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MLOps Certification Course

MLOps Certification Course
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    Instructor-led Introduction to MLOPS live online Training Schedule

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    Starts at 5,200 / monthWith No Cost EMI Know more
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    Why enroll for Machine Learning Operations Certification Course?

    pay scale by Edureka courseGlobal MLOps market is expected to reach $75.42 billion by 2033 from $2.08 billion in 2024 at a CAGR of 43.2% - Market.us
    IndustriesLarge enterprises dominate the MLOps market, possessing a significant 71% share in the year 2023 - Market.us
    Average Salary growth by Edureka courseThe average salary for MLOps Engineer is $1,76,248 per year in the United States with a $38,892 cash bonus per year - Glassdoor

    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
    MLOps Architect average salary
    Hiring Companies
     Hiring Companies
    Annual Salary
    MLOps Engineer average salary
    Hiring Companies
     Hiring Companies
    Annual Salary
    MLOps Lead average salary
    Hiring Companies
     Hiring Companies

    Why Machine Learning Operations Certification Course from edureka

    Live Interactive Learning

    Live Interactive Learning

    • World-Class Instructors
    • Expert-Led Mentoring Sessions
    • Instant doubt clearing
    Lifetime Access

    Lifetime Access

    • Course Access Never Expires
    • Free Access to Future Updates
    • Unlimited Access to Course Content
    24x7 Support

    24x7 Support

    • One-On-One Learning Assistance
    • Help Desk Support
    • Resolve Doubts in Real-time
    Hands-On Project Based Learning

    Hands-On Project Based Learning

    • Industry-Relevant Projects
    • Course Demo Dataset & Files
    • Quizzes & Assignments
    Industry Recognised Certification

    Industry Recognised Certification

    • Edureka Training Certificate
    • Graded Performance Certificate
    • Certificate of Completion

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    About your Machine Learning Operations Certification Course

    MLOps Course Skills

    • skillVersion Control
    • skillModel Packaging
    • skillModel Management
    • skillModel Deployment
    • skillMonitoring and Debugging
    • skillMLOps on Cloud

    Tools Covered

    • Amazon Sage Maker
    • Aws
    • Docker
    • Flask
    • Git
    • Jenkins
    • Jupyter
    • Kubeflow
    • MLflow
    • Prometheus
    • Python
    • Streamlit
    • Tensorflow
    • Visual Studio Code

    MLOps Course Curriculum

    Curriculum Designed by Experts

    AdobeIconDOWNLOAD CURRICULUM

    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

    skillHands-on:

    • MLOps Case Study

    skillSkills

    • 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

    skillHands-on:

    • Git Common Commands
    • Branching and Merging

    skillSkills

    • 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

    skillHands-on:

    • ML Model Packaging and Testing

    skillSkills

    • 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

    skillHands-on:

    • Developing ML models using Streamlit
    • Building ML models using Flask
    • Deploying ML models using FAST API

    skillSkills

    • 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

    skillHands-On:

    • Building CI/CD Pipelines

    skillSkills

    • 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

    skillHands-On:

    • Working with MLFlow

    skillSkills

    • 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

    skillHands-On:

    • Docker CLI Commands
    • Writing a Dockerfile Deployment
    • DaemonSets
    • Deploying Services

    skillSkills

    • 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

    skillHands-On:

    • ML Model Monitoring
    • Monitoring using WhyLogs

    skillSkills

    • Model Monitoring and Debugging

    Kubeflow for Building ML Pipelines

    5 Topics

    Topics:

    • Kubeflow Introduction
    • Features
    • Kubeflow Fairing
    • Kubeflow Pipelines
    • Working with Kubeflow

    skillHands-On:

    • Building ML Pipeline with Kubeflow

    skillSkills

    • 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

    skillHands-On:

    • Building an end-to-end MLOps pipeline using AWS SageMaker

    skillSkills

    • 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

    skillHands-On:

    • Implementing A/B Testing Methodologies

    skillSkills

    • Implementation of A/B testing methodologies for model evaluation
    • Strategies for Ensuring model Integrity and Security

    Course Details

    About the MLOPS Course

    This course offers a comprehensive introduction to MLOps, focusing on the effective deployment and management of machine learning models in production. It covers the entire ML lifecycle, including data preparation, model development, deployment, monitoring, and retraining. Students get hands-on experience with essential MLOps tools and frameworks such as LangChain, CrewAI, AutoGen, and retrieval-augmented generation (RAG), alongside core technologies like MLflow, Kubernetes, Docker, and cloud platforms, enabling them to build scalable, automated workflows that bridge data science and operations.

      Key features:
      • Cloud-ready, practical, and hands-on training
      • Real-world projects for deploying and managing ML models at scale
      • Training on industry-standard MLOps tools and platforms
      • Focus on automating the ML lifecycle using best DevOps practices
      • Lifetime access to course materials and 24/7 technical support

      Who should take up this Course?

      This course is well-suited for programmers, developers, data analysts, statisticians, data scientists, and software engineers.

        What are the prerequisites for this certification Course?

        To excel in MLOps, you need Python skills, ML algorithm knowledge, experience with Docker, Kubernetes, and cloud platforms. A laptop with at least 8GB RAM, Intel i3+ processor, and stable internet is also required.

          How will I execute the practicals in this 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.

            What is this MLOps Certification Course?

            A professional training course that teaches end-to-end MLOps such as development, deployment, monitoring, and operations, using key tools and techniques in line with industry standards.

              Is this course interactive and project-based?

              Yes, It includes live classes, hands-on labs, quizzes, and end-to-end real-world projects.

                Which cloud platforms and tools are covered?

                This training course covers AWS SageMaker, Azure ML, Google Cloud Platform, MLflow, Docker, Kubernetes, Jenkins, and more with AI tools.

                  Projects

                   certification projects

                  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....
                   certification projects

                  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....

                  Certification

                  To unlock the Edurekaโ€™s 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
                  MLOps certifications serve as proof of your expertise in deploying and managing machine learning models effectively. They enhance your career prospects by making you a sought-after candidate in a competitive job market. Certified professionals contribute to smoother ML model deployment, monitoring, and maintenance, improving overall operational efficiency. Additionally, these certifications promote better collaboration among data scientists, DevOps teams, and stakeholders by aligning everyone with MLOps best practices.
                  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
                  You will earn a Course Completion Certificate from Edureka, validating your expertise in MLOps practices.
                  Yes, Edurekaโ€™s MLOps certificate is valued by companies globally due to the project-oriented and cloud-integrated training.
                  Edureka Certification
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                  XYZ123431st Jul 2024
                  The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate
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                  Dheerendra YadavProject Lead at HCL Technologies, Ghaziabad, Uttar Pradesh, India
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                  Earlier I had taken training in different technologies from other institutes and companies but no doubt Edureka is completely different, First time in my carrier I have received such kind of training and support. They have really awesome instructors. The support persons are technically sound and I would like to appreciate their 24 x 7 support. I never seen such kind of support by other companies in India till now. When I had started training on Hadoop I do not have any idea of Java but their training structure is marvelous and they taught Java in very easy way and build up confidence in it. My training is still going on and it is about to finish and I would like to thanks Edureka to help me to find robust path of carrier with such a new and emerging technology of Big Data.

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                  My experience with edureka about Python is much impressed, with such quality of the training. We get access to each day class recorded sessions after the live class. Edureka is very quick reactive towards the queries of each single user, they answer and follow-up to ensure you get the correct response to resolve your query. Online recording of the course is very useful as you can go back and refer at any time. You have access to course and materials for ever, which is really helpful. More over 24/7 quality support, which is very much important to any of us. I must thank to Edureka for giving such support. Thanks guys, cheerรƒยขรขย‚ยฌร‚ยฆ

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                  The online course delivered by Edureka on big data, Hadoop - developer is really nice. The first thing of Edureka is that, the very first day of your registration, you have access the full tutorial ( record of some previous batch) and which will provide enough knowledge/teaching, so whenever you are attending your actual training session, you are not a fresher of the session, but a kind, expert and you can clear any knowledge gap.More of that, as you are getting 2 lectures for the same session, your knowledge is much more, than you would have expected. As these recording will be available you for ever, you always go again and again and again.The help/administration/support is really really good.For Hadoop big Data developer, I will recommend to join.Thanks

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                  FAQs

                  What is MLOps?

                  MLOps, or Machine Learning Operations, is a set of practices designed to streamline the development, deployment, and maintenance of machine learning models, much like DevOps but tailored specifically for ML workflows.

                  Is this course suitable for working professionals?

                  ML Engineers focus on building models, while MLOps Engineers deploy and maintain them in production using DevOps practices.

                  Will I get placement assistance after completing this MLOps Training?

                  No, we do not provide placement assistance. The focus is on providing in-depth knowledge and hands-on skills to help you advance your career independently.

                  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. 

                  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 ?

                  You can easily catch up on missed lectures through recorded sessions in your LMS or by joining a live session in another batch.

                  What skills do I need to learn for MLOps?โ€Ž

                  You need strong programming skills, proficiency in infrastructure as code, an understanding of cloud services, and knowledge of machine learning concepts and frameworks to learn MLOps.

                  Why MLOps is Important?

                • Bridges the gap between data science and software engineering for faster and more reliable ML deployments.
                • Improves model performance and ensures higher reliability in production.
                • Automates and streamlines the ML lifecycle, enhancing efficiency and scalability.
                • What will be the mode and duration of this training?

                  We only offer live online training on weekends for a course duration of 5.5 weeks.

                  Does MLOps require coding?

                  Yes, MLOps generally requires coding knowledge because developers need it for MLOps to deploy models in production environments.

                  What is the cost of MLOps course in India?

                  The cost of the MLOps certification course in India is INR 21,999

                  Is MLOps better than DevOps?

                  MLOps focuses on automating machine learning tasks such as model training, while DevOps focuses on traditional software development tasks such as code deployment and builds.

                  What language is best for MLOps?

                  Python is the best language for MLOps

                  What is the significance of MLOps in the machine learning lifecycle?

                  The importance of MLOps in machine learning is to ensure faster deployment and easier troubleshooting.

                  What is the role of MLOps engineer?

                  They deploy, manage, and optimize machine learning models in production environments to ensure smooth integration and efficient operations.

                  What is the salary of MLOps analyst?

                  According to Glassdoor, the average salary for MLOps Engineer/Analyst is โ‚น11,64,260 per year in India.

                  Does MLOps have a future?

                  MLOps is a powerful and essential approach shaping the future of Machine Learning, and it's one of the most in-demand and rewarding skills to learn in 2024. With AI and ML being adopted across industries, MLOps roles are growing rapidly, offering strong career growth and competitive salaries from both a technical and business perspective.

                  What are the differences between DevOps and MLOps?

                  Both MLOps and DevOps are different. MLOps is based on Machine Learning While DevOps is based on development. In DevOps tools like Jenkins, GitLab CI, and Travis CI are used. In MLOps tools like MLflow, Weights & Biases are used.

                  What is the salary of MLOps in India?

                  MLOps Engineer salaries in India range from โ‚น6โ€“10 LPA for entry-level, โ‚น10โ€“20 LPA for 2โ€“5 years of experience, and โ‚น20โ€“35+ LPA for senior roles with strong cloud and CI/CD skills.

                  How long does it take to learn MLOps?

                  This course takes 5.5 Weeks to complete.

                  Who earns more, MLOps or data engineer?

                  In data science and machine learning, MLOps engineers typically earn more than data engineers due to their specialized skills in model deployment, automation, and cloud-based CI/CD pipelines.

                  Can beginners join this MLOps course?

                  Yes, If you have a basic background in Python and ML concepts, you can start this course

                  Do I need to know Docker and Kubernetes for MLOps?

                  Yes, basic knowledge of Docker and Kubernetes is important as theyโ€™re core tools for model deployment.
                  Have more questions?
                  Course counsellors are available 24x7
                  For Career Assistance :