The AI revolution is here with us, and it’s here to stay. Unlike its inception days, the technologies being built around it are now gaining context in many industries and fundamental aspects. Of particular interest are Big Data and Machine Learning.
Big Data is a conglomeration of data whose growth in terms of volume, variety, and complexity is quite rapid and exponential, whilst machine learning is a subfield of Artificial Intelligence that involves building and developing data-based systems capable of self-learning, and fine-tuning their performances, with progressively less need for explicit programming.
Despite being delivered as separate courses by learning institutions, both big data and machine learning are key AI enablers. In India for instance, over 40 institutes are offering AI and Machine Learning courses in Delhi, its capital, and a majority of them offer separate or elective courses on big data.
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So what makes these two technologies different?
Well in case you’re still struggling to distinguish them, below we distinguish between big data and machine learning. It is important to note that they complement each other and are often used in the same setting, one as a technique (machine learning) and the other as a resource (big data).
Big data refers to massive volumes of data varieties generated by organizations at a very high velocity that is processed, structured, and analyzed to extract valuable information that is important for decision-making.
Machine learning, on the other hand, is a subset of artificial intelligence. This is a data management and analytics technique that involves the use of algorithms that are programmed using training data to automatically learn to discover patterns and make accurate predictions on input data without human intervention.
Thus, as mentioned earlier, machine learning algorithms are techniques that can be used to make predictions from big data which in this case is the resource.
Data is the raw material used by all industries and sectors to gain insights that will enable informed decision-making. Big data use cuts across all industries and sectors. Decisions informed by big data analytics have been found to improve efficiency, streamline workflow by facilitating automation, understand customer expectations and behavior, conduct personalized targeted marketing campaigns, and many more. The end goal of adopting big data analytics for any business is to increase productivity, gain a competitive edge in the market, and discover opportunities.
Machine Learning on the other hand is a subset of AI and a technique used to analyze data for a specific purpose, to teach machines how to respond to input data to deliver the expected outcome. For this reason, while AI has grown to have a very wide scope, machine learning is limited in scope.
There are three main categories of big data based on structure. These are:
- Structured big data. Often generated by business systems, structured data is generated, processed, stored, and accessed in a standardized format. Structured data is easy to manipulate.
- Semi-structured big data. While semi-structured data is structured, it does not follow a schematic data structure in which variables are organized in rows and columns as used in relational databases.
- Unstructured big data. Unstructured data comes in many forms and hence does not conform to any particular internal structure. It can be in the form of audio, text, video, images, posts, or documents.
There are three types of machine learning
- Supervised machine learning. Supervised machine learning uses labeled datasets to train algorithms to classify data accurately to produce predicted outcomes.
- Unsupervised machine learning. Unsupervised machine learning uses unlabelled datasets to train algorithms to analyze and cluster data. By discovering patterns in clustered data without being programmed by humans.
- Reinforced machine learning. Reinforced machine learning trains algorithms through a rewarding system whereby the desired behavior is rewarded and the undesired behavior punished. Through encouraging and discouraging certain behaviors, the learning agent interacts with the environment to take the desired actions that will maximize its reward. Thus it learns, through trial and error.
Due to the diversity of big data, manipulation is complex and required large capacity systems capable of handling vast volumes, velocity, and variety of big data. Big data processing and analytics require such tools as Apache Hadoop, Apache Storm, Cassandra, Cloudera, and MongoDB.
Analysis using machine learning models is not as complex because the goal of using machine learning models is to automate the entire process for easier pattern identification. Together with the use of ML tools and libraries like Pandas, Numpy, and TensorFlow, analysis s done on already prepared datasets.
Objectives and Professional Skills
The main objective of any organization when investing in big data is usually to extract relevant information and insight to improve decision-making. Training institutions equally focus on producing skilled professionals fit to handle and manipulate vast volumes of data. Such skills as data mining, data structures, data analysis, data modeling, and data visualization skills alongside domain knowledge are key for big data professionals.
The goal of machine learning is to improve efficiency, accuracy, consistency, and productivity in the analysis of data and business processes, with minimal human intervention. This requires professionals with advanced mathematics, statistics, data modeling, and programming languages like Python, SQL, and Java as well as domain knowledge.
According to NASSCOM, India’s big data industry will form 32% of the global market and reach $16b by 2025 from the present $2b. This is an 8-fold increase which is quite rapid. Based on studies by Shiksha Internal Authors, there are about 90 big data colleges in New Delhi-NCR alone with 80% being private, 18.5% being public, and 1.5% being public-private data colleges.
Comparatively, there are over 40 colleges, over 90% of which are private, 5% government, and the rest are public-private institutions that offer AI, robotics, and machine learning courses. The big data industry is huge even though machine learning is part of it directly or indirectly.
According to PayScale, the average salary for a Machine Learning professional in India is Rs. 686,281, inclusive of bonuses and profit-sharing.
According to Indeed, the average data scientist in the US makes $109,802 per year while their Machine Learning counterparts take home $132,651 annually.
Even with the several overlaps between the two fields, understanding the difference between big data and machine learning within context is vital. The future of big data and data science, in general, is upgrading and optimizing storage solutions for more efficient data handling. This can be achieved through investing in higher computing power and the research and development of both new and existing techniques and technologies.
For machine learning, the future is in enhancing the level of cognitive and predictive analysis, the ability to operate sustainably independent of human programming, and the ethics that go with it. This will lead to faster and better decision-making, increasing its effectiveness across whichever industries it will be deployed in while minding its impact on humans and the environment around.
With AI ranking as the fastest growing industry globally, there’s no doubt that the demand for both big data and machine learning professionals will soon outstrip its supply. Furthermore, these specialized fields both command lucrative compensation.