@ -66,7 +66,7 @@ The steps necessary to create this projects are the following:
* Create an output sink and specify the job output
* Start the job
To view the full process, check out the [documentation](https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-40229-cxa&ocid=AID30411099).
To view the full process, check out the [documentation](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-40229-cxa&ocid=AID30411099).
### Scientific papers analysis
@ -75,7 +75,7 @@ Let’s take another example, of a project created by [Dmitry Soshnikov](http://
Dmitry created a tool that analyses COVID papers. By reviewing this project, you will see how you can create a tool that extracts knowledge from scientific papers, gains insights, and get a tool that helps researchers navigate large collections of papers in a meaningful way.
Let's see the different steps used for this:
* Extracting and pre-processing information with [Text Analytics for Health](https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
* Extracting and pre-processing information with [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
* Using [Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) to parallelize the processing
* Storing and querying information with [Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
* Create an interactive dashboard for data exploration and visualization using Power BI
Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models, which is time consuming, and often makes inefficient use of expensive compute hardware.
[Azure ML](https://docs.microsoft.com/EN-US/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps data scientists increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively to handle large volumes of data while incurring costs only when actually used.
[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps data scientists increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively to handle large volumes of data while incurring costs only when actually used.
Azure ML provides all the tools developers and data scientists need for their machine learning workflows, including:
@ -98,7 +98,7 @@ Once you have the dataset, we can start the project in Azure.
## 2. Low code/No code training of a model in Azure ML Studio
### 2.1 Create an Azure ML workspace
To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. [Learn more](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
To train a model in Azure ML you first need to create an Azure ML workspace. The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
It is recommended to use the most up-to-date browser that's compatible with your operating system. The following browsers are supported:
@ -226,7 +226,7 @@ Great now that the dataset is in place and the compute cluster is created, we ca
### 2.4 Low code/No Code training with AutoML
Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models.
Automated machine learning (AutoML), is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. It greatly accelerates the time it takes to get production-ready ML models with great ease and efficiency. [Learn more](https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
Automated machine learning (AutoML), is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. It greatly accelerates the time it takes to get production-ready ML models with great ease and efficiency. [Learn more](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109)
1. In the [Azure ML workspace](https://ml.azure.com/) that we created earlier click on "Automated ML" in the left menu and select the dataset you just uploaded. Click Next.
@ -355,7 +355,7 @@ Look more closely at the model explanations and details that AutoML generated fo
In this lesson, you learned how to train, deploy and consume a model to predict heart failure risk in a Low code/No code fashion in the cloud. If you have not done it yet, look more closely at the model explanations that AutoML generated for the top models and try to understand why the best model is better than the other ones.
You can go further into Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
You can go further into Low code/No code AutoML by reading this [documentation](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
We saw how to use the Azure ML platform to train, deploy and consume a model with the Azure ML SDK. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-40229-cxa&ocid=AID3041109).
"You will need to create a [compute target](https://docs.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target) for your AutoML run."
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target) for your AutoML run."
Amagash
3 years ago