Data Cleaning and Feature Selection: A Practical GuideLet’s talk about two critical steps in machine learning that can make or break your models: data cleaning and feature selection.Oct 19Oct 19
Understanding Databricks: A Foundation for ML Engineers-1You’re an ML engineer. You’ve worked with data in CSV files, pandas DataFrames, maybe PostgreSQL databases. You’ve trained models locally…Oct 7Oct 7
The Complete GenAI Development Lifecycle (in Databricks)Building compound AI systems isn’t just about writing code. It’s an end-to-end process from understanding business problems through…Oct 6Oct 6
Compound AI Systems: Beyond Simple PromptsRAG is powerful, but it’s just the beginning. Real-world AI applications rarely solve problems with a single prompt-response interaction…Oct 6Oct 6
MLflow and Model Deployment: From Training to ProductionTraining a model is just the beginning. The real challenge is getting it into production reliably, consistently, and at scale across…Oct 4Oct 4
Hyperparameter Tuning: Finding Optimal Model ConfigurationYou built a model. It works. But is it optimized? Could different settings make it better?Oct 3Oct 3
Model Evaluation Metrics: Measuring What Actually MattersYou trained a model. Now what? Is it any good? Better than yesterday’s version? Good enough for production?Oct 3Oct 3
MLflow Concepts: Understanding Experiments, Runs, and OrganizationMLflow’s power comes from how it organizes your machine learning work. Understanding experiments, runs, and their relationships is…Oct 3Oct 3
MLflow: Operationalizing the Machine Learning LifecycleMachine learning in production is messy. Experiments multiply. Models get lost. Deployments break. Nobody remembers which hyperparameters…Oct 3Oct 3
RAG Evaluation Metrics: Measuring What Actually MattersBuilding a RAG system is one thing. Knowing whether it works well is another entirely.Oct 2Oct 2
