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

Course Description

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 with Azure Machine Learning and MLflow.

Key Learnings:

  • Use Azure Machine Learning Service & Studio for model development.
  • Prepare data, train, optimize, and deploy models at scale.
  • Work with Datastores, datasets, and ML Pipelines.
  • Implement real-time & batch inferencing.
  • Master Hyperparameter tuning, AutoML & Model interpretation.
  • Ensure responsible AI with monitoring tools like Application Insights & Data drift detection.
  • Ideal for data scientists looking to optimize ML workflows on Azure.

Curriculum for this course

22 Lessons
Module 1: Explore and configure the Azure Machine Learning workspace
5 Lessons
  • Explore Azure Machine Learning workspace resources and assets
    .
  • Explore developer tools for workspace interaction
    .
  • Make data available in Azure Machine Learning
    .
  • Work with compute targets in Azure Machine Learning
    .
  • Work with environments in Azure Machine Learning
    .
Module 2: Experiment with Azure Machine Learning
2 Lessons
  • Find the best classification model with Automated Machine Learning
    .
  • Track model training in Jupyter notebooks with MLflow
    .
Module 3: Optimize model training with Azure Machine Learning
4 Lessons
  • Run a training script as a command job in Azure Machine Learning
    .
  • Track model training with MLflow in jobs
    .
  • Perform hyperparameter tuning with Azure Machine Learning
    .
  • Run pipelines in Azure Machine Learning
    .
Module 4: Manage and review models in Azure Machine Learning
2 Lessons
  • Register an MLflow model in Azure Machine Learning
    .
  • Create and explore the Responsible AI dashboard for a model in Azure Machine Learning
    .
Module 5: Deploy and consume models with Azure Machine Learning
2 Lessons
  • Deploy a model to a managed online endpoint
    .
  • Deploy a model to a batch endpoint
    .
Module 6: Develop generative AI apps in Azure AI Foundry portal
7 Lessons
  • Introduction to Azure AI Foundry
    .
  • Explore and deploy models from the model catalog in Azure AI Foundry portal
    .
  • Get started with prompt flow to develop language model apps in the Azure AI Foundry
    .
  • Build a RAG-based agent with your own data using Azure AI Foundry
    .
  • Fine-tune a language model with Azure AI Foundry
    .
  •  Evaluate the performance of generative AI apps with Azure AI Foundry
    .
  • Responsible generative AI
    .

Drop us a message for any query

For more information about our courses, get in touch
with Alchemy contacts

Course rating

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90%

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80%

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65%

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60%

Reviews

  • Ruma Sriwastav

    Cover all my needs

    The course identify things we want to change and then figure out the things that need to be done to create the desired outcome. The course helped me in clearly define problems and generate a wider variety of quality solutions. Support more structures analysis of options.

All Courses

Skills Offered

Emerging Skills Offered by Alchemy for 2024-2025

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Data Science & Analytics
  • Advanced Data Engineering (e.g., Snowflake, Databricks)
  • Real-Time Data Streaming (e.g., Kafka, Flink)
  • Predictive Analytics with AI/ML Models
  • Advanced Business Intelligence Tools (e.g., Power BI, Tableau)
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Emerging Programming Frameworks
  • Quantum Computing Basics (IBM Qiskit, Google Cirq)
  • Web3 Development: Solidity, Smart Contracts
  • Low-code/No-code Platforms (OutSystems, Mendix)
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Infrastructure Modernization
  • Software-Defined Networking (SDN) and NFV
  • Advanced Virtualization (Citrix, VMware updates)
  • Hybrid and Edge Computing
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Soft Skills and Leadership in Tech
  • Agile Transformation and Leadership
  • Design Thinking for Problem Solving
  • Leading AI-Driven Innovation
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AI & Machine Learning
  • Generative AI (e.g., ChatGPT, Bard, MidJourney integrations)
  • Machine Learning Operations (MLOps)
  • Advanced NLP and Speech Recognition
  • AI for Edge Computing
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Cloud Technologies
  • Multi-Cloud Management: Azure, AWS, GCP integrations
  • Cloud-Native Development: Kubernetes, Docker, Serverless Architectures
  • AI and ML integration in Cloud Platforms
  • FinOps: Cloud Cost Management
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Cybersecurity
  • Zero Trust Architecture (e.g., Illumio)
  • Incident Response and Threat Intelligence
  • Cloud Security (AWS, Azure, and GCP-focused)
  • Advanced Penetration Testing and Ethical Hacking
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DevOps and DevSecOps
  • Infrastructure as Code (IaC) with Terraform and Ansible
  • GitOps and Continuous Integration/Continuous Delivery (CI/CD)
  • Security in DevOps Pipelines
Image

Data Science & Analytics
  • Advanced Data Engineering (e.g., Snowflake, Databricks)
  • Real-Time Data Streaming (e.g., Kafka, Flink)
  • Predictive Analytics with AI/ML Models
  • Advanced Business Intelligence Tools (e.g., Power BI, Tableau)
Image

Emerging Programming Frameworks
  • Quantum Computing Basics (IBM Qiskit, Google Cirq)
  • Web3 Development: Solidity, Smart Contracts
  • Low-code/No-code Platforms (OutSystems, Mendix)
Image

Infrastructure Modernization
  • Software-Defined Networking (SDN) and NFV
  • Advanced Virtualization (Citrix, VMware updates)
  • Hybrid and Edge Computing
Image

Soft Skills and Leadership in Tech
  • Agile Transformation and Leadership
  • Design Thinking for Problem Solving
  • Leading AI-Driven Innovation
Image

AI & Machine Learning
  • Generative AI (e.g., ChatGPT, Bard, MidJourney integrations)
  • Machine Learning Operations (MLOps)
  • Advanced NLP and Speech Recognition
  • AI for Edge Computing
Image

Cloud Technologies
  • Multi-Cloud Management: Azure, AWS, GCP integrations
  • Cloud-Native Development: Kubernetes, Docker, Serverless Architectures
  • AI and ML integration in Cloud Platforms
  • FinOps: Cloud Cost Management
Image

Cybersecurity
  • Zero Trust Architecture (e.g., Illumio)
  • Incident Response and Threat Intelligence
  • Cloud Security (AWS, Azure, and GCP-focused)
  • Advanced Penetration Testing and Ethical Hacking
Image

DevOps and DevSecOps
  • Infrastructure as Code (IaC) with Terraform and Ansible
  • GitOps and Continuous Integration/Continuous Delivery (CI/CD)
  • Security in DevOps Pipelines