Ideal for teams that…
Cloud, automation and CI/CD in practice — hands-on for engineering teams.
How to configure and manage an Azure ML Workspace.
How to deploy ML models on Azure ML Service and monitor their performance.
How to conduct exploratory data analysis and train ML models using Azure ML Designer and AutoML.
How to integrate ML models with other Azure services such as Databricks and Cognitive Services.
What we actually do
- · Introduction to the Azure ecosystem and ML services
- · Configuring the Azure ML Workspace
- · Importing and processing data in Azure
- · Performing EDA using Azure tools
- · Introduction to Azure ML Designer
- · Automating model training with Azure AutoML
- · Training the first model
- · Hands-on exercises: training a model with a real dataset
- · Analyzing results and evaluating the model
- · Using custom scripts to train models
- · Leveraging GPUs and compute clusters to accelerate training
- · Deploying models on Azure ML Service
- · Monitoring and managing deployed models
- · Model deployment and optimization
- · Hands-on exercises: deploying a model
- · Model optimization and hyperparameter tuning
From brief to retro in 30 days.
Brief & diagnosis
A call with the team lead + a short survey for participants. We define goals, gap and context.
Program customization
We adapt modules, case studies and code examples to your stack. Approval in 5 days.
Workshop
Trainer-led sessions, hands-on, code review. Mentor available between sessions too.
Retro + report
Outcome report for the team and lead. 30 days of consulting included.
Send a brief. We'll reply within 1 day.
After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.
Thank you!
We'll get back to you within 1 business day.
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