SQLBits encompasses everything from in-depth technical immersions to the enhancement of valuable soft skills. The full agenda will be announced in the spring; in the meantime check out the timetable and content we cover below.
2024 Training Days
Presenting 2024’s selection of training days, encompassing a deep dive into a range of subjects with some of the best data trainers in the world.
- 08:00 Registration opens and breakfast served.
- All training days run simultaneously across the venue from 09:00 – 17:00 with co-ordinated breaks.
- All training days include regular refreshment breaks and a lunch stop to rest, recharge, and chat to fellow delegates.
- No evening events planned, but if you’re staying over the night beforehand, why not join us in the Aviator on Monday night to meet the training day speakers for an informal drinks reception.
End to End with Azure Machine Learning
Description
Machine Learning is the process of extracting predictive or categorical relationships from large quantities of multiple sources of data. Most often,the Data Scientist uses a series of tools and processes to gain meaning from data – and coordination between other professionals is a manual affair. Microsoft’s Azure Machine Learning (Azure ML) brings together a completely on-line collaborative experimentation environment to build solutions, and a simple way to publish the results so that other code and processes can use them.Buck Woody from the Microsoft Machine Learning and Data Science team (MLADS) will show you not only how to use the Azure ML tool to create solutions, but also explain the entire Cortana Analytics Suite and where AzureML fits in. You’ll also learn the process for creating the solution. We’ll take a solution from source data to published output. At the end of this workshop, you’ll be able to:
Understand the Cortana Analytics Suite and when and where to use Azure ML
Apply the Cortana Analytics Process (CAP) to a given solution
Understand and use the Azure ML Studio environment to create collaborative experiments and publish solutions
Get input data from on-premises, online, and cloud-based sources
Clean, transform, normalized and quantize your data
Build, score and evaluate a Predictive Model
Build and evaluate a categorical model
Publish and stage the predictive model as an Azure-based Service
Requirements:
You’ll need a laptop with connectivity to the Internet
Optionally: Microsoft Excel (if you wish to consume the model in Excel)
Understand the Cortana Analytics Suite and when and where to use Azure ML
Apply the Cortana Analytics Process (CAP) to a given solution
Understand and use the Azure ML Studio environment to create collaborative experiments and publish solutions
Get input data from on-premises, online, and cloud-based sources
Clean, transform, normalized and quantize your data
Build, score and evaluate a Predictive Model
Build and evaluate a categorical model
Publish and stage the predictive model as an Azure-based Service
Requirements:
You’ll need a laptop with connectivity to the Internet
Optionally: Microsoft Excel (if you wish to consume the model in Excel)
Learning Objectives
Previous Experience
Tech Covered
Azure, AzureML, Analytics