"Data science and machine learning are not just about the algorithms; they are about asking the right questions and finding meaningful insights."
- Hilary Mason
Data Science and Machine Learning (ML) are very closely interconnected areas. That is the reason they are often used interchangeably, many times incorrectly too. They function together to analyze and create vast chunks of data. They are all totally related, but they offer various aspects and have different approaches of operation.
Data Science and Machine Learning are swiftly emerging and evolving areas that leverage the power of data to get useful information, predictions, as well as decisions. The data has become highly-centric in terms of Artificial Intelligence (AI), mainly with the rise of generative AI and DSML is facing the latest path as well as obstacles that will structure its future in the coming years.
Data science is mainly related to the area of study that essentially deals with managing huge amounts of data via modern tools and techniques to understand hidden patterns, acquire useful insights, and create informed business decisions. The processes of involving gathering, organizing, and examining data are connected to the area of data science.
Machine learning is a subfield of AI that mainly aims on building algorithms and models that can learn from, predict or take decisions related to data. It consists of training a model on a dataset, giving it to automatically learn patterns and relationships within the data, and then utilize those models for predictions or take actions on latest, unseen or unused data.
Data science professionals often use ML as one of the essential tools to analyze and interpret data. They also use ML algorithms to develop predictive models, classify data, cluster similar data points, or unleash patterns as well as trends. ML techniques are mainly useful when dealing with massive and complex datasets where traditional analytical approaches may be impractical or inefficient.
While ML is an important component of data science, data science professionals have a vast skill set that consists of data cleaning and preprocessing, exploratory data analysis, statistical analysis, data visualization, and domain expertise. They understand the end-to-end data lifecycle, from data collection and cleaning to analysis and interpretation.
A few real-world examples and uses cases that give clear interrelation:
In fraud detection data science and machine learning plays an important role. It involves analyzing huge chunks of data to understand the patterns and anomalies that indicate fraudulent works. ML algorithms are useful to identify the patterns and anomalies, whereas data science is present in collecting and preparing the data for analysis.
- Use Case
There are several applications whose main functionality is online payments. For instance,?PayPal?is a popular company, which uses a combination of ML and data science methods to examine substantial quantities of transaction data to understand and highlight the fraudulent actions. The system can easily identify patterns and anomalies in the data that indicates fraudulent behavior, like atypical expenditure habits or suspicious IP locations. By excelling in these tools and strategies, PayPal can avoid the transactions that are fraudulent, well-safeguard its users, and also preserve the trustworthiness of its digital environment.
Natural Language Processing (NLP) is an advance stage, which comprises of linguistics information, computer science, engineering, as well as AI. These are helpful for programming the systems to process and analyze huge datasets. Data science professionals must do tasks extensively on NLP that includes language, audio, and video processing by leveraging many NLP libraries and tools.
- Use Case
AI-powered chatbots like ChatGPT actively use ML algorithms and NLP libraries and tools to understand natural language queries and give better personalized responses.?To build an AI-powered Chabot, developers are required to initially collect and prepare a huge dataset of training data. This dataset is required to train ML models that can understand and interpret natural language text. Once the models are trained, they get integrated into the Chabot to offer highly intelligent and accurate responses.
Predictive maintenance is where the data analysts often analyze the data from sensors to predict the probability related to failure of equipment. Various ML algorithms are used to know the patterns in the data that pinpoint the potential issues, giving maintenance teams to take corrective action before the occurrence of failure. In simple terms, to predict future results depending on the historical data.
- Use Case
General Electric (GE) is a popular industrial software firm, which uses predictive maintenance. It uses it to acquire sensor data from its equipment and analyzes it to predict when maintenance is required.
A recommendation system is a kind of algorithm that creates personalized recommendations based on the user’s available information. Data science is used here to get and analyze the user data, while ML is helpful to build the algorithm that powers the recommendation system.
- Use Case
Amazon and Netflix uses?personalized recommendation algorithms. Amazon uses it for product recommendations to its customers. It takes help from machine learning techniques and algorithms to understand user behavior information, such as, previous transactions, product ratings, and browsing history, to give personalized suggestions to the customers. Whereas, Netflix uses to offer personalized movie and TV show recommendations to its streamers. By improving their experience and enhancing the likelihood of switching to premium plans or continuing to use the platform.
Peter Krensky, Director Analyst at Gartner, spoke at the Gartner Data & Analytics Summit in Sydney, Australia stated: “As machine learning adoption continues to grow rapidly across industries, DSML is evolving from just focusing on predictive models, toward a more democratized, dynamic and data-centric discipline. This is now also fueled by the fervor around generative AI. While potential risks are emerging, so too are the many new capabilities and use cases for data scientists and their organizations.”
The future of DSML is characterized by automation, interpretability, privacy considerations, edge computing, and advancements in deep learning, ethical considerations, continuous learning, and interdisciplinary collaboration. These trends will redefine the way DSML is implemented across several different domains and industries, driving innovation and transforming the way and organizations works and grows.
Here are top trends in which DSML is evolving and influencing the future:
Edge AI is growing to enable data processing at the point of creation at the edge, near IoT endpoints, rather than in centralized servers or clouds. This gives real-time inputs, pattern detection, and data privacy. It enhances the AI model development, orchestration, integration, and deployment for firms to improve performance.
According to Gartner, “By 2025, 55 percent of all data analysis by deep neural networks will happen at the point of capture in an edge system”. The companies must identify the applications and AI training needed to move to edge environments closer to the IoT endpoints.
Data ecosystems are shifting from self-oriented software or blended deployments to end-to-end cloud-native solutions that give massive scalability, flexibility, and integration. The enterprises can evaluate cloud data ecosystems depending on their ability to solve the distributed data challenges as well as access to integrate with data beyond their immediate environment. “50 percent of latest system deployments in cloud?will be depending on a cohesive cloud data ecosystem rather than on integrated point solutions that are manual by 2024,” survey by Gartner.
Data-centric AI is a shift from model and code-centric approaches to a focus on data quality and availability to build better AI systems. Data-centric AI solutions include AI-specific data management, synthetic data generation, and data labeling technologies that aim to overcome data challenges such as accessibility, volume, privacy, security, complexity, and scope. The use of generative AI to create synthetic data is rapidly growing, with Gartner predicting that by 2024, 60 percent of data for AI gets synthetic to simulate reality, future and derisk AI.
Data-centric AI represents a transformation from a model and code-centric approach to more data focused to develop better AI systems. Solutions like AI-specific data management, synthetic data and data labeling technologies, aim to solve many data challenges, such as accessibility, volume, privacy, security, complexity and scope. The usage of?generative AI?to make synthetic data is quickly growing, reducing the burden of getting real-world data so ML models can be trained effectively.
Investment in AI is enhancing by?various enterprises applying solutions and also by industries that are focusing to grow via AI-based technologies and businesses are active in AI investments. According to Gartner, “by the end of 2026, more than USD 10 billion will invest in AI firms that are reliable on basic models that are large AI models trained on huge volumes of data. Gartner also conducted a survey with more than 2,500 executive leaders and gained insights that 45 percent stated that recent hype of ChatGPT had led them to accelerate the investments of AI. 70 percent of respondents also said that their company uses generative AI for research and exploration while 19 percent are in the pilot or production phase.
Responsible AI, also called as Ethical AI or AI Governance, is an evolving area whose main objective is to enable AI systems are designed and applied in a responsible and ethical way. It covers a range of principles, guidelines, and practices which are aimed at addressing the potential risks and challenges related to AI technologies. Responsible AI aims to create AI a positive force than a threat to society and development to enable inclusivity and remove harmful consequences. According to Gartner, which predicts that the level of pre-trained AI models among 1 percent of AI vendors will rise by 2025 and makes responsible AI a societal concern, Gartner also stated that the industries must adopt a risk-proportional ways to offer AI value and be prepared when implementing the solutions and models. They must also look for vendor assurances to handle their risk and compliance obligations.
The DSML leads to the development of powerful tools and technologies, which are continuing to advance, and accelerate by addressing ethical, social, and legal considerations, which will remain a major priority for businesses, governments, and the AI research community. Several legal frameworks and discussions on AI accountability and liability are obtaining momentum. These trends possess a unique and important role and are active in solving complicated real-world issues and drive advanced innovations in several different industries with emerging recognition of the necessity of shaping the future.
