When building custom machine learning (ML) models, the phrase “garbage in, garbage out” holds true. Even the most advanced algorithms will fall short if the data you feed them—your features—is not carefully crafted. Feature engineering is the process of shaping raw data into meaningful variables that help your model uncover patterns and deliver reliable results. Let us explore the best practices for feature engineering in 2025, with practical business cases to show how it works and why it matters. What Is Feature Engineering? Feature engineering involves selecting, transforming, and creating variables (features) from your raw data to make your ML model smarter. For example, instead of using a customer’s “date of birth,” you might create a feature like “age range” to better predict shopping habits or fraud risks. It is about turning raw numbers into insights your model can use effectively. Best Practices for Feature Engineering 1. Start with Your Business Goal Before touching the data, understand the problem you aim to solve. Your features should align with your business objectives. For instance, if you want to predict customer churn, focus on features like how often someone buys, their recent support interactions, or how long they have been a customer. Practical Business Case: A subscription-based streaming service engineered features like “average watch time per session” and “number of genres watched” to predict which users might cancel, improving retention by 10%. 2. Tap Into Expert Knowledge Collaborate with people who know your industry inside out. Their insights can guide you to the most relevant data points. In healthcare, for example, a doctor might suggest using “body mass index (BMI)” instead of separate “height” and “weight” measurements for better predictions. Practical Business Case: A hospital worked with clinicians to create a feature called “patient recovery score” based on vital signs and treatment history, which improved readmission predictions by 15%. 3. Deal with Missing Data Smartly Missing data can derail your model. Instead of tossing out incomplete records, try: Practical Business Case: A retailer used purchase history to estimate missing “customer location” data, enabling more accurate regional sales predictions. 4. Balance Feature Scales ML algorithms work best when features are on similar scales. Normalize or standardize variables so that something like “income” (in thousands) does not overpower “age” (in years). Practical Business Case: A bank scaled features like “account balance” and “transaction frequency” to ensure their fraud detection model treated both equally, catching 20% more suspicious activities. 5. Trim Excess Features Too many features can confuse your model and lead to overfitting. Use tools like Principal Component Analysis (PCA) or feature selection algorithms to keep only the most impactful ones. Practical Business Case: An e-commerce platform reduced 50 features to 10 key ones, like “time spent browsing” and “cart abandonment rate,” making their recommendation model faster and more accurate. 6. Combine Features for Deeper Insights Sometimes, two features together tell a stronger story than either alone. Combining variables can reveal patterns your model might miss otherwise. Practical Business Case: A clothing retailer combined “discount offered” and “holiday season” into a single feature, which helped predict sales spikes better than either variable alone. 7. Use Automation Carefully In 2025, tools like FeatureTools, H2O.ai, and AutoML pipelines can generate features automatically. However, always check that these features make sense for your specific business needs. Practical Business Case: A logistics company used an AutoML tool to create features but manually validated ones like “average delivery delay” to ensure they matched real-world conditions, improving route optimization by 12%. Why Feature Engineering Matters Feature engineering is not a one-and-done task—it is an ongoing process of testing and refining. By blending industry expertise, data science techniques, and modern tools, you can create features that unlock the true potential of your custom ML models. Think of features as the bridge between raw data and smart decisions—build them well, and your business will thrive.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing businesses worldwide. From Netflix recommending your next show to banks detecting fraud, these technologies rely on two core approaches: Supervised Learning and Unsupervised Learning. But which one suits your business goals? Let us break it down in clear, simple terms, with practical business cases to show how each works and why it matters in 2025. What Is Supervised Learning? Imagine teaching a child to identify animals using flashcards. You show a picture of a cat and say, “This is a cat.” Then a dog: “This is a dog.” With practice, they learn to recognize cats and dogs independently. That is supervised learning—training an algorithm with labelled data, where the correct answers are already known. Practical Business Cases: Why It is Great: The Catch: What Is Unsupervised Learning? Now picture giving that child a pile of toys and asking them to sort them however they see fit—by colour, shape, or size. That is unsupervised learning—giving an algorithm unlabelled data and letting it discover hidden patterns or groupings on its own. Practical Business Cases: Why It is Great: The Catch: Which One Should You Choose? Your choice depends on your business objective: Many businesses use both together. For instance, a retailer might use unsupervised learning to identify customer segments, then apply supervised learning to predict which segments are most likely to buy a new product. Practical Business Case: A supermarket chain used unsupervised learning to categorize customers into groups like “health-conscious” or “budget shoppers,” then used supervised learning to predict which products each group would buy, increasing sales by 8%. Supervised and unsupervised learning are complementary tools, each with unique strengths. Supervised learning is your go-to for predicting clear outcomes with labelled data. Unsupervised learning uncovers hidden patterns when you are exploring without predefined answers. By aligning the right approach with your business goals, you can harness machine learning to make smarter decisions and stay competitive in 2025.

DevOps: The Essential Strategy for Modern IT Infrastructure | DevOps Strategy In today’s digital landscape, where user expectations are higher than ever, businesses must ensure their IT infrastructure is not only robust but also agile and responsive. DevOps has emerged as a pivotal strategy to meet these demands, fostering a culture of collaboration, automation, and continuous improvement. Breaking Down Silos: Fostering Collaboration Traditional IT models often suffer from compartmentalization, with development and operations teams working in isolation. DevOps bridges this gap, promoting a unified approach to software development and deployment. Case Study: Capital One Capital One faced challenges with prolonged release cycles and manual errors. By adopting DevOps principles, they: This transformation led to faster release cycles and improved security, significantly reducing vulnerabilities in their software. Speed and Agility: Responding to Market Demands In a fast-paced market, the ability to deploy updates swiftly is crucial. DevOps emphasizes automation and continuous integration/continuous delivery (CI/CD) to achieve this. Case Study: Etsy Etsy transitioned from a monolithic architecture to a cloud-native, microservices-based system. By adopting CI/CD pipelines and automating their deployment processes, they: This shift allowed Etsy to respond rapidly to customer needs and market changes. Building Resilience: Ensuring Stability While speed is vital, maintaining system stability is equally important. DevOps practices like Infrastructure as Code (IaC) and continuous monitoring play a significant role in achieving this balance. Case Study: Netflix Netflix operates on a massive scale, requiring a resilient infrastructure. They developed “Titus,” their container management platform, to handle deployments efficiently. Additionally, they implemented “Chaos Monkey,” a tool that randomly disables production instances to test system resilience. These practices have enabled Netflix to: Core Pillars of Successful DevOps Implementation For organizations aiming to adopt DevOps, focusing on the following pillars is essential: Continuous Evolution: The DevOps Journey Adopting DevOps is not a one-time effort but a continuous journey of refinement and adaptation. Organizations must cultivate a culture that embraces change, encourages learning, and prioritizes continuous improvement. Conclusion: Embracing DevOps for Digital Success In an era where digital excellence is a key differentiator, DevOps stands out as a strategic approach to building and managing modern IT infrastructure. By fostering collaboration, embracing automation, and focusing on both agility and stability, organizations can deliver superior value to their customers swiftly and reliably.