Why enroll for MLOps Certification Training Course?
Global 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
Large enterprises dominate the MLOps market, possessing a significant 71% share in the year 2023 - Market.us
The 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
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
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
Hands-On:
Building an end-to-end MLOps pipeline using AWS SageMaker
Skills
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
Hands-On:
Implementing A/B Testing Methodologies
Skills
Implementation of A/B testing methodologies for model evaluation
Strategies for Ensuring model Integrity and Security
Free Career Counselling
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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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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.