Data Science Courses

Training Course on MLOps for Real-time Inference: Optimizing Models for Low-Latency Predictions is meticulously designed to equip professionals with the cutting-edge skills and practical expertise needed to deploy, manage, and optimize machine learning models for low-latency predictions in production environments.

Training Course on Data Versioning & Experiment Tracking for ML: Tools like DVC, MLflow for Reproducible Research delves into the critical practices of data versioning and experiment tracking, empowering data scientists, ML engineers, and researchers to effectively manage their ML workflows.

Training Course on Cloud MLOps on AWS (SageMaker Advanced): Deep Dive into AWS Services for MLOps provides a comprehensive, hands-on deep dive into Cloud MLOps principles and practices, specifically leveraging advanced AWS services, with a strong focus on Amazon SageMaker

Training Course on Scalable ML Serving with Kubeflow/Sagemaker: Deploying and Scaling Models in Cloud Environments focuses on empowering data scientists, ML engineers, and DevOps professionals with the essential skills to deploy and scale machine learning models efficiently in cloud environments.

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.