ML workflow involves a systematic and iterative process that encompasses various stages, from conceptualization to deployment. A well-structured ML workflow ensures efficiency and reproducibility in model development. Beginning with understanding the basics of machine learning models, a practitioner may follow a machine learning tutorial for beginners, gaining insights into the workflow of a machine learning project. This typically involves creating an ML workflow diagram, outlining the steps in a machine learning project, and delving into the intricacies of ML models explained. Mamtaj Narain, an expert in the field, emphasizes the importance of the data science/ML workflow, underscoring the need for meticulous attention at each stage. To operationalize and streamline the process, ML workflow tools and ML workflow orchestration tools become indispensable. These tools aid in the creation of ML pipelines, offering a systematic approach to developing, testing, and deploying machine learning models.

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Table of Contents (powered by https://videoken.com)
0:00:00 The Data Science / ML Workflow and Role of Product Managers
0:00:09 Introduction to the Speaker
0:01:18 Machine Learning Process and Role of Product Managers
0:01:30 About myself
0:02:13 Role of Product Management
0:05:15 Goal of Machine Learning
0:06:42 So what is AI Product Management?
0:07:49 It comes with its own set of challenges
0:12:57 Product Managers have become more relevant than ever before, to embed intelligence into the product
0:14:57 What fuels AI/ML
0:15:27 Selecting the right problem depends upon Data
0:17:37 Where is the most time spent in Data Science
0:19:06 The Machine Learning Anatomy
0:19:17 Anatomy of Machine Learning Problems
0:21:26 The OSEMN Data Science Pipeline
0:22:49 Data Life Cycle - Data Wrangling
0:23:54 The Supervised Machine Learning Process
0:24:02 Machine Learning Model Life Cycle
0:27:54 Define the Problem
0:29:53 Supervised Learning Training Data Set overview
0:33:57 Ready for validation - after training
0:34:14 Machine Learning Model Life Cycle
0:34:29 Data Wrangling and Data Import
0:38:54 Data preparation for testing and validation
0:42:43 Feature Selection
0:46:13 Train the model & Prediction
0:48:47 Evaluate the accuracy
0:50:45 Recap
0:52:48 Thank You
0:52:56 Q&A with Audience
1:06:23 Best question wins a Digital Learning Course
1:06:25 Institute of Product Leadership
1:16:03 Champion of Curiosity
1:18:03 Certificate of Appreciation
1:18:29 Wrapping Up