Post by rahim on Jan 31, 2024 5:27:49 GMT -5
Learning algorithms that are optimized to efficiently handle extremely large amounts of data in a distributed environment.Unlike Azure ML Studio, Amazon SageMaker is heavily code-based, meaning all steps must be written in Python. Although this allows the greatest possible flexibility - especially since Python is very popular with data scientists - it makes it difficult for people who are not familiar with Python to get started.Azure ML Studio / Azure ML ServicesMicrosoft currently provides two different machine learning platforms: Azure ML Studio and Azure.
ML Services .The core element of Azure ML Studio is an DB to Data interactive, graphical working environment: using drag & drop, datasets and analysis modules can be inserted into an interactive canvas and connected to a workflow and executed. This is undoubtedly very helpful, especially for newcomers to the field of machine learning, but it comes at the expense of a certain flexibility in model development.This is probably why Microsoft introduced a new set of ML-focused products in September 2017 under the umbrella name Azure ML Services . Unlike Azure ML Studio, Azure ML.
Services does not have built-in methods; instead, the models must be created completely custom. However, they offer a powerful toolset and the ability to integrate common ML frameworks such as TensorFlow, scikit-learn, etc.While Azure ML Studio is primarily aimed at beginners, the ML Services are primarily intended for experienced data scientists. However, it is doubtful whether this separation into two different product lines really makes sense. The introduction of ML Services definitely caused some confusion in the Azure developer community, as it now requires choosing between two different platforms that cannot.
ML Services .The core element of Azure ML Studio is an DB to Data interactive, graphical working environment: using drag & drop, datasets and analysis modules can be inserted into an interactive canvas and connected to a workflow and executed. This is undoubtedly very helpful, especially for newcomers to the field of machine learning, but it comes at the expense of a certain flexibility in model development.This is probably why Microsoft introduced a new set of ML-focused products in September 2017 under the umbrella name Azure ML Services . Unlike Azure ML Studio, Azure ML.
Services does not have built-in methods; instead, the models must be created completely custom. However, they offer a powerful toolset and the ability to integrate common ML frameworks such as TensorFlow, scikit-learn, etc.While Azure ML Studio is primarily aimed at beginners, the ML Services are primarily intended for experienced data scientists. However, it is doubtful whether this separation into two different product lines really makes sense. The introduction of ML Services definitely caused some confusion in the Azure developer community, as it now requires choosing between two different platforms that cannot.
