Show training

Advanced Data Science tools in Microsoft Fabric

training code: Microsoft Fabric / ENG DL 3d / EN
Authorial training. 

level Intermediate

For more information, please contact the sales department. For more information, please contact the sales department.
5,000.00 PLN 6,150.00 PLN with TAX

At our training you will be acquainted with our brand new analytical platform Microsoft Fabric, especially with what Data Scientists offers. We will teach you how to evaluate data using SynapseML library and prepare data for modelling with Data wrangler tool. We will be monitoring the process of experiments with MlFlow library and register created models in Fabric.

Moreover, we will find out how to use Cognitive Service to enrich our solutions with typical human abilities such as reading comprehension, speech or object recognition.

An important aspect of Data Science is responsibility – predictive models may discriminate some users or simply function worse for certain observation groups. Luckily, tools such as FairLearn allow us to interpret model predictions and analyze their errors

Having completed the training students will be able to use Microsoft Fabric functionalities in Data Science projects.

Python knowledge. Acquaintance with Machine Learning algorithms (completing Machine Learning course with the use of Azure Machine Learning Service graphic tools).

The training is mainly addressed to developers.

Attention – If you associate random forests with exotic holiday, and multiple cross validation with unpleasant orthodontic surgery, first of all we encourage you to participate in the training "Machine Learning with Azure Machine Learning Service graphic tools". As part of the course, individual ML projects stages, popular Machine Learning algorithms and methods of evaluating predictive models.

  • Training: English
  • Materials: English

Training method:

Lecture + exercises

  1. Introduction to Microsoft Fabric
  2. Introduction to Azure Cognitive Services
  3. Data science projects on Fabric platform
  4. Data evaluation with SynapseML library
  5. Data preparation for modelling
  6. Logging experiments with MlFlow library
  7. Registering models
  8. Predictive queries and model deployment
  9. Natural language processing with Cognitive Service
  10. Speech processing with Cognitive Services
  11. Image processing with Cognitive Services