Machine Learning Deployment

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Course Fee

₹ 10,000 ₹ 6,000

Course Insides

Level of Course

Beginners to Intermediate


1.5 Months

Classes Mode



Weekend(Sat & Sun)

Live Project

1 Live Projects


12 Exercises

About the course

Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome. Often, an organization’s IT systems are incompatible with traditional model-building languages, forcing data scientists and programmers to spend valuable time and brainpower rewriting them.

Who should attend ?

  • This course is meant for people with at least some programming experience
  • Software Engineer
  • Data Scientist
  • Machine Learning Engineer


  • The course is absolutely practical and real-time based on theory material provided in advance.
  • The sessions are interactive and interesting
  • All the queries are answered along with guidance on certification
  • Data Science is greatly in demand. Prospective job seekers have numerous opportunities. It is the fastest growing job on LinkedIn and is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.
  • Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.
  • Data Science has helped various industries to automate redundant tasks. Companies are using historical data to train machines in order to perform repetitive tasks. This has simplified the arduous jobs undertaken by humans before.


  • Course Curriculum Overview
  • Knowledge requirements
  • How to approach this course
  • Machine Learning pipeline overview
  • Data Gathering
  • Feature engineering
  • Feature selection
  • Machine learning model building
  • Model assessment
  • Machine Learning System Architecture
  • What is is and why it is important
  • Challenges of creating a suitable system architecture
  • Different architecture approaches
  • Architecture components
  • Building Reproducible Machine Learning Pipelines
  • How to transform your jupyter notebooks into production ready code
  • Different ways utilised in the industry
  • Procedural programming
  • OOP with a custom pipeline
  • OOP with third party pipeline
  • Pros and cons of each method
  • Our recommendation
  • Git refresher
  • System path and pythonpath refresher
  • Virtual environments
  • How to use the course resources
  • Train the model and make predictions
  • Ensuring data format -data validation
  • Versioning and Logging
  • Building a python package with the model
  • Introduction to Flask
  • Creating the API skeleton
  • Versioning and Logging
  • Schema validation
  • Introduction to CI/CD
  • CircleCI
  • Publishing the model to Gemfury
  • Testing the CI pipeline
  • Setting up differential tests
  • Differential testing in CI
  • What is a PaaS?
  • Utilising Heroku
  • Testing the deployment manually
  • Deployment to Heroku using CI
  • Intro to containers and Docker
  • Installing docker
  • Creating an API App Dockerfile
  • Building and Running a Docker container
  • Releasing to Heroku utilising Docker
  • Intro to AWS
  • Creating an AWS account
  • Installing and Configuring the AWS CLI
  • Elastic Container Registry
  • Elastic container Service
  • Deploying to ECS using CI
  • Classify images with CNN
  • Challenges of deploying models using big data
  • Updating applications to big data
  • AWS S3 for large datasets
Curriculum is empty


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₹ 10,000 ₹ 6,000