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    Machine Learning DevOps Engineer

    BY
    Udacity

    Upgrade your understanding of machine learning tools and technology.

    Brochure Enquire
    Lavel

    Expert

    Mode

    Online

    Duration

    63 Hours

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based

    Course overview

    The Machine Learning DevOps Engineer Live Course is a specialized course that introduces learners to the advanced concepts and techniques of machine learning. The course allows learners to interact with industry experts and engage in the creation of real-world projects all the while providing technical mentor support.

    Machine Learning DevOps Engineer Certification by Udacity follows a flexible learning structure and can be completed within 4 months. The course will assist learners in building a DevOps skillset required for a future career in machine learning.

    All candidates enrolling in the Machine Learning DevOps Engineer Training will receive expert career services and personalized feedback from industry experts.

    The highlights

    • 4 months duration
    • 10 hours weekly study
    • Flexible learning
    • Real-world projects
    • Industry experts
    • Personalized feedback
    • Technical mentor support
    • Career services
    • Resume support and Github review

    Program offerings

    • 4 months duration
    • 10 hours weekly study
    • Flexible learning
    • Real world projects
    • Industry experts
    • Personalized feedback
    • Technical mentor support
    • Career services
    • Resume support
    • Github review.

    Course and certificate fees

    Machine Learning DevOps Engineer Fee Structure

    HeadAmount
    All Access monthly
    		₹21,072 /month
    All Access bundle₹17,911 /month
    Machine Learning DevOps Engineer
    		₹84,545 /one-time payment


    certificate availability

    No

    Who it is for

    • The course is suitable for working professionals who wish to gain skills in machine learning model deployment.

    Eligibility criteria

    • Candidates should have prior knowledge and experience with Python and Machine Learning.
    • Candidates should ideally have knowledge of the data science process and the general workflow of building machine learning models.
    • Candidates should know the basics of Jupyter notebooks and the ways of using them to solve data science problems along with writing scripts using NumPy, pandas, Scikit-learn, TensorFlow, or PyTorch in Jupyter notebooks.
    • Candidates should be familiar with using the Terminal, version control in Git, and using GitHub.

    What you will learn

    Software development skills Machine learning

    After completing the Machine Learning DevOps Engineer Certification Classes, you will gain knowledge about the following topics:

    • Clean code principles
    • PyLint and AutoPEP8
    • Git and Github
    • Deploy modes using MLflow
    • Deploy machine learning models
    • Data Version Control (DVC)
    • Automated model scoring and monitoring

    The syllabus

    Course 1: Welcome to the Machine Learning DevOps Engineer Nanodegree

    An Introduction to Machine Learning DevOps Engineer
    • Welcome! We're so glad you're here. Join us in learning a bit more about what to expect and ways to succeed.
    Getting Help
    • You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

    Course 2: Clean Code Principles

    Introduction
    • Get introduced to clean code principles, why and when to use them, and the history of clean code. Then, see what you'll be able to build by the end of the course!
    GitHub to Azure DevOps Translation Guide
    Coding Best Practices
    • Learn coding best practices, such as clean and modular code, code efficiency, refactoring, documentation, and linting.
    Working with Others Using Version Control
    • Version control is crucial for any coding project, but becomes even more important when working in teams. Another new area in working with teams is the code review, which you'll also learn about here.
    Production Ready Code
    • Find more coding best practices here, such as handling errors, testing and logging, as well as addressing model drift in machine learning models.

    Course 3: Building a Reproducible Model Workflow

    Introduction to Reproducible Model Workflows
    • Dive into reproducible model workflows and machine learning operations, learning about use cases, its history, and what you'll build at the end of the course.
    GitHub to Azure DevOps Translation Guide
    • Build out machine learning pipelines, as well as learning how to version data and model artifacts.
    Machine Learning Pipelines
    • Come up with re-usable processes for performing exploratory data analysis (EDA), cleaning and pre-processing data, and segregating/splitting data.
    Data Validation
    • Validate data through deterministic and non-deterministic testing, and look at handling different parameters with PyTest.  
    Training, Validation and Experiment Tracking
    • Write an inference pipeline, validate and choose your best performing models from experiments, and test your final model artifacts.
    Final Pipeline, Release and Deploy
    • Write a full end-to-end pipeline, release the pipeline, and deploy with MLflow.

    Course 4: Deploying a Scalable ML Pipeline in Production

    Introduction to Deploying a Scalable ML Pipeline in Production
    • We'll introduce you to the course concepts of operationalizing our model, focusing on the ecosystem surrounding that model to successfully deploy it, and easily maintain it in production.
    GitHub to Azure DevOps Translation Guide
    Performance Testing and Preparing a Model for Production
    • In this lesson, we will cover performance testing and preparing a model for production.
    [Optional] Data and Model Versioning
    • In this lesson, we will review git and then delve into Data Version Control (DVC) and the concepts of data provenance.
    CI/CD
    • We cover the software engineering principles of automation, testing, and versioning. We put these into action using Continuous Integration and Continuous Delivery with Heroku and Github Actions.
    API Deployment with FastAPI
    • Delve into FastAPI which leverages type hints to build a robust and self-documenting REST API. First, build out our API locally, test it, and the deploy to Heroku where you'll test it again live

    Course 5: ML Model Scoring and Monitoring

    Welcome to ML Model Scoring and Monitoring
    • This lesson will talk about goals for the course, when to use ML model scoring and monitoring, stakeholders, the history of the field, and the tools and dependencies you need to be aware of.
    GitHub to Azure DevOps Translation Guide
    Model Training and Deployment
    • This lesson will talk about model training and deployment. We’ll focus on automating the training and deployment process and making sure that the trained, deployed models are ready to be monitored.
    Model Scoring and Model Drift
    • This lesson will discuss model scoring and model drift, an important part of the continuous monitoring that makes sure your deployed model remains as accurate as possible.
    Diagnosing and Fixing Operational Problems
    • There are problems that can come up in deployed projects. So this lesson will talk about diagnosing and fixing operational problems, a crucial part of the post-deployment machine learning process.
    Model Reporting and Monitoring with API's
    • This lesson will discuss model reporting and monitoring with APIs which can be used as an automatic interface with your ML project.

    Course 6: Congratulations!

    Congratulations!
    • Congratulations on your graduation from this program! Please join us in celebrating your accomplishments.

    Admission details

    Given below are the steps to enroll in the Machine Learning DevOps Engineer Online Course:

    Step 1: Go to the official website by clicking on the URL given below -

    https://www.udacity.com/course/machine-learning-dev-ops-engineer-nanodegree--nd0821

    Step 2: Click on the "Enroll Now" option.

    Step 3: Find the suitable fee structure and proceed to the next steps of creating an account with Udacity.

    How it helps

    The Machine Learning DevOps Engineer Certification Benefits are given below:

    • The Machine Learning DevOps Engineer Course will help learners understand the processes involved in the integration of machine-learning models and the ways to deploy them to a production-level environment.
    • The skills gained in ML DevOps will open up a wide range of opportunities in industries including healthcare, engineering, transportation, and manufacturing sectors.
    • The course will equip learners to pursue their careers as Data Scientists, Data Engineers, Machine Learning Engineers, or DevOps Engineers.

    Instructors

    Mr Joshua Bernhard
    Data Scientist
    Freelancer

    Mr Giacomo Vianello
    Data Scientist
    Freelancer

    Mr Justin Clifford Smith
    Senior Data Scientist
    Optum Global Solutions

    Ph.D

    Mr Bradford Tuckfield
    Data Scientist
    Freelancer

    Ms Ulrika Jägare
    Head
    Freelancer

    FAQs

    What if I cannot complete the programme within the given time?

    If you fail to complete the course within the time frame, you can continue by making monthly payments.

    What is the average completion time of the course?

    The Machine Learning DevOps Engineer Certification Course has an average completion time of 4 months.

    Can I cancel the course anytime?

    Yes, you have the option to cancel the course anytime.

    Who is providing the Machine Learning DevOps Engineer Course?

    The online course is provided by Udacity.

    How many hours should I spend per week for the Machine Learning DevOps Engineer Course?

    You should spend about 10 hours per week on the Machine Learning DevOps Engineer Online Course.

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