Machine Learning
Machine learning allows computers to learn and act like humans without being explicitly programmed and improves their learning over time
What is Machine Learning?
Machine learning is a subset of artificial intelligence that allows computers to learn and mimic humans without being explicitly programmed and improve their learning over time based on information captured from real-world interactions.
Machine learning refers to building algorithms that consume input data and apply statistical analysis to create features for predicting an output. It also involves updating results as new data becomes available.
Various applications of ML systems include but are not limited to:
- Predictive models to forecast credit default risk, customer churn, sales forecast, revenue forecast, etc.
- A speech recognition system that helps in appliance control, call routing, and data entries.
- Finance and insurance sectors that apply ML for fraud investigations and credit checks
- Personalized recommender systems, helping e-commerce stakeholders
How does Machine Learning Help?
In this data-driven business world, ML has become mainstream with its ability to drive actionable insights and accurate predictions. ML helps solve complex problems, identify potential opportunities, and mitigate business risks. The following factors make ML an indispensable solution for a modern business.
Immense data generation: With a vast amount of data generated every moment, data scientists need a framework to organize and analyze these datasets. ML helps apply this data to solve business-critical problems at scale.
Better decision making: ML helps decision stakeholders in guided and informed decision-making by applying statistical methods and algorithms.
Generate meaningful insights: Machine learning helps you analyze data in unconventional ways to uncover hidden facts, patterns, and trends. ML systems help drive actionable insights from large datasets within a fraction of seconds. It also helps automate monotonous and repetitive work.
Machine Learning Approaches
Supervised machine learning: Using the unsupervised ML approach, models learn using labeled data to predict outcomes in this ML system.
Unsupervised machine learning: Models learn using unlabeled and uncategorized data to predict outcomes using the unsupervised ML approach.
Reinforcement learning: Agent-driven learning that involves interacting with the environment, receiving rewards for better performance, and penalties for underperforming.
Driving ROI with AI
Getting machine learning right is the key to modern business success. However, securing desired returns from ML projects is challenged by - data quality and quantity, post-deployment governance, incorrect AI strategy, inappropriate team collaboration, and more. Additionally, manual and complex workflows in ML deployments hinder the rapid detection of ML performance issues. Censius AI Observability Platform helps you crack ML performance monitoring challenges easily.
Censius platform offers a holistic way of monitoring your production ML models. It helps ensure that your ML models are a great business asset forever and not turning into liabilities. Automation of ML model monitoring brings a proactive approach to detecting and troubleshooting issues. Such proactive steps help drive the desired ROI with your AI adoptions.
Further Reading
Introduction To Machine Learning: All You Need To Know About Machine Learning