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MLOps Certification Training Course
MLOps Certification Training Course
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Live Online Classes starting on 25th May 2024
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Instructor-led Introduction to MLOPS live online Training Schedule
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Why enroll for MLOps Certification Training Course?
MLOps Course Benefits
The demand for MLOps engineers is rising across geographies due to the widespread adoption of AI and ML across industries and a recent report projected that the market will grow at a CAGR of 43.2% for the ensuing decade. You will acquire the knowledge to efficiently deploy, monitor, and manage machine learning models, along with practical experience in real-world scenarios, to be ready to navigate the intricacies of MLOps and progress in your professional career within the continuously changing technological environment.
Annual Salary
Hiring Companies
Want to become a MLOps Architect?
Annual Salary
Hiring Companies
Want to become a MLOps Architect?
Annual Salary
Hiring Companies
Want to become a MLOps Architect?
Why MLOps Certification Training Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your MLOps Certification Training Course
MLOps Course Skills
Tools Covered
MLOps Course Curriculum
Curriculum Designed by Experts
MLOps Essentials
15 Topics
Topics:
- Introduction to MLOps
- MLOps vs. DevOps
- SDLC Basics
- Waterfall vs AGILE vs DevOps vs MLOps
- MLOps Phases
- Versioning
- Testing
- Automation
- Reproducibility
- Deployment
- Monitoring
- MLOPs Architecture
- ML Pipeline
- MLOps Tools
- MLOPs Case Study
![skill]( "Hands On Experience")
- MLOps Case Study
![skill]( "skill you will learn")
- Understanding MLOps Concepts
- Proficiency in SDLC Methodologies
Version Control System
7 Topics
Topics:
- Git Essentials
- Configuring Git
- Branching
- Git Workflow
- Repo
- Git Commands
- GitHub Action
![skill]( "Hands On Experience")
- Git Common Commands
- Branching and Merging
![skill]( "skill you will learn")
- Tracking and managing changes to code
- Source Code Management
- Tracking and Saving Changes in Files
Packaging ML Models
13 Topics
Topics:
- Model Packaging
- Experimentation
- Model Fitting
- Challenges in Working inside the Jupyter Notebook
- Create Config Module
- Data Handling Module
- Data Preprocessing
- Pipelines
- Training and Prediction
- Requirements.txt file
- Testing Virtual Environments
- Pytest
- Model Packaging and Testing
![skill]( "Hands On Experience")
- ML Model Packaging and Testing
![skill]( "skill you will learn")
- Model Packaging Techniques
- Data Preprocessing and Handling
- Testing and Validation of ML models
Build MLApps using New Age Tools
8 Topics
Topics:
- API Essentials
- Streamlit Fundamentals
- Working with Flask
- REST API
- FAST API
- Building ML Model with Streamlit
- Creating ML Model with Flask
- ML Model Deployment with FAST API
![skill]( "Hands On Experience")
- Developing ML models using Streamlit
- Building ML models using Flask
- Deploying ML models using FAST API
![skill]( "skill you will learn")
- ML Application Development
- ML Pipelines with FAST API
CI/CD for ML Models
13 Topics
Topics:
- Introduction to CI/CD
- CI/CD Challenges
- CI/CD Implementation in ML
- Popular DevOps Tools
- AWS CodeCommit
- AWS CodePipeline
- AWS CodeBuild
- AWS CodeDeploy
- Azure Boards
- Azure Repos
- Azure Pipeline
- Azure Test Plans
- Azure Artifacts
![skill]( "Hands On Experience")
- Building CI/CD Pipelines
![skill]( "skill you will learn")
- Implementation of CI/CD Pipelines for ML
Machine Learning Model Management
9 Topics
Topics:
- What is Model Management?
- Activities in Model Management
- Data Versioning
- Code Versioning
- Experiment Tracking
- Model Cataloging
- Model Monitoring
- Overview of Model Management tools
- Working with MLFlow
![skill]( "Hands On Experience")
- Working with MLFlow
![skill]( "skill you will learn")
- Data Management
- Code Versioning
- Experiment Tracking
Docker and Kubernetes for ML Deployment
20 Topics
Topics:
- Containerization
- Docker
- Docker Architecture
- Docker for Machine Learning
- Docker Desktop Installation
- Working with Docker
- Running Docker Container
- Dockerfile
- Pushing Docker Image to DockerHub
- Dockerize the ML Model
- Container Orchestration
- Kubernetes Core Concepts
- Pod
- Deployment
- Replica
- Service
- Volumes (PVC)
- Monitoring
- Liveness and Readiness Probes
- Labels and Selectors
![skill]( "Hands On Experience")
- Docker CLI Commands
- Writing a Dockerfile Deployment
- DaemonSets
- Deploying Services
![skill]( "skill you will learn")
- Continuous Deployment
- Writing a Dockerfile to Create an Image
- Installing Docker Compose
- Configuring Local Registry
- Container Orchestration
- Application Deployment
Monitoring and Debugging of ML System
10 Topics
Topics:
- Model Monitoring Importance
- Tools for Model Monitoring
- Understanding ML Model Monitoring
- Challenges in Monitoring ML Models
- Exploring Model Drift Phenomenon
- Operational Level Monitoring Insights
- Introduction to Prometheus Monitoring System
- WhyLabs Setup Process
- Exploring WhyLogs Features: Drift Detection, Input/Output Monitoring, Bias Detection
- WhyLogs Usage: Constraints and Drift Reports
![skill]( "Hands On Experience")
- ML Model Monitoring
- Monitoring using WhyLogs
![skill]( "skill you will learn")
- Model Monitoring and Debugging
Kubeflow for Building ML Pipelines
5 Topics
Topics:
- Kubeflow Introduction
- Features
- Kubeflow Fairing
- Kubeflow Pipelines
- Working with Kubeflow
![skill]( "Hands On Experience")
- Building ML Pipeline with Kubeflow
![skill]( "skill you will learn")
- Building end-to-end ML pipelines with Kubeflow
MLOps on Cloud
18 Topics
Topics:
- Getting Started with Amazon Sagemaker
- Amazon SageMaker Notebooks Overview
- Configuration of Notebook Instance
- Creating, Training, and Deploying ML Models with Sagemaker
- Setting up Endpoints and Endpoint Configurations
- Executing Inference from Deployed Models
- Exploring SageMaker Studio & Domain Features
- Introduction to SageMaker Projects
- Understanding Repositories in SageMaker
- Utilizing Pipelines and Graphs in SageMaker
- Conducting Experiments with SageMaker
- Managing Model Groups in SageMaker
- Configuring Endpoints in SageMaker
- Introduction to Azure Machine Learning Studio
- Exploring Azure MLOps
- Understanding Azure ML Components
- Integration of Azure MLOps with DevOps
- Setting up Fully Automated End-to-End CI/CD ML Pipelines
![skill]( "Hands On Experience")
- Building an end-to-end MLOps pipeline using AWS SageMaker
![skill]( "skill you will learn")
- Using Amazon SageMaker for ML development and deployment
- Management of ML Models and Experiments using Azure Machine Learning
ML Model Post Deployment Challenges (Self-paced)
8 Topics
Topics:
- Ensuring Model Integrity
- Counteracting Adversarial Attacks
- Guarding Against Data Poisoning
- Preventing Distributed Denial of Service (DDOS)
- Safeguarding Data Privacy
- Strategies for Mitigating Model Attack Risks
- Implementing A/B Testing Methodologies
- Outlook on the Future of MLOps
![skill]( "Hands On Experience")
- Implementing A/B Testing Methodologies
![skill]( "skill you will learn")
- Implementation of A/B testing methodologies for model evaluation
- Strategies for Ensuring model Integrity and Security
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MLOps Course Details
About MLOps Certification Training Course
This training program on MLOps Certification offers a thorough guide on how to deploy, monitor, and manage machine learning models. Participants will acquire a deep understanding of MLOps concepts, SDLC methodologies, and model management tasks, and they will learn how to effectively use version control systems, package ML models, and create ML applications with cutting-edge tools. Practical sessions will enhance their learning experience, delving into areas like CI/CD implementation, Docker, Kubernetes, and model monitoring.
The course will also cover the utilization of cloud platforms such as Amazon SageMaker and Azure Machine Learning Studio for ML development and deployment. Moreover, participants will tackle post-deployment issues such as model integrity and adversarial attacks, gaining valuable insights into model evaluation and risk management strategies.
What are the learning outcomes of this MLOps Course?
The MLOps Course encompasses the following learning outcomes:
- Understanding the significance of deploying, monitoring, and managing machine learning models in real-world scenarios.
- Grasping MLOps concepts, SDLC methodologies, and model management practices.
- Applying version control systems, packaging ML models, and developing ML applications with contemporary tools.
- Examining and executing CI/CD pipelines, Docker, Kubernetes, and model monitoring methods.
- Assessing and leveraging cloud platforms like Amazon SageMaker and Azure Machine Learning Studio for ML development and deployment.
- Devising strategies to tackle post-deployment issues such as model integrity, adversarial attacks, and data privacy, ensuring effective risk management and model assessment.
Why take up this MLOps Training Course?
By enrolling in this MLOps Training Course, individuals can acquire specialized skills in efficiently deploying, monitoring, and managing machine learning models in production environments. The certification endorses the skills gained by the learners, boosting their career opportunities and establishing participants as adept experts in the dynamic realm of MLOps. Moreover, the course's hands-on approach equips learners to address practical issues and make valuable contributions to companies utilizing AI technologies.
Who should take up this MLOps Course?
This course on MLOps is well-suited for data scientists, machine learning engineers, software developers, and IT professionals who wish to improve their abilities in deploying, monitoring, and managing machine learning models in practical scenarios.
Furthermore, those individuals with a desire to progress in artificial intelligence and data science careers will find great value in this comprehensive course on MLOps.
What are the prerequisites for this MLOps Course?
The requirements for enrolling in this MLOps Course generally consist of a high level of proficiency in programming languages like Python, understanding of core machine learning concepts, familiarity with various cloud computing platforms, and knowledge of DevOps principles.
What are the system requirements for this MLOps course?
The system requirements for this MLOps Course includes:
- A laptop or desktop computer with a minimum of 8 GB RAM with Intel Core-i3 and above processor.
- Stable and high-speed internet connection is necessary for accessing online course materials, videos, and software.
How will I execute the practicals in this MLOps Course?
Practicals for this course will be implemented using various tools and detailed step-by-step installation guidesfor these tools are available on the LMS. In case you come across any doubt, the 24*7 support teams will promptly assist you.
MLOps Training Course Project
Automated Model Deployment and Scaling
Design an MLOps pipeline with the purpose of automating the deployment and scaling of machine learning models to accommodate varying workloads is crucial. The primary objective o....Dynamic Model Monitoring
Develop a framework for MLOps to ensure ongoing monitoring and updating of machine learning models using real-time data streams. The aim is to identify any deterioration, deviati....MLOps Certification
To unlock the Edurekaโs MLOps Certification Training Course, you must ensure the following:
- Completely participate in this MLOps Certification Training Course
- Evaluation and completion of the quizzes and projects listed
The significance of undergoing an MLOps Certification Training Course lies in its capacity to furnish individuals with the necessary knowledge, skills, and qualifications essential for excelling in the swiftly evolving domain of machine learning operations. Upon successful completion of such a program, individuals acquire adeptness in the deployment, monitoring, and management of machine learning models in operational settings.
This certification enhances the career prospects and empowers learners to make valuable contributions to organizations that harness AI technologies. Moreover, the certification serves as a validation of their expertise, endorsing their credibility and acknowledgment within the sector, ultimately creating avenues for career progression and development.
After obtaining certification in MLOps, you may be qualified for a range of job roles. Some of the more specific job roles you may be qualified for include:
- MLOps Engineer
- MLOps Lead
- Devops & ML Engineer
- Associate Architect - MLOps
- MLOps Consultant
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Knowledgeable Presenters, Professional Materials, Excellent Customer Support what else can a person ask for when acquiring a new skill or knowledge to...
It is a very good experience to learn from edureka!.Faculty seems excellent in sharing real world experiences.I do not feel even for single moment tha...
I got full value from Edureka's DevOps course. I had to come out of my comfort zone as I was expecting a lot theory but this had continuous Lab/practi...
I'm currently enrolled in a lot of courses offered at Edureka, so I'm attending live classes and using their study materials, course projects, have ac...
Edureka has very good instructors and technical support team, I have completed the course BIG DATA & AWS through web training, and it was very good tr...
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MLOps Training Course FAQs
What is MLOps?
MLOps, also known as Machine Learning Operations, refers to the practices, procedures, and tools employed for optimizing the deployment, management, and scaling of machine learning models in operational settings. It amalgamates principles from DevOps and data engineering to ensure the effective progression, implementation, supervision, and sustenance of machine learning frameworks. The primary goal of MLOps is to mechanize and standardize the lifespan of machine learning models, empowering entities to furnish high-quality, reliable, robust, and scalable AI solutions.
Will I get placement assistance after completing this MLOps Training?
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
Who are the instructors for MLOps Course?
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
What if I have more queries after completion of MLOps Course?
Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co.
What if I miss a class of MLOps Course?
You will never miss a lecture at Edureka! You can choose either of the two options:
- View the recorded session of the class available in your LMS.
- You can attend the missed session in any other live batch.
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