Online & Classroom

DevOps For Data Science

Months Icon 3 Months
38 Modules
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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Key Features
Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

Key Features
24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

Key Features
Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

Key Features
Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why DevOps For Data Science ?

Why Data Science
Dramatically reduces the recovery time and chances of failure

The primary reason behind the failure of the teams is programming defects. With a limited development cycle, DevOps promotes regular code versions.

Why Data Science
Better cooperation and effective communication

It has been observed that DevOps helps in better cultural development. In an organization, culture doesn’t focus on personal goals; it focuses on overall performance.

Why Data Science
Higher Competencies

Speeding up equipment is the best way to boost efficiency. For instance, a cloud-based platform can boost the developer’s access to hardware. This, in turn, accelerates the testing and deployment process.

Who is This program for

  • Data Scientists,Machine Learning Engineers, Artificial Intelligence Engineers
  • Data Analysts and Functional Experts
  • Python developers who want to build real-world AI applications
  • Python beginners who want a comprehensive learning plan
  • Experienced programmers looking to use AI in their existing technology stacks
Who is this program


Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

Deployment of Machine Learning Models

  • Machine Learning Pipeline: Overview
  • Machine Learning Pipeline: Feature Engineering
  • Machine Learning Pipeline: Feature Selection
  • Machine Learning Pipeline: Model Building
  • Data Analysis
  • Feature Engineering
  • Feature Selection
  • Model Building
  • Getting Ready for Deployment
  • Develop a Machine Learning Pipeline for Classification
  • Machine Learning System Architecture and Why it Matters
  • Specific Challenges of Machine Learning Systems
  • Machine Learning System Approaches
  • Machine Learning System Component Breakdown
  • Building a Reproducible Machine Learning Pipeline
  • Challenges to Reproducibility
  • Architecture to Minimise Reproducibility Challenges
  • Production Code: Overview
  • Procedural Programming Pipeline
  • Procedural Programing: House Prices Demo
  • Assignment: Procedural Programming
  • Designing a Custom Pipeline
  • Custom Pipeline | Processing steps
  • Custom Pipeline| Fit and Transform
  • Executing the Custom Pipeline
  • Leveraging a Third Party Pipeline: Scikit-Learn
  • Shallow Dive into Scikit-learn API
  • Note on library versions
  • Third Party Pipeline
  • Scikit-Learn compatible Transformers
  • Executing the Deployment Pipeline
  • Third Party Pipeline: Closing Remarks
  • Production Code - Third Party Pipeline
  • Installing and Configuring Git
  • How to Use the Course Resources, Monorepos + Git Refresher
  • Our Github repository
  • Opening Pull Requests
  • Primer on Monorepos
  • Operating System Differences and Potential Gotchas
  • System Path and Pythonpath Demo
  • Requirements files Introduction
  • Virtualenv refresher
  • Text Editors / IDEs
  • Engineering and Python Best Practices
  • Quick Note About the Next 2 Lectures
  • Introduction to Pytest
  • Introduction to Tox
  • Training the Model
  • Connecting the Pipeline
  • Making Predictions with the Model
  • Data Validation in the Model Package
  • Feature Engineering in the Pipeline
  • Versioning and Logging
  • Building the Package
  • Creating the API Skeleton
  • Adding Config and Logging
  • Adding the Prediction Endpoint
  • Adding a Version Endpoint
  • API Schema Validation
  • Introduction to CI/CD
  • Setting up CircleCI
  • Setup Circle CI Config
  • Publishing the Model to Gemfury
  • Testing the CI Pipeline
  • Introduction
  • Setting up Differential Tests
  • Differential Tests in CI
  • Introduction
  • Heroku Account Creation
  • Heroku Gotchas
  • Heroku Config
  • Testing the Deployment Manually
  • Deploying to Heroku via CI
  • Introduction to Containers and Docker
  • Installing Docker
  • Creating Our API App Dockerfile
  • Building and Running the Docker Container
  • Releasing to Heroku with Docker
  • Introduction to AWS
  • AWS Costs and Caution
  • Container Orchestration Options: Kubernetes, ECS, Docker Swarm
  • Create an AWS Account
  • Setting Permissions with IAM
  • Installing the AWS CLI
  • Configuring the AWS CLI
  • Intro the Elastic Container Registry (ECR)
  • Uploading Images to the Elastic Container Registry (ECR)
  • Creating the ECS Cluster with Fargate Launch Method
  • Creating the ECS Cluster with the EC2 Launch Method
  • Updating the Cluster Containers
  • Tearing down the ECS Cluster
  • Deploying to ECS via the CI pipeline
  • Challenges of using Big Data in Machine Learning
  • Installing Keras
  • Download the data set
  • Introduction to a Large Dataset - Plant Seedlings Images
  • Building a CNN in the Research Environment
  • Production Code for a CNN Learning Pipeline
  • Reproducibility in Neural Networks
  • Setting the Seed for Keras
  • Seed for Neural Networks
  • Packaging the CNN
  • Adding the CNN to the API
  • Additional Considerations
Hours of Content
Case Study & Projects
Live Sessions
Coding Assignments
Capstone Projects to Choose From
Tools, Languages & Libraries

Languages and Tools covered

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Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Learn From Home

First-Ever Hybrid Learning System

Enjoy the flexibility of selecting online or offline classes with Osacad first-ever hybrid learning model.
Get the fruitful chance of choosing between the privilege of learning from home or the
advantage of one-on-one knowledge gaining - all in one place.

Learn From Home

Learn from Home

Why leave the comfort and safety of your home when you can learn the eminent non-technical courses right at your fingertips? Gig up to upskill yourself from home with Osacad online courses.

Learn From Home

Learn from Classroom

Exploit the high-tech face-to-face learning experience with esteemed professional educators at Osacad. Our well-equipped, safe, and secure classrooms are waiting to get you on board!

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Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.

All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

You can get started by enrolling yourself. The enrollment can be initiated from this website by clicking on "ENROLL NOW". If you are having questions or difficulties regarding this, you can talk to our counselors and they can help you with the same.

Once you enroll with us you will receive access to our Learning Center. All online classrooms, recordings, assignments, etc. can be accessed here.

Get in touch with us

What do you benefit from this programs
  • Better Operational execution
  • Increase in the flexibility of deployment
  • Effective Collaboration working
  • Cost-Effective Maintenance
  • Streamlined Development & Deployment process

I’m Interested

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