Azure MLOPS Masterclass
Build your expertise into Machine Learning Operations (MLOps) from model development to deployment and scaling
*Based on feedback received from 200+ participants
- Master Azure Fundamentals and Machine Learning
- Advanced Azure MLOPS and Kubernetes
- Advanced Use cases on Azure Databricks
- Best practices in Azure MLOps
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Build your expertise in to Machine Learning Operations (MLOps) from model development to deployment and scaling
The “Azure MLOPS” training program is an immersive journey into the realm of Machine Learning Operations (MLOps) powered by Microsoft Azure.
*Limited offer seats available. Claim yours now
Programme Overview
This comprehensive course is designed to equip participants with the knowledge and practical skills needed to effectively manage the end-to-end machine learning lifecycle, from model development to deployment and scaling.
- Azure Fundamentals and Machine Learning - The course begins by introducing Azure as a cloud platform and explores its machine learning services. Participants will learn to set up Azure environments, workspaces, and datasets. They will delve into model development, hyperparameter tuning, and explore various deployment options within Azure.
- Advanced Azure MLOPS and Kubernetes delves deeper into advanced topics. Participants will explore Azure Container Instances (ACI) and Azure Kubernetes Service (AKS) for model deployment and scaling. Kubernetes, a key framework for scalability, will be thoroughly covered. The course also addresses the critical aspects of handling concurrent requests and load balancing in MLOps environments.
- Advanced Azure services like Azure Databricks and Azure Functions - Participants will learn to implement Azure Logic Apps and explore best practices in Azure MLOps. The culmination of the course is a capstone project, where participants apply their acquired knowledge to an end-to-end Azure MLOps project.
Who is this Programme for?
- This course is ideal for data scientists, machine learning engineers, AI developers, and IT professionals who want to master the art of deploying, managing, and scaling machine learning models on Azure. It's suitable for both beginners and those with some prior experience in Azure and machine learning.
Prerequisites:
While no prior experience in MLOps is required, a basic familiarity with machine learning concepts, Azure fundamentals, and Python programming is beneficial. Participants should have access to a computer with Azure and required software configurations. By the end of this training, participants will be well-versed in Azure MLOps, proficient in Kubernetes, and capable of orchestrating and managing machine learning pipelines and deployments on Microsoft Azure—an invaluable skill set for businesses harnessing the power of AI and machine learning.
Programme Highlights
75+ Hours of Live and Face-to-Face Learning
Mini Projects to design, build, deploy, and scale AI/ML models
Advanced Use cases on Azure Databricks
Emerging trends on Azure Container Instances (ACI)
Real-world scenarios on Azure Kubernetes Service (AKS)
Implement Azure Logic Apps
Best practices in Azure MLOps
Capstone Project - End-to- End Azure MLOPS Project
Learning Outcomes
Enhance your MLOps skills and efficiency.
Building CI/CD Pipelines for ML
Azure Kubernetes Service (AKS) for Model Deployment
Kubernetes in MLOps Pipelines
Azure Databricks for Big Data Analytics
AzureFunctions forServerless Computing
Gen AI for Business Leaders Club
Upon completion of the Gen AI for Business Leaders programme, participants can register with a one-time registration fee of INR 5,000 + GST to receive the prestigious Gen AI for Business Leaders Club .
- Be part of a huge network with fellow leaders and build a community of practice.
- Time to time update on the new versions/tools/researches/projects
- Complete discount of 30% for any upcoming program by Kpiladder
- Lifelong access to the recording of the webinar
- 10% group discount on the fee for referring participants (minimum group of Three)
Programme Modules
- Overview of Azure as a Cloud Platform
- Azure Machine Learning Services
- Setting Up Azure Environment
- Hands-on Lab: Azure Setup and Basics
- Creating and Configuring Azure ML Workspace
- Managing Experiments and Compute Targets
- Azure ML Datasets
- Hands-on Lab:Azure ML Workspace Setup
- Model Training and Evaluation
- Hyperparameter Tuning
- Model Deployment Options in Azure
- Hands-on Lab: Model Development in Azure ML
- Introduction to Azure DevOps
- Building CI/CD Pipelines for ML
- Version Control for ML Models
- Hands-on Lab: Azure DevOps for ML
- Deploying Models as Web Services
- Azure Kubernetes Service (AKS) for Model Deployment
- Monitoring and Scaling Models
- Hands-on Lab: Model Deployment in Azure
- Introduction to Azure Container Instances
- Deploying ML Models in ACI
- Real-time Inference with ACI
- Hands-on Lab: ACI for Model Deployment
- Introduction to Kubernetes
- Azure Kubernetes Service (AKS) Overview
- Deploying Containers in AKS
- Hands-on Lab: AKS for Scaling Solutions
- Kubernetes in MLOps Pipelines
- Managing Deployments with Kubernetes
- Continuous Deployment with AKS
- Hands-on Lab: Kubernetes in MLOps
- Handling Concurrent Requests for REST APIs
- Load Balancing Strategies
- Autoscaling in Azure
- Hands-on Lab: Load Balancing and Autoscaling
- Azure Security and Compliance
- Cost Management in Azure
- Implementing Governance in Azure
- Hands-on Lab: Azure MLOps Best Practices
- Azure Databricks for Big Data Analytics
- Azure Functions for Serverless Computing
- Implementing Azure Logic Apps
- Hands-on Lab: Advanced Azure Services
- Project Kickoff and Data Exploration
- Model Development and Training
- CI/CD Pipeline Setup
- Deployment in Kubernetes and ACI
- Project Presentation and Evaluation
- Review of Key Concepts
- Open Q&A Session
- Course Evaluation and Assessment