger health risks to society by identifying patterns.
Machine learning in data engineering is also beneficial as it allows database professionals to invest their time in better planning and strategic tasks. All the tedious and autonomous tasks will be handled by machine learning technology. When less challenging and time-consuming tasks are completed easily, it allows businesses to focus on more important things.
Benefits of machine learning in the database
There are numerous benefits that come with machine learning in database. Here are some below:
Makes the process simpler
When time-consuming processes are automated, work becomes simpler, but that’s not all that database machine learning offers. A data scientist hired by an organization will apply analytical techniques to data that is clean and well managed.
It also saves company resources as you need fewer people on board to work the existing ecosystem. Also, machine learning in database ensures that everyone working on the data is on the same page.
When we talk of efficient streamlining of work, then moving voluminous data to systems where you run your algorithm cannot be overlooked. This important function is highly time-consuming and complex. As well as this, there is always the possibility of potential data loss.
Therefore, companies opt for moving the algorithms to the database where it can access further data. Also, machine learning improves business activities, giving real-time results that can be shared across all verticals.
Keen to explore the vast sector of data engineering? Check out the Data Engineering and Analytics with Co-op program offered by The Canadian College of Technology and Business (CCTB).
This post-secondary diploma has a curriculum, designed by experienced professionals, that puts emphasis on real-life industry needs. It will help students gain relevant expertise to build a strong career in the field of data analysis. While the programme has a strong focus on theory, it also helps you gain practical experience as 50% of the overall program involves work experience.
Find out more here!
What kind of job roles can I expect after studying the Data Engineering and Analytics with Co-op program at CCTB?
This program prepares you for a dynamic career where you can opt for a job of your choice among a multitude of roles including database administrator, data analyst, data architect, database developer, technical architect and more.
Are CCTB programs globally recognized?
The programs offered by CCTB are globally recognized and accredited by major educational institutes such as The Education Quality Assurance (EQA). The Data Engineering and Analytics with Co-op program offered by CCTB, is approved by the registrar of the Private Training Institutions Branch (PTIB) of the Ministry of Advanced Education, Skills & Training. The college is part of Global University Systems’ network of educational institutions.