Productization of ML models- Instructor Led Course

  • Admin bar avatar
    Anshu Pandey
  • 267 (Registered)
  • (0 Reviews)

Productization of ML models- Instructor Led Course

This course is part of multiple programs

This course can be applied to multiple Specializations or Professional programs. Completing this course will count towards your learning in any of the following programs


A learner must have completed Data Analytics with Python Course and Data Science and Machine Learning Course 


Course Curriculum

Module 1: life cycle of ml/ai project – 2 Hours

  • A broad overview of terms and technology related to AI
  • AI v/s Machine Learning v/s Deep Learning v/s Data Science
  • Who should be involved in an AI project?
  • Examining team culture, capabilities and readiness
  • AI v/s Non AI
  • How to decide when to use the capabilities of AI for business?
  • Feasibility and Profitability Analysis
  • Identifying the right data components
  • Collecting data from outside the room
  • Problem Framing
  • Deciding the correct validation metric, optimizer
  • Data Science and Software Engineering
  • Collecting and munging Data
  • Experimenting with data, features and Algorithms
  • Testing and Validating models
  • Version Control
  • How to handle Overfitting and Underfitting?
  • Size of Data and its impact in AI/ML Project lifecycle


Module 2: Production deployment strategies – 8 Hours

  • AI and Data Science end to end project lifecycle
  • Data Science Deployment strategy
  • Deployment best practices
  • Deployment with Flask
    • Creating a sample web app with flask
    • Creating Request response interface
    • Integrating with a ML model
    • Building the frontend interface via JS and integrating with Flask webserver


  • Best Practices for API design for ML services
  • Deploying model with nginx and uWSGI – demo
  • Machine Learning and DevOps
  • Defining scalability
  • Tools and techniques for scalable machine learning
  • Architecture design patterns for scalable systems
  • Machine learning models as services
  • Containerizing models
    • Building Docker images with dockerfiles
    • Example Docker Build Process
    • Using Docker registries to manage images
  • Best Practices of scaling machine learning models
  • Finish off experiments
  • Review of what has been taught
  • Get individuals to create their own action list
  • Final Q&A.


Course Features

  • More than 80% hands-on session
  • Project-oriented learning
  • Cloud-based LMS
  • Experienced Trainer
  • Masterclass for Expert
  • Standard reading materials
  • Study resources
  • Graded Quizzes
  • Real-time case study-based projects
  • Discussion Forum


Machine Requirement

  1. Windows Machine (Windows 7 or Above) /Linux Machine
  2. Only 64 Bit
  3. 8 GB RAM 
  4. NVIDIA Graphics Card (Recommended)



Admin bar avatar
A Technical expert and a passionate trainer has expertise in the field of Artificial Intelligence, Machine Learning and IoT, he has a proven work record of delivering more than 100+ workshops and Technical Training in various technologies and domains at the top MNCs, premier organizations including IITs, NITs and other premier educational organizations. He has delivered 50+ corporate Training to clients from India and abroad. Skills- Programming Languages: Python, Arduino, R, MATLAB Scripting Languages: HTML, JS, CSS AI Skills: Machine Learning – Linear & Logistic Regression, Clustering, Artificial Neural Networks, SVM, Genetic Algorithm, Fuzzy Logic, Natural Language Processing, PCA, CNN, LSTM AI, ML Tools & Platforms: Python, Tensorflow, Keras, Azure ML, AWS, Apache Spark Hardware: Arduino, Raspberry Pi, AVR, Intel Edison, Xbee, LoRaWAN IoT Platforms: AWS IoT, Thingworx, ThingsBoard, IBM Watson IoT, ThingSpeak, myDevices, Twilio, node-red, Ubidots, PubNub, IoT Gateway


0 rating
5 stars
4 stars
3 stars
2 stars
1 star
× Chat Now !