MLOps is a practice that streamlines the work between different teams like development and operations. Microsoft Azure is a leading name in the field of cloud computing. MLOps applies DevOps principles into the ML process. The aim is to experience faster development and deployment of Azure ML models into production. To better understand these two together, let's understand the capabilities of MLOps Azure. Explore igmGuru's MLOps certification training program to build your career in Machine Learning.
Utilizing MLOps Azure aids organizations in streamlining the tasks of model experimenting and building. A well-segregated and integrated workflow as well as intuitive components are utilized to make this happen. Here are certain key benefits and capabilities of MLOps Azure.
Azure provides a humongous variety of resources that are available for all types of ML workloads, which also includes GPUs. Organizations do not have to fret about optimizing jobs to a great degree since all this is stored and accessed remotely.
MLOps comes with plenty of features like reproducibility, model monitoring and experiment tracking. Azure ML provides many different APIs and components, including MLFlow for tracking logs and experiments during runtime. The results are easily accessible in the future.
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MLOps Azure's automated machine learning takes the data to find the most apt single model or sets of models. Various parallel pipelines are created on the cluster, which run across various parameters having most suitable feature selection.
With MLOps Azure, organizations get immediate, swift and easy access to a gigantic number of public data sets. Additionally, companies can store their own data in the highly secure environment offered by Azure storage. All this can be accessed seamlessly in the workspace via the data store.
Using an interactive GUI, the Designer is capable of building an ML pipeline in its entirety. Different blocks of components such as splitting data, train models, load data, evaluate models and data transformation are depicted as flowcharts. These can also be modified further.
This is an MLOps Azure architecture for Python models that are using Azure ML service. It showcases implementation of continuous delivery, continuous integration and retraining pipeline for an AI app. Here are the different components of it.
Understanding 'what is MLOps Azure' is pretty easy. All one needs to do is take the help of the right professionals who have skills in the field. The amalgamation of the two is a brilliant one for organizations and thus, the demand for those with these skills is growing rapidly.
Course Schedule
| Course Name | Batch Type | Details |
| MLOps Training | Every Weekday | View Details |
| MLOps Training | Every Weekend | View Details |
