Data Science Courses

Training Course on Cloud MLOps on Azure (Azure ML Advanced): Deep dive into Azure services for MLOps. is meticulously designed to equip professionals with the cutting-edge skills and best practices required to streamline the entire Machine Learning Lifecycle on Microsoft Azure.

Training Course on Cloud MLOps on GCP (Vertex AI Advanced): Deep Dive into GCP Services for MLOps provides a deep dive into Cloud MLOps on Google Cloud Platform (GCP), specifically leveraging Vertex AI Advanced capabilities. Participants will gain hands-on expertise in building, deploying, monitoring, and managing robust Machine Learning (ML) pipelines in a production environment.

Training Course on A/B Testing & Experimentation for ML Models: Designing and Analyzing Online Experiments equips participants with the statistical rigor, experimental design principles, and practical tools necessary to confidently launch, analyze, and iterate on ML model deployments, moving beyond offline metrics to live production validation.

Training Course on LLM Deployment & Scaling Strategies: Productionizing Large Language Models is designed to equip machine learning engineers, data scientists, and AI architects with the essential skills to navigate the intricate landscape of enterprise LLM deployment.

Training Course on Synthetic Data Generation using Generative Models: Creating Artificial Data for Privacy and Augmentation empowers data professionals to master the techniques of generating synthetic data.

Training Course on Vector Databases & Embeddings for Semantic Search: Optimizing Similarity Search for LLM Applications provides a comprehensive deep dive into Vector Databases and Embeddings, equipping participants with the essential skills to revolutionize Semantic Search and optimize Large Language Model (LLM) applications.

Training Course on Evaluating & Benchmarking LLM Performance: Metrics and Methodologies for Assessing Generative Models delves into the critical metrics and methodologies essential for rigorously assessing generative models, ensuring optimal deployment and maximizing business value in real-world applications.

Training Course on Open-Source LLMs: Deployment & Customization ? Working with Llama, Mistral, and Derivatives delves deep into the practical aspects of working with state-of-the-art open-source LLMs.

Training Course on MLOps Fundamentals: From Experimentation to Production: Core principles of Machine Learning Operations. delves into the core principles, best practices, and cutting-edge tools that enable organizations to transition ML models from experimental prototypes to robust, scalable, and continuously monitored production systems.

Training Course on Productionizing Machine Learning Models with Docker & Kubernetes: Containerization and Orchestration for ML Deployment is meticulously designed to equip data scientists, machine learning engineers, and DevOps professionals with the essential skills and practical knowledge to seamlessly transition machine learning models from development to robust, scalable, and reproducible production environments.

Training Course on CI/CD for Machine Learning Pipelines: Automating ML Workflow Integration and Delivery emphasizes a hands-on, practical approach to building automated ML pipelines, integrating best-in-class tools and methodologies.

Training Course on Model Monitoring & Performance Drift Detection: Tracking deployed model health and retraining triggers addresses the urgent need for professionals to master the techniques and tools required to effectively track deployed model health and implement retraining triggers.