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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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Allan BusiApr 1, 202612 min readThe Training Azure Machine Learning - Deploying Scalable ML Pipelines training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training Azure Machine Learning - Deploying Scalable ML Pipelines training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training Azure Machine Learning - Deploying Scalable ML Pipelines training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Intermediate
€3,300.003 days
remote8 pers. max.Dive into Azure Machine Learning by creating your first secure workspace via the Azure portal and ML Studio interface, install the Python SDK to automate setups, explore compute instances and GPU clusters for fast training, complete practical exercises on resource management, and produce a ready-to-use environment for professional projects with full documentation.
Import and clean large datasets from Blob Storage or Data Lake via Azure ML pipelines, use built-in tools for EDA exploration with Pandas and MLflow tracking, apply automated transformations on real enterprise data, test dataset versioning for traceability, and generate interactive visual reports along with an optimized datastore for ML training.
Launch distributed training jobs on Azure ML with scikit-learn, TensorFlow, and PyTorch, enable AutoML to identify the best models in hours instead of days, configure hyperparameter tuning via Bayesian sweeps on GPU clusters, evaluate performance with ROC-AUC metrics and cross-validation, and produce a leading model with MLflow logs for immediate production integration.
Design CI/CD pipelines with Azure ML Designer and Python SDK for ingestion-enrichment-training-deployment, integrate Git for versioning code and models, test serverless orchestrations with Azure Functions, simulate fault-tolerant scenarios on large data volumes, and deliver a reproducible pipeline with automatic scheduling for continuous enterprise deployments.
Deploy models to real-time ACI/AKS endpoints with secure scoring via Azure AD tokens, configure auto-scaling for traffic spikes, implement monitoring with Application Insights and drift detection, test inference on concrete business cases like churn prediction, generate dashboards and alerts for proactive supervision, and finalize with a deployed and certified red thread project.
10 participants max.
$5,775 / per person
35 hours
in-personTarget audience
Data scientists, ML engineers, data engineers in companies for cloud skills development
Prerequisites
Mastery of Python for data science, basics of machine learning with scikit-learn, active Azure account
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