Take ML models from notebook to production — master the full MLOps lifecycle with expert-led, hands-on training.
Our MLOps training courses bridge the gap between data science and production engineering. You will learn to operationalise machine learning models at scale using MLflow, Kubeflow, Vertex AI, and SageMaker — covering experiment tracking, model registry, automated retraining, and production monitoring.
Programs are delivered by practitioners with hands-on experience deploying ML systems at scale across financial services, healthcare, and e-commerce. Choose from cohort-based online training or dedicated 1-to-1 sessions.
MLflow, DVC, and Weights & Biases for reproducible experiment tracking and model versioning.
Kubeflow Pipelines, Apache Airflow, and Metaflow for automated training, evaluation, and deployment.
Feast, Tecton, and Hopsworks for managing shared, discoverable, and consistent ML features.
TorchServe, BentoML, Triton Inference Server, and Kubernetes-native serving for low-latency inference.
Detecting data drift, concept drift, and prediction quality degradation using Evidently AI and Arize.
AWS SageMaker, Azure ML, and Google Vertex AI for managed end-to-end ML lifecycle in the cloud.
Deploy real models to Kubernetes, SageMaker, and Vertex AI in live lab environments.
Expert-led sessions with real-time Q&A from practising ML engineers.
ML operations engineers available around the clock to answer questions.
Courses aligned to AWS ML Specialty, GCP Professional ML Engineer, and Databricks ML objectives.
Three ways to learn — from free self-service to dedicated 1-to-1 instruction.
Self-service practice tests to assess your knowledge and prepare for certification. No sign-up required.
Instructor-led live sessions with cohort peers, hands-on labs, real-time Q&A, and exam preparation.
Fully personalised training delivered by a senior engineer, exclusively for you at your pace and schedule.
Join 1600+ ML practitioners who have mastered MLOps with our expert-led training programs.