MLOps Pipelines Course
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25 modules
English
Certificate of completion
Access for 2 days
Build scalable MLOps with projects
Overview
What you'll learn
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Build scalable MLOps pipelines with Git, Docker, and CI/CD integration.
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Implement MLFlow and DVC for model versioning and experiment tracking.
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Deploy end-to-end ML models with AWS SageMaker and Huggingface.
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Automate ETL pipelines and ML workflows using Apache Airflow and Astro.
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Monitor ML systems using Grafana and PostgreSQL for real-time insights.
Key Highlights
Integrate Git for version control of machine learning models
Utilize Docker for containerizing ML applications
Implement CI/CD practices for automated pipeline building
Scale MLOps pipelines easily with efficient strategies
Ensure reliability and scalability in ML development
Streamline workflows and enhance collaboration
Automate testing and deployment processes
Optimize performance and efficiency of ML projects
What you will learn
Understanding MLOps Concepts
Learn the fundamentals of MLOps, including its importance and core principles.
Building Scalable Pipelines
Discover how to create scalable MLOps pipelines using Git, Docker, and CI/CD integration.
Implementing CI/CD Practices
Explore the implementation of Continuous Integration and Continuous Deployment practices for MLOps projects.
Leveraging Docker for ML Workflow
Learn how to utilize Docker containers to streamline machine learning workflows efficiently.
Modules
Disclaimer
1 attachment • 1 mins
Employee-Only Access Disclaimer
01. Introduction
1 attachment • 19.61 mins
01. Introduction
02. IDE's And Code Editors You Can Use
3 attachments • 26.45 mins
01. Getting Started With Google Colab
02. Getting Started With Github Codespace
03. Anaconda And VS Code Installation
03. Python Prerequisites
41 attachments • 11 hrs
01. Getting Started With VS Code And Environment
02. Python Basics-Syntax and Semantics
03. Variables In Python
04. Basics Data Types
05. Operators In Python
06. Conditional Statements In Python
07. Loops In Python
08. Practical Examples Of List
09. Sets In Python
10. Tuples In Python
11. Dictionaries In Python
12. Functions In Python
13. Python Function Examples
14. Lambda Functions In Python
15. Map functions In Python
16. Python Filter Function
17. Import Modules And Packages In Python
18. Standard Library Overview
19. File Operation In Python
20. Working With File Paths
21. Exception Handling In Python
22. OOPS In Python
23. Inheritance In Python
24. Polymorphism In Python
25. Encapsulation In Python
26. Abstraction In Python
27. Magic Methods In Python
28. Custom Exception In Python
29. Operator OverLoading In Python
30. Iterators In Python
31. Generators In Python
32. Decorators In Python
33. Working With Numpy In Python
34. Pandas DataFrame And Series
35. Data Manipulation And Analysis
36. Data Source Reading
37. Logging In Python
38. Logging With Multiple Loggers
39. Logging In a Real World Examples
Assets
Complete-Python-materials
04. Complete Flask Tutorial
8 attachments • 1 hrs
01. Introduction To Flask Framework
02. Understanding A Sample Flask Application
03. Integrating HTML With Flask Framework
04. HTTP Verbs Get And Post
05. Building Dynamic Url With Jinja 2
06. Put Delete And API's In Flask
Assets
flask
05. Git and Github
5 attachments • 51.24 mins
01. Getting Started With Git And Github
02. Part 2- Git Merge,Push, Checkout And Log With Commands
03. Part 3- Resolving Git Branch Merge Conflict
Assets
git-cheat-sheet-education
2 pages
06. Complete MLFLOW Tutorials
12 attachments • 1 hrs
01. Introduction To MLFLOW
2-Introduction-To-MLFLOW
2 pages
02. Getting Started With MLFLOW
03. Creating MLFLOW Environment
04. Getting Started With MLFLow Tracking Server
05. Deep Diving Into MLFlow Experiments
06. Getting Started With MLFlow ML Project
07. First ML Project With MLFLOW
08. Inferencing Model Artifacts With MLFlow Inferencing
09. MLFLOW Model Registry Tracking
Assets
mlflow
07. ML Project Integration With MLFLOW Tracking
4 attachments • 34.69 mins
01. Data Preparation House Price Prediction
02. Model Building And MLFLOW Tracking
Assets
mlflow
08. Deep Learning ANN Model Building Integration With MLFLOW
4 attachments • 47.54 mins
01. ANN With MLFLOW- Part 1
02. ANN with MLFLOW-Part 2
Assets
mlflow
09. Getting Started With DVC- Data Version Control
3 attachments • 24.46 mins
01. Introduction To DVC With Practical Implementation
Assets
DVCDEMO
10. Getting Started With Dagshub
7 attachments • 32.99 mins
Assets
01. Introduction To Dagshub Remote Repository
02. Creating First Remote Repo Using Dagshub
03. DVC With Dagshub Remote Repository
Assets
Dagshub-Repo
dvcdagshub
11. End To End Machine Learning Pipeline Using GIT, DVC,MLFLOW And DAGSHUB
8 attachments • 1 hrs
01. Getting Started With Project Structure
02. Implemeting Data Preprocessing Pipeline
03. Implementing Model Training Pipeline with MLFLOW Setup
04. MLFLOW Experiment Tracking In Dagshub
05. ML Evaluation Piepline With MLFLOW
06. Run The Complete Pipeline With DVC Stage And Repro
Asset
machinelearningpipeline
12. MLFLOW With AWS Cloud
8 attachments • 58.47 mins
01. Introduction To MLFLOW In AWS
02. MLFLOW Project Set Up With Installation
03. Implementing The End To End Project With MLFLOW
04. AWS Cloud EC2,IAM,S3 Bucket Set Up
05. AWS EC2 Instance- Setting MLFLOW Tracking Server
Assest
dsproject
AWSMLFLOW
1 page
13. Complete Basic To Advance Dockers
13 attachments • 1 hrs
01. Introduction To Docker Series
02. What are Dockers And Containers
03. Docker Images vs Containers
04. Dockers vs Virtual Machines
05. Dockers Installation
06. Creating A Docker Image
07. Docker Basic Commands
08. Push Docker Image To Docker Hub
09. Docker Compose
Assest
Hello-World
dockercopose
1 page
dockerss
4 pages
14. Getting Started With Airflow
10 attachments • 1 hrs
01. Introduction To Apache Airflow
02. Key Components Of Apache Airflow
03. Why Airflow For MLOPS
04. Setting Up Airflow With Astro
05. Building Your First DAG With Airflow
06. Designing Mathematical Calculation DAG With Airflow
07. Getting Started With TaskFlow API Using Apache Airflow
Asset
airflow-astro
finalairflow
2 pages
15. Airflow ETL Pipeline with Postgres and API Integration In ASTRO Cloud And AWS
11 attachments • 1 hrs
01. Introduction To ETL Pipeline
02. ETL Problem Statement And Project Structure Set Up
03. Defining ETL DAG With Implementing Steps
04. Step 1- Setting Up Postgres And Creating Table Task In Postgres
05. Step 2- NASA API Integration With Extract Pipeline
06. Step 3- Building Transformation And Load Pipeline
07. ETL Pipeline Final Implementation With AirFlow Connection Set Up
08. ETL Pipeline Deployment In Astro Cloud And AWS
Asset
etl
etlpipeline
2 pages
16. Introduction To Github Actions
6 attachments • 1 hrs
01. What is Github Action and CI CD Pipeline
02. What is Developers Workflow With Examples
03. Practicals-Automate Testing Workflow With Python
Asset
example
final-github-action
6 pages
17. End To End Github Action Workflow Project With Dockerhub
7 attachments • 42.16 mins
01. Github Action Workflow Project with Docker hub
02. Setting Project Structure With Github Repo
03. Setting Up Github Repository
04. Implementing Project With Flask And Dockers
05. Building the Yaml file for Dockers
Asset
DockerImage
18. Getting Started With Your First End To End Data Science Project With Deployment
12 attachments • 3 hrs
01. Project Structure, Github Repo And Environment Set Up
02. Custom Logging Implementation
03. Common Utilities Functions Implementation
04. Step By Step Building Data Ingestion Pipeline- Part 1
05. Data Ingestion Pipeline-Part 2
06. Complete Data Validation Pipeline Implementation
07. Complete Data Transformation Pipeline Implementation
08. Model Trainer Pipeline Implementation
09. Model Evaluation Pipeline Implementation
10. Training And Prediction Pipeline With Flask App
Asset
datascienceproject
19. End To End MLOPS Projects With ETL Pipelines- Building Network Security System
28 attachments • 7 hrs
01. Project Structure Set up With Environment
02. Github Repository Set Up With VS Code
03. Packaging the Project With Setup.py
04. Logging And Exception Handling Implementation
05. Introduction To ETL Pipelines
06. Setting Up MongoDb Atlas
07. ETL Pipeline Setup With Python
08. Data Ingestion Architecture
09. Implementing Data Ingestion Configuration
10. Implementing Data Ingestions Component
11. Implementing Data Validation-Part 1
12. Implementing Data Validation- Part 2
13. Data Transformation Architecture
14. Data Transformation Implementation
15. Model Trainer-Part 1
16. Model Trainer And Evaluation With Hyperparameter Tuning
17. Model Experiment Tracker With MLFlow
18. MLFLOW Experiment Tracking With Remote Respository Dagshub
19. Model Pusher Implementation
20. Model Training Pipeline Implementation
21. Batch Prediction Pipeline Implementation
22. Final Model And Artifacts Pusher To AWS S3 buckets
23. Building Docker Image And Github Actions
24. Github Action-Docker Image Push to AWS ECR Repo Implementation
25. Final Deployment To EC2 instance
Asset
finalnetworksecurity
6 pages
networksecurity
20. End To End DS Project Implementation With Mulitple AWS,Azure Deployment
15 attachments • 4 hrs
01. Github And Code Setup
02. Project structure Logging And Exception
03. Project Problem Statement EDA And Model Training
04. Data Ingestion Implementation
05. Data Transformation Implementation
06. Model Trainer Implementation
07. Hyperparameter Tuning Implementation
08. Building Prediction Pipeline
09. Deployment AWS Beanstalk
10. Deployment In EC2 Instance
11. Deployment In Azure Web App
Asset
AWS-CI-CD-Projects-main-1
mlproject-main
Student-Performance-Azure-deployment-main-1
21. End To End NLP Project With HuggingFace And Transformers
12 attachments • 2 hrs
01. Introduction To Huggingface And Problem Statement
02. Github Repo And Project Structure Set up
03. Logging And Utils Common Functionalities
04. Finetuning HuggingFace Models In Google Colab
05. Data Ingestion Implementation- Part 1
06. Data Ingestion Implementation- Part 2
07. Data Transformation Implementation
08. Model Trainer Implementation
09. Model Evaluation Implementation
10. Prediction Pipeline And API Integration
Asset
textsummarizer
22. Build, Train ,Deploy And Create Endpoints For ML Project Using AWS Sagemaker
7 attachments
01. Introduction To AWS Sagemaker Amd Project Set up
02. EDA,AWS IAM, S3 Set up With Data Ingestion
03. Implementing Training Script For AWS Sagemaker
04. Training With An On Spot Instance In AWS Sagemaker
05. Deployment Of Endpoint With AWS Sagemaker And Inferencing
Asset
awssagemaker
23. Grafana-Open Source Tool For Data Visualization And Monitoring
7 attachments • 1 mins
01. Introduction To Grafana Open Source Tool
02. Grafana Cloud Set Up And Problem Statement
03. Visualization Implementation With Grafana Cloud And Postgresql In AWS
Assets
finalgrafana
1 page
Queries
grafana
24. Generative AI Series With AWS LLMOPS
6 attachments
01. LifeCycle Of Gen AI Projects In Cloud
02. Blog Generation Generative AI App Using AWS Lambda And Bedrock
03. Deployment Of HuggingFace LLM Model In AWS Sagemaker
04. End To End GENAI App Using NVIDIA NIM
Assets
Generative-AI-With-Cloud-main
Certification
When you complete this course you receive a ‘Certificate of Completion’ signed and addressed personally by me.
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Absolutely! we are committed to providing an engaging and interactive learning experience. You will have opportunities to interact with them through our community. Take full advantage to enhance your understanding and gain insights directly from the expert.
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