ProductizeML
  • ProductizeML
  • Introduction
    • Objectives
    • About the Course
    • Guidelines
    • Syllabus
    • After Completion
  • Machine Learning
    • Why ML, and why now
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • ML Terminology
  • Data Management
    • Data Access
    • Data Collection
    • Data Curation
  • Train and Evaluate
    • Framework and Hardware
    • Training Neural Networks
    • Model Evaluation
  • Productize It
    • ML Life Cycle
    • Business Objectives
    • Data Preparation
    • Model Development
    • Train, Evaluate, and Deploy
    • A/B Testing
    • KPI Evaluation
    • PM Terminology
  • Resources
    • Readings
    • Courses
    • Videos
  • Hands-On
    • Python for Machine Learning
      • Python Installation
        • MacOS
        • Linux
Powered by GitBook
On this page
  • Let your product be smart
  • Productizing ML solutions
  • Measuring Business Impact
  • Challenges to solve

Was this helpful?

  1. Introduction

Objectives

PreviousProductizeMLNextAbout the Course

Last updated 2 years ago

Was this helpful?

Let your product be smart

If you are reading this, you are probably interested in or thinking about building a product that uses some form of Artificial Intelligence or Machine Learning. Maybe you want to make predictions from the data you are generating using a trained ML model, or maybe you want to automate existing repetitive and time-consuming tasks with a high-performing AI solution. You might also be thinking about how you can ship these solutions as new products or features for your users, so you can provide them value and collect real-world data, and feed it back to your product, so it can become better over time.

Productizing ML solutions

Measuring Business Impact

The course that you are about to start will provide you with guidelines and best practices for the productization of Machine Learning solutions and how businesses can benefit from incorporating AI into their current pipelines.

This is, in fact, a really important point this course focuses on how to measure the business impact and success of your product not only from the technology point of view but also how it is presented to users and these benefits from it.

Challenges to solve

Throughout the course, we will learn that once a solution is moved into production, there are some concerns we might face related to data dependencies and changes in the external world, model complexity, reproducibility, testing, monitoring, model deployment, and quick delivery of new models. At the end of this self-study course, you will feel confident on how to build a solution that prevents these setbacks to happen, as well as the best way to tackle these.

In recent years, large consumer and data-driven companies such as Netflix, Facebook, and Google have already deployed ML at scale in their production pipeline as , targeted ads, and optimized search results. These solutions were enabled by leveraging their massive amount of data and big data tools, but even without these key pieces, any company should be able to ship their first AI product.

recommendation systems
We should see AI as part of the current innovation strategies and tools we have been offered.