INTRODUCTION – Orchestrate Pipelines And Deploy Real-Time Machine Learning Services With Azure Machine Learning This will also be setting up practice pipelines to enable DevOps around machine learning workflows. In this module, you’ll learn how to create, publish, and run pipelines for training models with Azure Machine Learning. You will already have learned how to … Read more

INTRODUCTION – Deploy Batch Inference Pipelines And Tune Hyperparameters With Azure Machine Learning Predictive modeling through batch processing of large data sets is the mainstay of machine learning models. In this module, learn using Azure Machine Learning to publish a batch inference pipeline for trimming extensive batch prediction errands. Perform massive cloud-scale experimentation with the … Read more

INTRODUCTION – Select Models And Protect Sensitive Data In this module, you’ll learn to utilize automated machine learning in Azure Machine Learning to fit the best model for the best data. You’ll study differential privacy, which is an advanced method that produces useful observations while hiding value information which could otherwise uniquely identify a person. … Read more

INTRODUCTION – Monitor Machine Learning Deployments Biases present in machine learning models may lead them to give biased results. This module will cover how you can apply Fairlearn and Azure Machine Learning to discover and reduce these biases in your models. You will also learn to employ telemetry for assessing the performance of models after … Read more

INTRODUCTION – Introduction To Azure Databricks In this session, you will get to know the efficacious functionalities of Azure Databricks or an Apache Spark Notebook to run large datasets. This module will make you aware of the Azure Databricks platform and help you classify which types of tasks are well done by Apache Spark. It … Read more

INTRODUCTION – Get Started With Databricks And Machine Learning In this module you will learn how to take advantage of PySpark’s machine learning library to construct important elements of a machine learning workflow. The module will teach you how to conduct exploratory data analysis and find the relevant insights and patterns from your data, how … Read more

Module 5: Manage Machine Learning Lifecycles And Fine Tune Models Almost every student is keenly aware of conditions students find themselves in to track learning processes using MLflow. Students also learn how to include modules from the machine learning library of Spark for hyperparameter tuning and model selection. This ensures that students understand how to … Read more

INTRODUCTION – Train A Distributed Neural Network And Serve Models With Azure Machine Learning In this section, you will learn to use Uber’s Horovod framework and Petastorm to run distributed deep learning training jobs using Spark with training datasets in Apache Parquet format. You will also explore how to register, package, and deploy trained models … Read more

INTRODUCTION – EXAM PREPARATION COURSE 1 This is the revisit of Course 1 content from Microsoft Azure Data Scientist Associate standard specialization. The course lays down concepts and skillsets introductory to using Azure with data science-essential. Data exploration, data preparation, and basic machine learning concepts, among other things, will be topics discussed in the review. … Read more