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DP-100: Designing and Implementing a Data Science Solution on Azure

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1 800 EUR + alv 24 %
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1 800 EUR
16.12.2020
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DP-100: Designing and Implementing a Data Science Solution on Azure


Koulutuksessa opit käyttämään Microsoft Azuren Machine Learning PaaS-työkalua sekä sen tekoäly- ja koneoppimisominaisuuksia. Koulutuksen jälkeen osaat hallinnoida datan siirtoa ja prosessointia analysointia varten.
Koulutus valmistaa sertifiointitestiin DP-100: Designing and Implementing a Data Science Solution on Azure.

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.
Lessons
Getting Started with Azure Machine LearningAzure Machine Learning Tools
Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools
After completing this module, you will be able to:

Provision an Azure Machine Learning workspace
Use tools and code to work with Azure Machine Learning

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.
Lessons
Training Models with DesignerPublishing Models with Designer
Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer
After completing this module, you will be able to:

Use designer to train a machine learning model
Deploy a Designer pipeline as a service

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.
Lessons
Introduction to ExperimentsTraining and Registering Models
Lab : Running ExperimentsLab : Training and Registering Models
After completing this module, you will be able to:

Run code-based experiments in an Azure Machine Learning workspace
Train and register machine learning 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.
Lessons
Working with DatastoresWorking with Datasets
Lab : Working with DatastoresLab : Working with Datasets
After completing this module, you will be able to:

Create and consume datastores
Create and consume 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.
Lessons
Working with EnvironmentsWorking with Compute Targets
Lab : Working with EnvironmentsLab : Working with Compute Targets
After completing this module, you will be able to:

Create and use environments
Create and use 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.
Lessons
Introduction to PipelinesPublishing and Running Pipelines
Lab : Creating a PipelineLab : Publishing a Pipeline
After completing this module, you will be able to:

Create pipelines to automate machine learning workflows
Publish and run pipeline services

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.
Lessons
Real-time InferencingBatch Inferencing
Lab : Creating a Real-time Inferencing ServiceLab : Creating a Batch Inferencing Service
After completing this module, you will be able to:

Publish a model as a real-time inference service
Publish a model as a batch inference service

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.
Lessons
Hyperparameter TuningAutomated Machine Learning
Lab : Tuning HyperparametersLab : Using Automated Machine Learning
After completing this module, you will be able to:

Optimize hyperparameters for model training
Use automated machine learning to find the optimal model for your data

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.
Lessons
Introduction to Model Interpretationusing Model Explainers
Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models
After completing this module, you will be able to:

Generate model explanations with automated machine learning
Use explainers to interpret machine learning models

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.
Lessons
Monitoring Models with Application InsightsMonitoring Data Drift
Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift
After completing this module, you will be able to:

Use Application Insights to monitor a published model
Monitor data drift

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Esitietovaatimuksina on:
perustiedot Microsoft Azuresta
kokemusta Python-ohjelmointikielestä sekä NumPy-, Pandas- tai Matplotlib-kirjastoista
datatieteen perustiedot ja mm. kokemusta koneoppimisen mallien kouluttamisesta esimerkiksi Scikit-Learnillä, PyTorchilla tai Tensorflowla.
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Ota Adobe-ohjelmat haltuun!

Pienryhmäkoulutukset lähi- tai etäopetuksena. Mm. InDesign, Photoshop, Illustrator, Acrobat ja värinhallinta. Myös räätälöitynä tarpeesi mukaan!

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