Master Mlops Training Courses

MLOps for Reproducible Research and Model Deployment Training Course is designed to empower data scientists, ML engineers, and research professionals with cutting-edge MLOps practices, tools, and frameworks to ensure reproducible research, automated workflows, and robust model deployment across diverse 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 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 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 Cost Optimization in MLOps: Managing cloud infrastructure and compute costs for ML workloads delves deep into the intersection of MLOps best practices and cloud financial management. Participants will gain actionable insights into identifying cost inefficiencies, implementing governance policies, and adopting a FinOps for ML mindset.

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 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 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.