Bengaluru, India
Mon - Fri : 09:00 - 17:00
Close
+91-9731705961
Mon - Fri : 09:00 - 17:00
Online Instructor-Led Certification BootCamp

DP-100T01-A: Designing and Implementing a Data Science Solution on Azure

4 Days Instructor-led training

This course is designed for professionals to learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure

In this course you will:

  • Learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models
  • Learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace
  • Get introduced to the Designer tool, a drag and drop interface for creating machine learning models without writing any code
  • Learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume
  • Get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models
  • Learn how to create and manage data stores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments
  • Know how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs
  • Implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure
  • Learn how to deploy models for real-time inferencing, and for batch inferencing
  • Explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data
  • How you can interpret models to explain how feature importance determines their predictions
  • Understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift

This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.

Job role: Data Scientist, AI Engineers

Day 1:

Module 1: Introduction to Azure Machine Learning

In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.

Lessons

  • Getting Started with Azure Machine Learning
  • Azure Machine Learning Tools

Lab: Creating an Azure Machine Learning Workspace

Lab: Working with Azure Machine Learning Tools

After completing this module, you will be able to

  • Provision an Azure Machine Learning workspace
  • Use tools and code to work with Azure Machine Learning

Module 2: No-Code Machine Learning with Designer

This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume.

Lessons

  • Training Models with Designer
  • Publishing Models with Designer

Lab: Creating a Training Pipeline with the Azure ML Designer

Lab: Deploying a Service with the Azure ML Designer

After completing this module, you will be able to

  • Use the designer to train a machine learning model
  • Deploy a Designer pipeline as a service

Module 3: Running Experiments and Training Models

In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models.

Lessons

  • Introduction to Experiments
  • Training and Registering Models

Lab: Running Experiments

Lab: Training and Registering Models

After completing this module, you will be able to

  • Run code-based experiments in an Azure Machine Learning workspace
  • Train and register machine learning models

Module 4: Working with Data

Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage data stores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.

Lessons

  • Working with Datastores
  • Working with Datasets

Lab: Working with Datastores

Lab: Working with Datasets

After completing this module, you will be able to

  • Create and consume datastores
  • Create and consume datasets

Day 2:

Module 5: Compute Contexts

One of the key benefits of the cloud is the ability to leverage compute resources on-demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you’ll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs.

Lessons

  • Working with Environments
  • Working with Compute Targets

Lab: Working with Environments

Lab: Working with Compute Targets

After completing this module, you will be able to

  • Create and use environments
  • Create and use compute targets

Module 6: Orchestrating Operations with Pipelines

Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it’s time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you’ll explore how to define and run them in this module.

Lessons

  • Introduction to Pipelines
  • Publishing and Running Pipelines

Lab: Creating a Pipeline

Lab: Publishing a Pipeline

After completing this module, you will be able to

  • Create pipelines to automate machine learning workflows
  • Publish and run pipeline services

Module 7: Deploying and Consuming Models

Models are designed to help decision making through predictions, so they’re only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.

Lessons

  • Real-time Inferencing
  • Batch Inferencing

Lab: Creating a Real-time Inferencing Service

Lab: Creating a Batch Inferencing Service

After completing this module, you will be able to

  • Publish a model as a real-time inference service
  • Publish a model as a batch inference service

Day 3:

Module 8: Training Optimal Models

By this stage of the course, you’ve learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you’ll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data.

Lessons

  • Hyperparameter Tuning
  • Automated Machine Learning

Lab: Tuning Hyperparameters

Lab: Using Automated Machine Learning

After completing this module, you will be able to

  • Optimize hyperparameters for model training
  • Use automated machine learning to find the optimal model for your data

Module 9: Interpreting Models

Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It’s increasingly important to be able to understand the factors that influence the predictions made by a model and to be able to determine any unintended biases in the model’s behaviour. This module describes how you can interpret models to explain how feature importance determines their predictions.

Lessons

  • Introduction to Model Interpretation
  • using Model Explainers

Lab: Reviewing Automated Machine Learning Explanations

Lab: Interpreting Models

After completing this module, you will be able to

  • Generate model explanations with automated machine learning
  • Use explainers to interpret machine learning models

Module 10: Monitoring Models

After a model has been deployed, it’s important to understand how the model is being used in production and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data.

Lessons

  • Monitoring Models with Application Insights
  • Monitoring Data Drift

Lab: Monitoring a Model with Application Insights

Lab: Monitoring Data Drift

After completing this module, you will be able to

  • Use Application Insights to monitor a published model
  • Monitor data drift

Successful Azure Data Scientists start this role with a fundamental knowledge of cloud computing concepts, and experience in general data science and machine learning tools and techniques.

Specifically:

  • Creating cloud resources in Microsoft Azure.
  • Using Python to explore and visualize data.
  • Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.

To gain these prerequisite skills, take the following free online training before attending the course:

If you are completely new to data science and machine learning, please complete Microsoft Azure AI Fundamentals first.

Azure subscription
Visual Studio Code
Anaconda
Operating system- Windows or Mac

Shivam Sharma – Co-founder, TechScalable

Shivam is an author, cloud architect, speaker and Co-Founder at TechScalable. Being passionate about ever evolving technology he works on Azure, GCP, Machine Learning & Blockchain. He is also a Microsoft Certified Trainer.

He works on projects offering learning and understanding in the fields of Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Big Data Analytics and Data Visualization. He is also a Microsoft Certified Trainer, Microsoft certified solution expert for cloud platform and infrastructure, Microsoft certified solution associate for Machine Learning, Microsoft certified solution developer for azure platform.

He also have following certifications:

  • Google Cloud Certified – Associate Cloud Engineer
  • Microsoft Certified Trainer (MCT)
  • Microsoft Certified: Azure Solutions Architect Expert
  • Microsoft Certified: Azure Data Scientist Associate
  • Microsoft Certified: Azure Developer Associate
  • Microsoft Certified: Azure DevOps Engineer Expert
  • Microsoft Certified: Azure Security Engineer Associate
  • Microsoft Certified: Azure AI Engineer Associate
  • Microsoft Certified: Azure Administrator Associate
  • Microsoft Certified: Azure Fundamentals
  • MCSA: Machine Learning – Certified 2018
  • MCSE: Cloud Platform and Infrastructure — Certified 2018
  • MCSE: Cloud Platform and Infrastructure — Certified 2016

 

It will be purely Hands-on and Case study driven training program.

In case the batch is cancelled, the amount would be credited back to the payee’s account in 5 working days.

    I agree that my submitted data is being collected and stored.

    Testimonials

    She is having In-depth Knowledge on the subject and excellent presentation skill towards the delivery of the training.

    Manjunath N

    A very systematic and detailed training program, covered many topics in a short span in time. Personally enjoyed the training a lot. Great Instructor.

    Bhaskar

    Mamta is the best trainer I came across. Her excellent teaching skills and courteous personality has helped me tremendously. It is comforting to know that whenever I have a question you answer right away. I have learned so much from you and I look forward to learning more. Thank you for being a remarkable teacher and I am very grateful.

    Naga Neelam

    Mamta was precise and detail-oriented in the training approach. She was able to address many of the queries which were relevant to different practical scenarios. Appreciate all the effort and knowledge provided by the trainer.

    Aananth Gopalan

    The trainer was very patient to explain basic concepts in an understandable manner to a non-developer. Has tons of experience and patience.

    Giridharan Ramaswamy

    Fantastic Trainer, very friendly and encouraging. All of the exercises were on a scale of good to excellent. I thoroughly enjoyed your class. The course materials are excellent. Her professional attitude is much appreciated. I have been very pleased with her efforts. Mamta's enthusiasm and passion are exemplary. Some valuable experiences and learning – thank you

    Naveen Kumar

    It was very interesting and intensive training. Shivam is a very qualified and experienced trainer. Shivam, thank you for your course.

    Ruslan Abdrakmanov

    He is friendly and very knowledgeable in the subject.

    Ilias Shaik

    Having Proficient Knowledge in Cloud Technologies, Great to have him. The Best Trainer in my life so far.

    Swamy Vallamalla

    Yes, It was excellent training by the Trainer. He is always ready to clear the doubts at any time. Overall Perfect.

    Arunkumar Manickavasagam