Data is becoming increasingly ubiquitous, and machine learning can help us make sense of the vast amounts of data out there.
In this article, we’ll explore how machine learning can be used to unlock insights from big data and reveal patterns that may otherwise remain hidden.
We’ll look at why machine learning is so important for big data analysis and discuss some practical applications of machine learning in the field.
Finally, we’ll examine what challenges exist when it comes to using machine learning for big data processing. So let’s get started on our journey into understanding how machine learning can help us make sense of all this information!
What is Machine Learning?
Machine learning is a type of artificial intelligence (AI) that enables computers to learn from data and experience. Through its self-learning algorithms, machines can gain knowledge about different topics and take actions accordingly.
This core method works well in pattern recognition and predictive modeling applications. To improve accuracy, machine learning algorithms must be fed enough quality data so they can exercise their skills just like a human athlete continually trains for their chosen sport.
To facilitate the process, developers usually rely on one of several programming languages such as Python, R, Java, JavaScript or Scala. Of the many available tools for machine learning development, Python’s TensorFlow library is the most popular given its robust set of features.
Whether you’re just starting to explore machine learning or ready to make an impact with your AI ambitions, there are plenty of opportunities with this technology.
What is Big Data?
Big data consists of an arduous volume, velocity, variety and veracity of data that exceeds traditional data-processing software.
Huge amounts of structured and unstructured information pour in from multiple sources every second. As such, big data is complex and needs to be handled carefully.
Data scientists determine the value of the gathered information, break it down into simpler components so that it’s easier to parse and analyze. Once broken down, those components can be used to draw fresh insights on consumers and market trends.
By leveraging new technology-driven approaches to explore these vast amounts of information, companies are able to increase their revenues, reduce costs and formulate strategies backed by meaningful numbers.
Big Data Meets Machine Learning
By pairing big data with machine learning algorithms, businesses are able to advance their predictive models and uncover hidden patterns.
Companies may automate certain processes if the algorithm provides reliable findings, but more often than not, professionals will still review the results in search of valuable insights.
This combination harnesses both sets of advantages: machine-learning’s effectiveness as databases grow and big data providing new fuel for a machine-learning algorithm’s predictive prowess.
Big data and machine learning together create a powerful tool for businesses to increase efficiency, get detailed analyses, and plan for the future.
Machine Learning Applications for Big Data
Big data and machine learning are two tools that are increasingly being used together to create powerful applications.
Machine learning allows for the extrapolation of insights from vast amounts of data, giving experts and decision makers valuable information that can be used to guide their strategy.
Companies such as Amazon use machine learning algorithms for predictive analysis and product recommendation, providing shoppers with especially tailored offerings. In health care, machine learning is used in diagnostics and image analysis, helping to quickly detect potential medical issues in people.
Government agencies use the power of big data and machine learning to better understand their constituents’ needs and ensure public safety by more accurately locating criminals.
With these examples already existing, we can only imagine what great advances will de revealed as big data continues to grow and evolve along with improved machine learning algorithms.
Cloud Networks
Cloud networks offer companies the perfect solution when it comes to analyzing large amounts of data.
Amazon EMR is one popular cloud-based service that provides a managed framework for data analysis and machine-learning models, including GPU-accelerated image recognition and text classification.
This allows content to be distributed more quickly and efficiently without compromising on security. Moving a big-data environment to the cloud also has many cost saving benefits, as explained in LiveRamp’s detailed outline. Ultimately, cloud networks provide an efficient and cost effective way for firms to collect and analyze their data.
Web Scraping
Web scraping can be a powerful tool for uncovering data that traditional market research methods such as surveys and industry reports often leave out.
For example, a manufacturer of kitchen appliances might opt to web scrape an enormous amount of existing customer feedback and product reviews in their effort to gain insight on how to improve their products.
By aggregating the gathered data and feeding it into a deep-learning model, the manufacturer can better understand the needs of its customers which can not only result in increased sales but also help them create more satisfactory products that meet customer demands in an ever-changing marketplace.
Remember that web-scraping is ultimately only as useful as the sources you choose. That’s why it’s important to review best data-mining practices before you get started.
Mixed-Initiative Systems
Mixed-initiative systems, or human-computer interactions, can be seen in everyday products like the recommendation system on Netflix.
It uses big data and machine learning algorithms to weigh opinions from your history and others’, providing customized recommendations for you. Smart car manufacturers are also implementing predictive-analytics systems that use data to make algorithm-based decisions.
The Tesla car is a prime example of this. By communicating with the driver and responding to external stimuli, these techniques extend our understanding of having a computer take the initiative.
Achieving accurate results
Getting into the world of machine learning can be extremely intimidating, but with the right preparation you can easily get accurate results and alleviate the fear of big data.
Of course, first and foremost you need an algorithm that is set up properly and trained on large-scale clean data sets — this will ensure that your outcomes are told by the facts instead of being polluted by bad data.
Additionally, in order to avoid clunky workflows and costly headaches as you scale, invest in tools that can help automate jobs such as feature engineering, model humanization, etc.
With all these approaches in mind, take a moment to really consider what it is that you want out of using machine learning — having a clear end goal will certainly prevent confusion and frustration during implementation.
Data Hygiene
Data hygiene is essential in any machine learning process. Failed or incomplete data sets may result in the wrong decision-making processes, ultimately leading to a greater expense for a company.
Companies must be able to answer questions about their data – where it comes from, how complete and accurate it is, and how well it can help with optimization or problem solving. Without proper data hygiene, machine learning algorithms are liable to produce faulty results that could cost a business much more than anticipated.
The consequences of poor data can thus be costly; staying on top of data quality by regularly checking its accuracy and completeness is key to forging successful results from machine learning models.
Practicing with Real Data
Using real data to train algorithms is the best choice for ensuring your machine-learning algorithm’s potential is fully realized. Derived computed data usually does not replicate the type of input that perfectly suits your algorithm’s specific problem.
As a result, while it can seem tempting to use this substitute, doing so may leave you with an algorithm that fails to meet your expectations. Rather than risking a costly mistake, it’s best to opt for experimenting re-created data–or better yet–gathering real data to ensure your algorithm’s long-term success.
Don’t let the hype around integrating machine learning
The immediacy of technology and the rise of machine-learning have shifted our approach to problem-solving. It can be tempting to jump headfirst into a project without taking the time to understand the data associated with it.
However, this rushed approach often leads to costly misapplications of analytics as we take what we think works instead of having an intimate understanding of our own data.
Knowing what you want to achieve is crucial for any big data project—and that means taking the time to examine your data and really understanding it before searching for algorithms or solutions.
Having a good grasp on your data’s structure first allows you to find solutions best suited for your needs and gives you insight into how far you can push further boundaries through machine learning. Without this process, projects are doomed from the get-go, setting off an unending cycle of trial and error with no real solutions in sight.
Scaling Tools
With big data and machine learning, businesses can access and interpret information faster while efficiently solving problems on a larger scale than ever before. Scaling can help leverage these technologies to the fullest potential.
To build upon this potential, we must ensure that the other tools used in the business are fit for scaling as well.
By investing in finance, communication, and other effective tools which are built to handle large volumes of also data and transactions, businesses can put every component into action for even greater success.
Conclusion
In conclusion, machine learning is a powerful tool for unlocking the potential of big data. It can help businesses automate processes such as feature engineering and model humanization to gain insight from large datasets.
However, it’s important that companies take time to understand their own data before deploying machine learning algorithms and ensure they have the right tools in place to scale up if necessary.
By putting these principles into practice, companies will be able to get more out of machine learning with greater accuracy and efficiency than ever before.
