Certificate Course in
MLOps on Azure
- 60 Hours Classroom & Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Complimentary ML on Cloud Modules
- Complimentary Python Programming Course
- Complementary Kubernetes for Beginners
- Complimentary DevOps for Beginners
The past decade has seen an explosion in the world of data science and machine learning with a lot of companies investing in these fields. However, companies and data scientists quickly realized that while building a model is simple, deploying them at scale is the real challenge. Organizations today are trying to incorporate ML and AI in their applications by adopting MLOps to gain agility and serve the real-world online application. MLOps (Machine Learning Operations) facilitates the management of ML Lifecycle by integrating Data Science and Operations. It also helps in building, training, and deploying machine learning models and workflows to get to production faster. To gain in-depth insights on the fundamentals of MLOps, join the MLOps on Azure course from 360DigTMG.
MLOps on Azure
- Data Science - Traditional ML algorithms & DL (Neural network) algorithms
- Programming - Beginner to Intermediate
MLOps with Kubeflow Course Overview
This course gives you insights into the architecture of MLOps in the Azure Machine Learning environment. It aims to impart knowledge on how to perform MLOps efficiently using Microsoft cloud-native tools such as Azure Machine Learning, Azure Machine Learning Studio, Databricks, MLflow, Kubeflow, Apache Airflow among others. Students will learn to build ML pipelines to design, deploy, and manage model workflows to drive efficiency and productivity with MLOps.
Learn how monitoring and validation of machine learning models can speed up the pace of model development and deployment with MLOps. This course is designed for Data Scientist and Software Developers who wish to leverage Azure Machine Learning to facilitate MLOps practices. Join the ‘MLOps on Azure’ course with 360DigiTMG and learn to streamline and automate the machine learning life cycle by integrating DevOps processes.
What is MLOps?
Machine learning is quickly gaining a lot of traction and is becoming a technology that every company wants to implement but soon realize that creating and training an ML model is much easier than actually practically deploying that model. Machine Learning Operations (MLOps) is a discipline based on DevOps that accelerates the efficiency of workflows and enhances the quality of machine learning solutions.
MLOps on Azure Learning Outcomes
MLOps facilitates the development, deployment, management, and governance of ML models in production environments. This course introduces students to MLOps tools and best practices for building reproducible workflows and machine learning models. This course aims to be a set of guiding principles for MLOps on Azure. Needless to say, it is the result of experts in the domain distilling their hard work and knowledge and putting together a comprehensive guide to enable ML practitioners and Data Scientists to deploy models with ease. The objective of this course is to introduce learners to the MLDC also known as the Machine Learning Development Cycle using open source frameworks like MLflow, Kubeflow, Apache Airflow, Azure Machine Learning, etc. Participants will also learn about containers like Docker and containerized platforms like Kubernetes. So, prepare yourself for more lucrative career options with Industry-relevant curriculums and hands-on Capstone Project in the MLOps on Azure course. The participants will also learn:
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Who Should Sign Up?
- Data Scientists
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- DevOps Engineers
- IT/Software Engineers
- Machine Learning Architects
- Model Risk Managers/Auditors
Modules for MLOps on Azure
MLOps is a fast-growing subdomain of the larger AI/ML/DS domain. It is a portmanteau of the words Machine Learning (ML) and DevOps, signifying that this domain is essentially an intersection of these two different disciplines. While ML deals with the development of algorithms and models using statistical and machine learning techniques, DevOps is focused on deploying the (ML model or any other) software applications into production using the Continuous Delivery and Continuous Integration principles. There is a need for source control, reproducible ML pipelines, model versioning and storage, model packaging, validation, deployment, and monitoring. Besides, there is also a need for model retraining based on the results of the monitoring activities. Currently, MLOps is being thought of as a concept, not a product or a service although a lot of effort is being put into either productizing it or providing it as a service. This course aims to introduce everyone to MLOps done right using Azure with Azure Machine Learning, MLflow, Databricks, and Kubeflow.
Data Scientists and ML practitioners usually are good at building models and performing complex statistical analysis but may not be good at software engineering skills required to put them into production. Also, a simple DevOps approach is not sufficient to address an ML project. It needs to take into account models, data, experiments, runs and artifacts produced. By way of this module we explore how model deployment is being done now vs how could the ideal state be.
Azure Machine Learning (AML) which is a cloud service offering has built-in MLOps. It can easily take care of the different phases of the Machine Learning Development Cycle (MLDC) and also offers complete integration right out of the box with popular ML frameworks like Scikit-learn, Pytorch, Tensorflow and MXNet. This module will provide a deeper look into how AML provides support for all the different phases of the MLDC.
Using Azure Pipelines, ML Engineers can automatically build and test ML projects. It seamlessly integrates CI (Continuous Integration) and CD (Continuous Delivery) and ships the ML model and supporting code to a target of choice. Azure Pipelines also facilitates CT (Continuous Testing) by helping automate the build, test and deploy processes efficiently.
Docker is a containerization mechanism to package the code and the underlying runtime as a Docker image to enable modularity and portability. The aim is to remove the occurrences of ‘it worked on my laptop’ by containerizing all the required elements of the model. Kubernetes is a container orchestration platform which helps in scheduling and orchestrating all the workloads among the different containers.
Understand how Azure ML helps in deploying the ML models by 1. ensuring that the source code is controlled, 2. Creating reproducible training cycles 3. Data and Environment management 4. Creating and maintaining ML pipelines 5. Utilizing model registry to track models and experiments 6. Evaluate and Validate the model 7. Monitor and retrain it as needed
MLflow is an open-source platform which can run on Azure (or any other public cloud or on-prem infrastructure) which also helps in managing the ML lifecycle. Understand the 4 major components
- MLflow Tracking
- MLflow projects
- MLflow Models
- Model registry and see how MLflow can be used in conjunction with Databricks on Azure to create an efficient MLOps framework.
Kubeflow is another open-source framework for MLOps which is gaining popularity very quickly. It is a framework which works on top of the Azure Kubernetes Service (AKS) and helps in orchestrating the different phases on MLDC without a lot of overhead of managing low level Kubernetes APIs. This module teaches how KFserving, Knative and other sub components of Kubeflow work on Azure to create another awesome MLOps framework.
Trends of MLOps on Azure in India
The MLOps market is expected to grow to nearly US$4.1 billion by 2025. MLOps connects the development of the machine learning model and implements it in production. Linking these disparate areas of machine learning requires the right team, continuous integration, and deployment of data along with collaboration and communication between data scientists, developers and platform engineers.
AI and ML practices are soon becoming an intrinsic part of many modern-day business applications. But most organizations have not been successful in delivering AI-based applications and are only hamstrung with transforming data Science models into interactive applications capable of working with large data sets. To address this challenge a new practice called MLOps has emerged that integrates the AI/ML capabilities with DevOps practices. It aims to continuously develop and deliver data along with ML applications. So one of the trends we are going to see will be an increase in the demand for hybrid AI deployments and organizations will use accessible and production-ready data-science platforms.
How We Prepare You
Additional Assignments of over 80+ hours
Live Free Webinars
Resume and LinkedIn Review Sessions
Lifetime LMS Access
Job Placements in Data Science Fields
Unlimited Mock Interview and Quiz Session
Hands-on Experience in Capstone Projects
Life Time Free Access to Industry Webinars
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