MLOps Training Online and Certification Course

SKU: 8413
15 Lesson
|
50 Hours
Machine Learning Operations or MLOps training program by igmGuru will facilitate you in mastering all the concepts centric to this technology. Our MLOps course online covers all integral aspects of orchestrating and managing ML lifecycle that includes versioning, monitoring, model creation, and deployment. Your MLOps certification is a proof of your skills around creating scalable and seamless ML systems with efficiency to offer accurate and robust predictive models.

MLOps Course Overview

igmGuru's Machine Learning Operations or MLOps Training online program sets the benchmark for the industry. Machine Learning Operations or MLOps is a set of practices and tools that enable collaboration and communication between data scientists and operations teams in the deployment and management of ML models. MLOps Course Online Program incorporates the latest syllabus to cater to the needs of multiple industries across the world. The MLOps Certification course has been formulated using the rich experience and expertise of industry captains.

MLOps or Machine Learning Operations, is a set of practices and tools that enable collaboration and communication between data scientists and operations teams in the deployment and management of machine learning models.

Our MLOps Certification Training online covers the following topics:

1. Continuous integration and delivery (CI/CD) for machine learning models
2. Automating and monitoring model training and deployment
3. Managing and scaling infrastructure for machine learning
4. Managing and versioning machine learning models and data
5. Collaboration and communication tools for data scientists and operations teams
6. Best practices for security and compliance in machine learning
7. Additionally, the course may cover specific tools and technologies used in MLOps such as Kubernetes, Docker, Git, and Jenkins.

As artificial intelligence (AI) takes the world by storm, the importance of MLOps has reached an unprecedented height. The engineering discipline, which offers a common platform for data scientists and operations professionals to collaborate and communicate, has been creating quite a buzz over the last couple of years.

MLOps combines a series of functions for businesses to leverage AI successfully as it not only takes machine learning models to production but even maintains them and keeps a thorough check. It delivers production goals by combining ML system development and deployment, data science, and data engineering. Essentially, MLOps production is machine learning.

With digital evolution gaining pace, an increasing number of businesses are discovering that the services of data scientists alone may not yield the desired results in production. MLOps Course essentially closes this gap between data science and production. Its helps boost the pace of model development, which then streamlines and smoothens the production process.

Objectives of MLOps Online Course

igmGuru’s MLOps training aims to train individuals to create machine learning models, deploy them in production and scale it for wider use. The training upskills individuals to use different tools and methodologies to produce machine learning effectively.

This MLOps course offers the much-needed exposure to MLOps essentials to help you acquire subject expertise, which goes a long way in ensuring a fruitful career in AI.

Who can pursue MLOps Online Training?

Since MLOps is a considered the junction of Data Science and DevOps, the course is a boon to individuals with insights of ML models, who now want to learn to create complete ML pipelines. Data scientists willing to hone their skills to leverage their talent in ML operations will also benefit highly from this course.

Benefits of MLOps Certification

In the recent past, data science has emerged as a high-paying career option with the demand for such professionals going through the roof. But as already mentioned, the need for production is data science and machine learning has shifted focus to MLOps experts. Not only the demand, but even the average pay scale of MLOps professionals isn’t too far behind that of data scientists.
Talent acquisition teams at all major businesses across the world are in search of professionals with the knowledge to put ML models into production. Considering that opportunities in the MLOps sector – unlike data science – are yet to hit the saturation point, the room for professional growth here is vast.
MLOps knowledge opens multiple doors for collaboration with data processing teams as well as for analytics professionals and IT engineers. The course also helps you fast-track model development and deployment aided by monitoring, validation, and management systems for ML models.

Skills you will grasp in the MLOps Training program

igmGuru’s MLOps online course helps individuals gather a wide range of expertise to boost their careers in ML. It lets them learn the tricks of the trade like how to move to the cloud, creating and managing ML pipelines, scaling, and dealing with sensitive data at scale, to name a few.

  1. The MLOps course will teach how to design an end-to-end machine learning system – from project scoping and data needs to modeling and deployment. It will further guide you to create pipelines to optimize the model training process.
  2. It will familiarize you with techniques to apply ML and deep learning algorithms to troubleshoot business problems. 
  3. The Azure MLOps course imparts training on how to productionize your machine learning effectively by using CI/CD pipelines, besides monitoring the performance of the system in place and deploying high precision models in any location.
  4. The training further gives access to TensorFlow, Keras, Linux, Git, Python, Docker, Kubernetes, Graffana, Prometheus, and Jenkins. 
  5. It will equip learners with skills for quick innovation through a sturdy machine learning lifecycle management. 
  6. The GCP MLOps course focuses on how to use automatic scaling, managed clusters of CPUs and GPUs with distributed learning in the cloud after deploying high precision models. 
  7. Learn how to pack the models easily to maintain high quality by using profiling and model validation. 
  8. This training module teaches how to keep track of model origin and version history, which comes handy in auditing. It also focuses on creating audit trails for regulatory requirements. 
  9. MLOps course enables you to run real-time analysis of various data concerning your business market and its impact on your company operations. 
  10. MLOps knowledge and expertise can play a critical role in addressing monitoring issues. It enables the marketing team to keep a track of logging and real-time decay monitoring. 

Additionally, this online MLOps course also covers specific tools and technologies used in MLOps such as Kubernetes, Docker, Git, and Jenkins.

MLOps Course Key Features

MLOps Training Modules

1. What is MLOps & MLOps Motivation
2. Solutions and Future Trends
3. MLOps Components
4. Different Roles involved in MLOps ( ML Engineering + Operations )
5. Machine Learning Life Cycle
6. MLOps Vs DevOps
7. Major Phases — what it takes to master MLOps
8. Different tools for MLOps
9. MLOps Maturity Model Levels
10. MLOps - Stages Of CI / CD

1. Why Linux? Linux types? How to access Linux env in different system
2. Free tier Amazon EC2 ubuntu instance
3. SSH and SSH tools & Putty
4. File zilla & WinSCP
5. Introduction to Shell , Bash Shell & Basic Linux Commands
6. Help for Command Line
7. Linux Core Concepts & Kernel and types
8. Linux file system, Boot Sequence, Run levels, File Types & Filesystem Hierarchy
9. Package Management Introduction and Configuration
10. Linux Type Based Package Manager
11. RPM and YUM
12. DPKG and APT
13. File Compression and Archival, Searching for Files and Patterns using grep/wildcards etc
14. VI, Nano Editor
15. Security and File Permissions, The Security Incident (story)
16. Linux Accounts, User Management, Access Control Files, Account Management
17. File Permissions and Ownership , Cron jobs
18. Service management with systemd, Working overtime (story)
19. Creating a Systemd Service, Systemd Tools
20. Lab - systemd services
21. Assignment | Assignment Solution 

1. What? Why? When? Type? Vendor? Pricing? Industry wise uses of GIT
2. Creation of Github / Gitlab / bitbucket account
3. Local GitHub UI installation, setup with VSCode and Pycharm
4. Local and Remote Repositories installation and configuration
5. GIT Repository initialisation
6. Commands: git log
7. Git Branches - What is branching in Git and why we need it?
8. Master/main branch and user-defined branch
9. Checkout and pushing to a branch, Merging of branches
10. Project control and management
11. In Remote Repositories, Initialisation of Remote Repositories
12. Pushing code to the remote repositories
13. Cloning of the remote repositories to local
14. PR (Pull Requests), Fetch and Pull
15. Handling conflict on merging branch, Forking of repository
16. Rebasing, Resetting and Reverting, Stashing
17. Assignment | Assignment Solution 

1. What is DVC, DVC Uses, Installation in Mac OS, Windows & Linux
2. Data Versioning, Model Versioning
3. Data Access, Model Access & Data Pipelines, Metrics, Parameters, Plots
4. Run, Queue, Compare, Persisting, and Sharing Experiments
5. Clean up, Versioning Data and Models, Sharing Data and Model Files
6. Data Registries, Shared Development Server & Project Structure
7. Setup Google Drive Remote, Large Dataset Optimisation
8. External Dependencies, Managing External Data
9. Automate Pipelines with DVC, Pipelines & Experiment Automation, Build automated pipelines
10. Experiments Management, Experimenting with reproducible pipelines, Common issues with ML experiments
11. Tracking metrics and plots & Compare experiment results, Build, Test & Deploy
12. Introduction to CI/CD in Machine Learning & Build CI/CD pipeline
13. Install GitLab Runner and Trigger CI/CD pipeline
14. Build Machine Learning pipeline, Build CI/CD pipeline, Trigger CI/CD pipeline
15. Making Continuous Integration work with ML, DVC Integration with Project
16. Build a model Prototype, Build a prototype with Jupyter Notebook
17. Start to version your code with Git, Version your code with Git
18. Create pipelines, Automate pipelines and data versioning with DVC
19. Create CI pipeline to build, test, experiment, Experimenting with DVC and CML & Deploy your model
20. Assignment | Assignment Solution 

1. What is DevOps, Why DevOps
2. Dev-Test-Deploy ,DevOps Principles,DevOps Toolchain
3. Overview of DevOps Tools
4. Co-relation between Agile and DevOps,Categories of DevOps Tools
5. Containers Concepts , Container Vs Virtual Machine
6. Installing docker on CentOS, Debian and Windows
7. Managing Container with Docker Commands
8. Building your own docker images & Docker Compose
9. Docker registry - Docker Hub , Networking inside single docker container
10. Lab - Running Python Web App in docker container
11. Lab - Create a docker image from git repo
12. Lab - Deploying flask app using docker-compose
13. Lab - Complex deployment using docker-compose
14. Lab - Creating your own docker registry 
15. Assignment | Assignment Solution 

1. Introduction to Kubernetes
2. Architecture and Kubernetes cluster installation
3. Raft Consensus Algorithm and Networking in Kubernetes
4. Raft Consensus Algorithm and Networking in Kubernetes
5. Installing Minikube and Objects in Kubernetes - Pod, Deployment
6. Services - Service Discovery, Service Object, Headless Services, Service Type
7. Role based Access
8. Volumes - Persistent Volumes, Persistent Volume Claim, Storage Class
9. Config Map and Secrets 
10. Ingress - Virtual Host, Types, Fanout, Virtual Host, Fanout Ingress configuration,
11. Virtual Host Ingress configuration
12. Lab - Installing Minikube on EC2
13. Lab - Enable and access Dashboard Addon
14. Lab - Deploy flask web app on Minikube
15. Lab - Deploy Nginx app on Minikube
16. Lab - Deploy application with host type volumes 
17. Assignment | Assignment Solution 

1. Introduction to Prometheus
2. Prometheus installation
3. Introduction to Grafana
4. Grafana Installation
5. Integration of Prometheus and Grafana
6. Adding customised dashboard in Grafana
7. Introduction to node exporter
8. Integrating node exporter for monitoring
9. Lab - Scrape metric from Grafana
10. Lab - View Node exporter metric in Grafana
11. Lab - View Docker metric in Grafana
12. Lab - Import AWS EC2 dashboard in Grafana 
13. Assignment | Assignment Solution 

1. Introduction to Jenkins
2. Continuous Integration & Continuous Integration with Jenkins
3. Jenkins Architecture
4. Installing Jenkins on EC2
5. User management
6. Set up Jenkins Master & Slave
7. Setup CI-CD pipeline for sample project
8. Lab - Setup Role based access
9. Lab - Master/Slave Setup
10. Lab - Configure SCM in Jenkins 
11. Assignment | Assignment Solution 

1. What is MLFLow & Installation
2. MLFlow Tracking, Where Runs Are Recorded, How Runs and Arti-facts are Recorded
3. Scenario 1: MLFlow on localhost
4. Scenario 2: MLFlow on localhost with SQLite
5. Scenario 3: MLFlow on localhost with Tracking Server
6. Scenario 4: MLFlow with remote Tracking Server, backend and arti-fact stores
7. Logging Data to Runs, Logging Functions, Launching Multiple Runs in One Program, Performance Tracking with Metrics
8. Visualising Metrics, Automatic Logging
9. Scikit-learn, TensorFlow and Keras, Gluon, XGBoost, Pytorch
10. MLFLow Tracker, Organising Runs in Experiments, Managing Experiments and Runs with the Tracking Service API, Tracking UI
11. Querying Runs Programmatically, MLFlow Tracking Servers, Storage,Networking
12. Logging to a Tracking Server, MLFlow Projects, Specifying Projects, Running Projects, Iterating Quickly, Building Multi Step Workflows
13. MLFLow Models, Storage Format, Model Signature And Input Example
14. Model API, Built-In Model Flavours, Model Customisation, Built-In Deployment Tools, Deployment to Custom Targets
15. Model Registry, Model Registry Workflows, UI Workflow, Registering a Model, Using the Model Registry, API Workflow
16. Adding an MLFlow Model to the Model Registry, Fetching an MLFlow Model from the Model Registry
17. Serving an MLFlow Model from Model Registry, Adding or Updating an MLFlow Model Descriptions, Renaming an MLFlow Model
18. Transitioning an MLFlow Model’s Stage, Listing and Searching MLFlow Models, Archiving an MLFlow Model, Deleting MLFlow Models
19. Assignment | Assignment Solution

1. Introduction to TFX
2. Data Ingestion using TFX & Data Validation using TFDV
3. Data Preprocessing using TFT
4. Model Training, Model Analysis & Model Evaluation using TFX
5. Model Deployment using TF Serving
6. Assignment Assignment Solution 

1. What is Kubeflow?
2. Core Kubeflow components
3. How to set up Kubeflow on Kubernetes
4. How to develop basic ML models in Kubeflow Notebooks
5. How to train and deploy models in Kubeflow
6. How to use Kubeflow Pipelines
7. How to use KFServing to deploy models
8. How to manage logs with Kubeflow Metadata component
9. Katib Hyper Parameter Tuning
10. Kubeflow Pipelines to KFServing
11. Assignment | Assignment Solution 

1. GitLab Triggers
2. AWS S3 storage
3. GitLab CI/CD Pipelines
4. Pipelines definition
5. MongoDB cloud Atlas
6. Heroku | Logdata | Coral for Monitoring
7. Assignment | Assignment Solution 

1. Amazon Sagemaker | Amazon S3 | AWS Codebuild | AWS Codecommit
2. Sagemaker Training Job | Sage Maker Endpoint | Amazon Api Gateway
3. Sagemake Model Monitoring | Cloudwatch Synthetics | Cloudwatch Alarm
4. Assignment | Assignment Solution 

1. Create an Azure Machine Learning workspace
2. Setup a new project in Azure DevOps
3. Import existing YAML pipeline to Azure DevOps
4. Declare variables for CI/CD pipeline
5. Create training compute
6. Train ML model | Register model
7. Deploy model in AKS
8. Assignment | Assignment Solution 

1. Deploy a Personalized Product Recommendation using MLOps
2. Deploy a Classification Model using MLOps on AWS
3. Deploy a Multiple Linear Regression Model using MLOps
4. Deploy a Gaussian Model in Time Series using MLOps on AWS
5. Deploy a Customer Churn Prediction using MLOps on Azure

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MLOps Online Course fee

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US $ 399.00
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  • Duration : 50 hrs
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US $ 799.00
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  • Duration : 50 Hrs
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Classes Starting From
  • Fast Track Batch 18 Jun 2024
  • Weekday Batch 24 Jun 2024
  • Weekend Batch 22 Jun 2024

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MLOps Certification Exam

How to become MLOps professional in 2024?

There are several organizations that offer certifications for MLOps professionals, including:


1. AWS Certified Machine Learning - Specialty: This certification from Amazon Web Services (AWS) focuses on the technical skills required to design, develop, and deploy machine learning models using AWS services.

2. Microsoft Certified: Azure AI Engineer Associate: This certification from Microsoft focuses on the technical skills required to design and implement solutions that use Azure Machine Learning.

3. Google Cloud Professional Machine Learning Engineer: This certification from Google Cloud focuses on the technical skills required to design, develop, and deploy machine learning models using Google Cloud services.

4. Cloudera Certified Professional Data Engineer for Machine Learning: This certification from Cloudera focuses on the technical skills required to design, develop, and deploy machine learning models using Cloudera's machine learning platform.

These certifications often require passing a multiple-choice exam and may have prerequisites such as a certain level of experience or passing a different certification. Be sure to check the specific requirements and format of the exam with the certifying organization.

While having an MLOps certification is not compulsory, it validates your proficiency in deploying and managing machine learning operations that further showcase expertise.
MLOps Certification Exam

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