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IA Designing and Implementing a Data Science Solution  Azure and Databricks

Il corso progettato da First Consulting  è un focus sull'utilizzo in ambito soluzioni IA Machine Learning della Piattaforma "Databricks" nell'implementazione Azure SaaS Platform. La  piattaforma "Managed Services" Databrick è la soluzione "dominante" build on the top Apache Spark per Big Data, ML, Analytics la sua implementazione all'interno dei Cloud Services Azure estende funzionalità e potenzialità applicative. 
Su richiesta risulta possibile in modalità analoga il corso anche nell'implementazione Saas tramite GCP oppure IaaS tramite AWS

DURATA

min 10 gg piano formativo personalizzabile

ATTESTATI

Attestato di partecipazione First Consulting 

CERTIFICAZIONI

Propedeutico exam Azure Databricks DP-100

KEY POINT

Databricks 
Apache Spark Cloud Services 

Programma

 Manage Azure resources for machine learning

  Create an Azure Machine Learning workspace

  • create an Azure Machine Learning workspace

  • configure workspace settings

  • manage a workspace by using Azure Machine Learning studio
     

Manage data in an Azure Machine Learning workspace

  • select Azure storage resources

  • register and maintain datastores

  • create and manage datasets
     

Manage compute for experiments in Azure Machine Learning

  • determine the appropriate compute specifications for a training workload

  • create compute targets for experiments and training

  • configure Attached Compute resources including Azure Databricks

  • monitor compute utilization
     

Implement security and access control in Azure Machine Learning

  • determine access requirements and map requirements to built-in roles

  • create custom roles

  • manage role membership

  • manage credentials by using Azure Key Vault
     

Set up an Azure Machine Learning development environment

  • create compute instances

  • share compute instances

  • access Azure Machine Learning workspaces from other development environments
     

Set up an Azure Databricks workspace

  • create an Azure Databricks workspace

  • create an Azure Databricks cluster

  • create and run notebooks in Azure Databricks

  • link and Azure Databricks workspace to an Azure Machine Learning workspace

Run experiments and train models

Create models by using the Azure Machine Learning designer

  • create a training pipeline by using Azure Machine Learning designer

  • ingest data in a designer pipeline

  • use designer modules to define a pipeline data flow

  • use custom code modules in designer

Run model training scripts 

  • create and run an experiment by using the Azure Machine Learning SDK

  • configure run settings for a script consume data from a dataset in an experiment by using the Azure Machine Learning SDK

  • run a training script on Azure Databricks compute

  • run code to train a model in an Azure Databricks notebook
     

Generate metrics from an experiment run

  • log metrics from an experiment run

  • retrieve and view experiment outputs

  • use logs to troubleshoot experiment run errors

  • use MLflow to track experiments

  • track experiments running in Azure Databricks

Use Automated Machine Learning to create optimal models

  • use the Automated ML interface in Azure Machine Learning studio

  • use Automated ML from the Azure Machine Learning SDK

  • select pre-processing options

  • select the algorithms to be searched

  • define a primary metric

  • get data for an Automated ML run

  • retrieve the best model


Tune hyperparameters with Azure Machine Learning

  • select a sampling method

  • define the search space

  • define the primary metric

  • define early termination options

  • find the model that has optimal hyperparameter values

 

Deploy and operationalize machine learning solutions 

Select compute for model deployment

  • consider security for deployed services

  • evaluate compute options for deployment


Deploy a model as a service

  • configure deployment settings

  • deploy a registered model

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