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DATA ANALYTICS   |   AZURE ML
DATA ANALYTICS
AZURE ML

Empower Your ML Workflow

Leverage powerful Machine Learning capabilities with Azure ML solutions from Exigo Tech

AZURE ML FOR UNMATCHED PERFORMANCE

Build, Deploy and Manage Machine Learning Models Easily with Azure ML  

ACCELERATE MACHINE LEARNING PROJECTS

CORE OFFERINGS
Automated Machine lerning

Automated
Machine Learning

It is a feature of Azure Machine Learning that allows users to build machine learning models without requiring extensive knowledge of data science or machine learning. Machine learning models can be quickly and easily created with AutoML, which automates the model selection, hyperparameter adjustments, and feature engineering processes. Businesses can utilise Azure ML’s AutoML to reduce time-to-market for machine learning solutions and gain important insights from their data faster.

Modal management

Model Management

Azure Machine Learning provides model management capabilities that enable businesses to manage the entire machine learning lifecycle by providing a centralised repository for model versioning, tracking and deployment. This includes easy deploying and managing of models across different environments, from development to production, with built-in monitoring capabilities. With model management, businesses can smoothly collaborate and share models across the organisation which ensures the constant deployment and updation of models as needed.
Data prepration

Data Preparation

Azure Machine Learning provides powerful tools for data preparation and cleansing which is an important aspect of the machine learning process. The capabilities like data wrangling, data cleaning, and data normalisation allow businesses to ensure that their data is ready to be used by machine learning models. Additionally, data versioning and tracking enable businesses to keep track of changes made to their data throughout the machine learning lifecycle.
modeal monitoring

Model Monitoring

Azure ML offers tools such as metrics tracking, anomaly detection, and drift detection to track the performance of deployed models. Continuous monitoring helps to maintain the performance and accuracy of deployed models. The platform offers real-time alerts and automated actions for model retraining and data fixes. With the help of Azure ML’s Model Monitoring, businesses can improve the dependability and stability of their machine learning models, which ensures accurate insights and forecasts over time.
Azure ML studio

Azure
ML Studio

Azure Machine Learning Studio is a web-based development environment that allows businesses to build, train, and deploy machine learning models. The drag-and-drop interface allows users to create data pre-processing pipelines, train models using a variety of algorithms, and deploy models as web services or in containers. With its powerful visual interface, Azure ML Studio streamlines machine learning development and makes it quicker and more effective.
Integration with Other Azure Services

Integration with
Other Azure Services

Azure ML integrates seamlessly with other Azure services including Azure Databricks, Azure Synapse Analytics , and Azure Cognitive Services, to enable businesses to build, deploy, and manage the machine learning models at scale. Azure ML also supports integration with popular open-source tools and frameworks like PyTorch, TensorFlow, Scikit-learn, and more. With seamless integration of Azure ML with other Azure services, businesses can streamline their machine learning workflows and gain valuable insights from their data.

Photo Niten Devalia
Niten Devalia

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Alpesh Prajapati

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