The Azure Data Scientist applies their knowledge of data science and machine learning to implementing and running machine learning workloads on Microsoft Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production.

M-DP-100
  • Duration

    3  days
  • Goal

  • Target group

    This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

  • Contents

    Module 1: Introduction to Azure Machine Learning
    In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

    • Getting Started with Azure Machine Learning
    • Azure Machine Learning Tools


    Module 2: No-Code Machine Learning with Designer
    This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

    • Training Models with Designer
    • Publishing Models with Designer


    Module 3: Running Experiments and Training Models
    In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

    • Introduction to Experiments
    • Training and Registering Models


    Module 4: Working with Data
    Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

    • Working with Datastores
    • Working with Datasets


    Module 5: Compute Contexts
    One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

    • Working with Environments
    • Working with Compute Targets


    Module 6: Orchestrating Operations with Pipelines
    Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module.

    • Introduction to Pipelines
    • Publishing and Running Pipelines


    Module 7: Deploying and Consuming Models
    Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

    • Real-time Inferencing
    • Batch Inferencing



    Module 8: Training Optimal Models
    By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

    • Hyperparameter Tuning
    • Automated Machine Learning



    Module 9: Interpreting Models
    Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions.

    • Introduction to Model Interpretation
    • using Model Explainers


    Module 10: Monitoring Models
    After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

    • Monitoring Models with Application Insights
    • Monitoring Data Drift

    CERTIFIATION

    Here you can register for the certification test.

  • Requirements

    Before attending this course, you must have:

    • A fundamental knowledge of Microsoft Azure
    • Experience of writing Python code to work with data, using libraries such as Numpy, Pandas, and Matplotlib.
    • Understanding of data science; including how to prepare data, and train machine learning models using common machine learning libraries such as Scikit-Learn, PyTorch, or Tensorflow.







  • Our quality promise

Contact to our service center

DE+49 (0) 711 90363245

CH+41 (0) 584 595795

AT+43 (01) 33 2353160

FR+41 (0) 584 595454

EN+49 (0) 711 90363245

DA+49 (0) 711 90363245

email[email protected]

Early booking pays off

Book your training at least 3 or 6 months before the start of the course and take advantage of the flexible pricing.

> 6 months

 1.370,00

> 3 months

 1.540,00

0-3 months

 1.710,00

Place Date Language Price
Virtual Classroom 17.10 - 19.10.2022 de  1.710,00
In-house training courses on request Inquiry
  •  Guaranteed to take place
  •   The course will definitely take place if you make a booking
  •   There are no free places left on this course. If you still book it, we will place you on the waiting list.
  • The course price is shown in the currency EUR. For orders from Switzerland, we convert the price into CHF and take into account the appropriate VAT rate. We are also happy to assist you with your order by telephone: CH +41 58 459 57 95 or DE +49 711 903 632 45.

Ausgezeichnet und geprüft

Erfahrungen & Bewertungen zu Trivadis Training Erfahrungen & Bewertungen zu Trivadis Training anzeigen
Back to overview
0 items in cart
Trivadis, Bernd Rössler, Head of Training

Bernd Rössler

Solution Manager Trivadis Training

DE: +49 (0) 711 90363245
CH: +41 (0) 584 595795
AT: +43 (0) 133 2353160
FR: +41 (0) 584 595454
EN: +49 (0) 711 90363245
DA: +49 (0) 711 90363245

E-Mail: [email protected]

“I'll be glad to advise you on topics such as individual coaching, workshops, project support, and online training courses.”

Your Bernd Rössler

trivadis training satisfaction guarantee

Guarantee of quality

Following completion by the participants, every single training course is assessed at the levels

  • suitability of the training room,
  • suitability of the workplace,
  • functionality of the technical equipment, and
  • satisfaction throughout the entire course / seminar,

and recorded in our "TRIVALUATION" feedback system for quality assurance purposes. This enables us to consistently ensure the high quality and satisfaction of our valued customers. We really appreciate the overall rating of 9.3 from 10 points awarded by our many enthusiastic customers.

 

rivadis training success guarantee

Success guarantee

Trivadis guarantees the success of your training. Having completed the course / seminar, do you have any questions about the practical aspects? Would you like to repeat any of the exercises in the lab environment?

Our success guarantee allows you to repeat individual days, or even the entire training course, free of charge for up to 6 months after attending a training course. You bring with you the course materials from the previous training course.