The terms machine learning and big data are buzzwords in modern computing. Yet, even though they are closely related, they cannot be used interchangeably. Machine learning is a branch of artificial intelligence and computer science that uses data and algorithms to mimic how humans learn, to improve its accuracy. The algorithms are trained using statistical methods to uncover key insights to drive decision-making in businesses and applications.
On the other hand, big data is a paradigm of computing where the data is huge in volume and grows exponentially with time. The complexity of this data makes it impossible to be stored or processed by traditional data management tools.
So, the question is, at what point do the two concepts – machine learning and big data – overlap? This article goes into detail about how machine learning is used in big data.
Applications of machine learning in big data
It is no secret that data is the new oil, and that there are significant financial rewards for those who can process it most efficiently. That’s where machine learning algorithms come in. Below are some of the applications of machine learning in big data.
Many organizations have access to lots of data, an untapped intelligence resource that can be used to streamline operations. Predictive analytics is an excellent approach to tapping into this resource. Predictive analytics encompasses statistical techniques, like machine learning, and uses statistics to predict future outcomes. In practice, a business can use predictive analytics to determine how a customer is likely to behave or how the market is expected to change in the near future.
Predictive analytics is driven by predictive modeling. In this way, predictive analytics and machine learning are intertwined as predictive models include machine learning algorithms. The predictive models can be trained over time to respond to new data or values, conforming to the needs of a particular business.
There are two types of predictive models: Classification and Regression models. The former predicts class membership, while the latter predicts a number. Each of these models is made up of algorithms that perform analytics and prediction. Some of the commonly used predictive models are:
- Regressions models
These models estimate the relationship among variables. They then identify patterns in diverse data sets.
- Neural networks
Neural networks are technologies used to solve complex pattern recognition problems. They are useful when handling nonlinear relationships in data.
- Ensemble models
Ensemble models use multiple algorithms to obtain better predictive performance than could be obtained from a single algorithm.
Other models include decision trees, time series algorithms, outlier detection algorithms, and support vector machines. While predictive analytics can be the holy grail for any organization, it only works if implemented in the right environment. Organizations must also feed high-quality data into these models to help them learn.
Examples of predictive analytics in use
Predictive analytics is applicable in many different business contexts. They are used in manufacturing, software, healthcare, and other sectors. Therefore, the demand for professionals with a Master of Science in Applied Data Science and Data Analytics Online, such as through the program offered by Kettering University Online, is growing. Below are some common examples of real-life uses for predictive analytics:
- Predicting buying behavior
Predictive analytics is widely used in the retail industry to predict buying behavior. When companies get insights about their customers, they can put strategies in place based on that information. For instance, businesses can determine the age distribution of their customers to determine the most effective marketing strategy.
- Fraud detection
Cyber security is a major contemporary concern. Machine learning systems can detect anomalies, which can help to identify threats.
- Content recommendation
Various entertainment platforms are fighting for users’ attention. The platform with the most accurate prediction carries the day. Therefore, platforms like Netflix use predictive analytics to anticipate the movies and shows users may like depending on their past behavior.
- Virtual assistants
Virtual assistants such as Alexa and Siri use predictive analytics and deep learning. These technologies learn a user’s behavior to deliver accurate results. Companies can also use virtual assistants as chatbots to enhance customer experiences, which could lead to higher customer retention.
- Equipment maintenance
Predictive analytics models come in handy in manufacturing, and in situations that require scheduled equipment maintenance. The machinery can alert personnel that it’s time for maintenance to avoid accidental breakdowns.
Natural Language Processing
Natural language processing (NLP) is a branch of artificial intelligence that studies the interaction between computers and languages. NLP aims to grasp human speech as uttered and find new methods of human-computer communication.
NLP relies on machine learning, statistics, computational linguistics, and deep learning models to enable computers to process human language from voice or text data. NLP helps computers to understand context, as opposed to understanding single words or phrases. Some methodologies applied to ensure comprehensive data extraction include part-of-speech tagging, disambiguation, entity extraction, and relations extraction. This technology is used to develop word processor applications and translation software. Other applications include chatbots, search engines, and banking apps.
A significant benefit of natural language processing is that it allows more people to interact with data – even those without technical know-how. Using NLP, people without in-depth technical knowledge can still obtain important data insights. NLP technologies can also save organizations time as they can analyze language-based data faster than humans. Not to mention they eliminate instances of bias, inconsistency, and fatigue. NLP can also be applied to social media, enabling businesses to monitor responses about a particular topic and pinpoint key influencers. It also allows users to use their own words to search for content; they don’t have to worry about knowing the right keywords to get the right information.
Image and Video Analysis
Computer vision is a current point of discussion in the tech industry. Applications like facial recognition and biometrics rely on computer vision, which rides on the back of image processing. Image processing can be defined as the process of transforming an image into digital form and getting useful information from it. There are different types of image processing, including visualization, pattern recognition, and retrieval. Image processing has various applications, such as traffic sensing technologies, medical image retrieval, image reconstruction, and face detection.
Another branch of computer vision is video analytics. The main goal of video analytics is to automatically recognize temporal and spatial events in videos. For instance, a video analytics model can detect someone who moves suspiciously in CCTV.
Usually, video analytics systems monitor the environment in real-time. However, they can be used to provide insight into historical data. For instance, organizations can use video analytics to determine when customer presence is at its peak.
Although video analytics has been around for several years, it has been revolutionized by machine learning. For instance, deep neural networks can be applied to train video analysis systems to mimic human behavior. A good example is license plate recognition. Models based on deep learning can be used to track and identify license plates, in the case of traffic or parking violations.
Video analytics with machine learning can also transform mental healthcare. Systems can be trained to analyze facial expressions and body posture to help evaluate various mental health conditions.
Challenges of machine learning in big data
Machine learning is a crucial part of big data, still, it has its fair share of challenges. Here are some of them:
- Data quality considerations
Data quality tremendously affects machine learning workflow. When the data is of poor quality, the results might be inaccurate, translating to wrong decisions based on those results. Usually, the datasets used to train machine learning algorithms are cleaned to provide accurate results, however, this is not always the case. The data might be erroneous, and if it is not taken care of before feeding it to the machine learning model, it can have dire consequences. Erroneous data in an algorithm can cost organizations millions of dollars and put people’s health at risk.
So, what is data quality? There are several definitions of data quality, but the simplest of them all is “fitness for use for a specific purpose.” Therefore, data quality is relative and objective. Typically, there are six dimensions of data quality: accuracy, timeliness, consistency, validity, uniqueness, and completeness.
Data analysts can use libraries available within Python to identify issues with datasets. Common libraries used for exploratory data analysis include Pandas Profiling and Missingno.
Scalability in machine learning refers to building machine learning applications that can handle any amount of data and cost-effectively perform several computations. However, scalability remains a sore issue for machine learning developers.
One of the major concerns of scalability in machine learning is the spread of the internet. Network speeds are higher than ever, and the number of people accessing the internet is ever-increasing. Therefore, the data footprint of an ordinary citizen has gone up exponentially. Developers can no longer ignore this fact.
There is also the use of data storage costs. Organizations must consider storage costs when machine learning models grow and use more data. Storage is getting cheaper over time; however, developers must ensure that the data is not so big that it doesn’t fit in the working memory of the training device.
- Scaling solutions
There are two approaches to scaling in machine learning for big data. The first option is vertical scaling. This approach involves getting a faster server with more powerful processors and memory. Vertical scaling is common in the cloud, as it’s not easy to scale a dedicated server without downtime.
The other option is scaling horizontally. This means using more servers for parallel computing and is ideal in real-time analytics scenarios. A load balancer would come in handy to manage the load across several servers. Horizontal scaling is a more cost-effective approach compared to vertical scaling.
- Model fairness and interpretability
Model interpretability is the ability to understand how a machine learning model makes predictions or decisions based on the data fed into it. It is a crucial aspect of big data because it allows organizations to gain insights into how their machine learning model operates. Think of a scenario where a business uses predictive modeling to gauge the probability that a certain drug will be effective for a patient. The company can decide that it’s enough to know whether or not the drug works. However, they might choose to go the extra step to understand why the drug worked. The why – in this case – is model interpretability. It’s a good way to detect potential biases and errors, ultimately leading to better model performance.
Achieving model interpretability, however, is not straightforward. For example, isolating key factors that a machine learning model uses to make predictions or data-based decisions might be challenging when dealing with large datasets – some machine learning algorithms have millions of parameters and layers that are difficult to sort. Additionally, machine learning models are built using black-box techniques, which means decision-making processes can be rather ambiguous.
Certain strategies can help data scientists move closer to model interpretability, however, such as building models with fewer parameters and layers. This would also help to create visualizations to understand the decision-making process.
Tools and technologies for machine learning and big data
When it comes to big data, machine learning has massive future potential. As well as its significant future potential, it is also being used productively today:
- Hadoop and Spark
Both Hadoop and Spark were developed by Apache Software Foundation. Hadoop is an open-source software used to manage big data sets – to the tune of gigabytes and petabytes. The software works by enabling nodes to solve intricate data problems. One of the best things about Apache Hadoop is its scalability. It is also cost-effective, guarantees data protection amid a hardware failure, and provides real-time analytics.
Apache Spark is also open-source and works well with big data sets. The only difference between Hadoop and Spark is that the latter is faster. Unlike Hadoop, which uses the file system, Spark uses its RAM to cache and process data. Spark can therefore handle use cases that Hadoop cannot.
- TensorFlow and PyTorch
TensorFlow is an end-to-end open-source deep learning framework developed in 2015. Most people know TensorFlow for its uses in documentation and training support.
There is also PyTorch, developed by Facebook’s AI research group. PyTorch is used for natural language processing applications. One of the major benefits of PyTorch is that it is Python-friendly. It is also optimized for GPUs supported by AWS and Azure for accelerated training times. It also boasts efficient memory use and flexibility.
- Cloud services
Cloud services, like Amazon Web Services (AWS), are becoming increasingly popular in big data environments for machine learning. Cloud services are particularly advantageous due to scalability; organizations can add or remove resources in cloud services depending on their big data needs. They also have redundant systems, making them incredibly reliable. These functions allow organizations to access their data anytime, anywhere.
Furthermore, cloud services are cost-effective. With a cloud service, an organization only pays for the resources they need instead of investing in expensive hardware and software upfront. The pay-as-you-go pricing model is favorable for organizations that run intermittent big data applications.
Cloud services offer several tools and platforms to develop and deploy machine learning models. For instance, Azure has Azure Machine Learning, while AWS has Amazon SageMaker. Cloud services also allow developers and data scientists to access their data easily. Other advantages include encryption, versioning, and data integrity.
What the future holds
The interplay between machine learning and big data is here to stay. Businesses generate around 2.5 quintillion bytes of data every day. That data is impossible to analyze manually, making the power of machine learning systems invaluable. As technologies advance, the use of machine learning in big data will continue to grow and evolve.
Machine learning and big data will also eventually be adopted in more settings, such as healthcare and fintech. These technologies can improve business operations such as supply chain management, fraud detection, and customer experience, making them useful in a variety of sectors.
Finally, as machine learning and big data tools become more accessible, they will be useful to those with little technical knowledge, meaning more businesses will be able to make use of their tools.