Machine Learning DevOps Engineer by Udacity: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Learning efforts 10 Hours Per Week

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.

Course and certificate fees

Machine Learning DevOps Engineer Fee Structure

Head	Amount
All Access monthly	₹20,500 /month
All Access bundle	₹17,425 /month for 4-month bundle
Machine Learning DevOps Engineer	₹78,131 /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

Clean Code Principles

Coding Best Practices

Write clean, modular, and well-documented code.
Refactor code for efficiency.
Follow PEP8 standards.
Automate use of PEP8 standards using PyLint and Auto PEP8

Working with Others Using Version Control

Work independently using Git and Github.
Work with teams using Git and Github.
Create branches for isolating changes in Git and Github.
Open pull requests for making changes to production code.
Conduct and receive code reviews using best practices.

Production Ready Code

Correctly use try-except blocks to identify errors.
Create unit tests to test programs.
Track actions and results of processes with logging.
Identify model drift and when automated or non-automated retraining should be used to make model updates.

Building a Reproducible Model Workflow

Machine Learning Pipelines

Learn MLOps fundamentals.
Version data and artifacts.
Write a ML pipeline component.
Link together ML components.

Data Exploration & Preparation

Execute and track the exploratory data analysis (EDA).
Clean and preprocess the data.
Segregate (split) datasets.

Data Validation

Use pytest with parameters for reproducible and automatic data tests.
Perform deterministic and non-deterministic data tests.

Tame the chaos with experiment, code, and data tracking.
Track experiments with W&B.
Validate and choose best-performing model.
Export model as an inference artifact.
Test final inference artifact

Release pipeline code.
Options for deployment and how to deploy a model

Deploying a Scalable ML Pipeline in Production

Performance Testing & Preparing a Model for Production

Analyze slices of data when training and testing models.
Probe a model for bias using common frameworks such as Aequitas.
Write model cards that explain the purpose, provenance, and pitfalls of a model.

Data & Model Versioning

Version control data/models/etc locally using DVC.
Set up remote storage for use with DVC.
Create pipelines and track experiments with DVC.

CI/CD

Follow software engineering principles by automating, testing, and versioning code.
Set up continuous integration using GitHub Actions.
Set up continuous deployment using Heroku

API Deployment with FastAPI

Write an API for machine learning inference using FastAPI.
Deploy a machine learning inference API to Heroku.
Write unit tests for APIs using the requests module.

Automated Model Scoring & Monitoring

Model Training & Deployment

Ingest data.
Automatically train models.
Deploy models to production.
Keep records about processes.
Automate processes using cron jobs.

Model Scoring & Model Drift

Automatically score ML models.
Keep records of model scores.
Check for model drift using several different model drift tests.
Determine whether models need to be retrained and re-deployed.

Diagnosing & Fixing Operational Problems

Check data integrity and stability.
Check for dependency issues.
Check for timing issues.
Resolve operational issues.

Model Reporting & Monitoring with APIs

Create API endpoints that enable users to access model results, metrics, and diagnostics.
Set up APIs with multiple, complex endpoints.
Call APIs and work with their results.

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.