Learn to Build an End-to-End Machine Learning Pipeline - Part 2

In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

START PROJECT

Project Template Outcomes

  • How to connect Python with Hopsworks and fetch data?
  • Understand the significance of train validation test data splitting
  • Implement one-hot encoding for categorical variables.
  • Distinguish between fit-transform and transform, storing for future use.
  • Implement normalization techniques in Python.
  • Understand the significance of experiment tracking.
  • How to connect with Weights and Biases for model experimentation?
  • Implement and Track Logistic regression, Random forest, and XGBoost models.
  • Explore model evaluation metrics and their business implications.
  • Utilize hyperparameter sweeps in Weights and Biases for tuning.
  • Learn to fetch the best model from Weights and Biases
  • Develop a Streamlit application and deploy it on AWS

Get started today

Request for free demo with us.

white grid

Architecture Diagrams

Unlimited 1:1 Live Interactive Sessions

  • number-icon
    60-minute live session

    Schedule 60-minute live interactive 1-to-1 video sessions with experts.

  • number-icon
    No extra charges

    Unlimited number of sessions with no extra charges. Yes, unlimited!

  • number-icon
    We match you to the right expert

    Give us 72 hours prior notice with a problem statement so we can match you to the right expert.

  • number-icon
    Schedule recurring sessions

    Schedule recurring sessions, once a week or bi-weekly, or monthly.

  • number-icon
    Pick your favorite expert

    If you find a favorite expert, schedule all future sessions with them.

  • number-icon
    Use the 1-to-1 sessions to
    • Troubleshoot your projects
    • Customize our templates to your use-case
    • Build a project portfolio
    • Brainstorm architecture design
    • Bring any project, even from outside ProjectPro
    • Mock interview practice
    • Career guidance
    • Resume review
squarebox svg

Customers sharing their love on online platforms

user review

Source: quora

user review

Source: quora

user review

Source: trustpilot

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: trustpilot

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: quora

user review

Source: quora

arrow left svg
arrow right svg

Benefits

250+ end-to-end project solutions

250+ end-to-end project solutions

Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.

15 new projects added every month

15 new projects added every month

New projects every month to help you stay updated in the latest tools and tactics.

500,000 lines of code

500,000 lines of code

Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.

600+ hours of videos

600+ hours of videos

Each project solves a real business problem from start to finish. These projects cover the domains of Data Science, Machine Learning, Data Engineering, Big Data and Cloud.

Cloud Lab Workspace

Cloud Lab Workspace

New projects every month to help you stay updated in the latest tools and tactics.

Unlimited 1:1 sessions

Unlimited 1:1 sessions

Each project comes with verified and tested solutions including code, queries, configuration files, and scripts. Download and reuse them.

Technical Support

Technical Support

Chat with our technical experts to solve any issues you face while building your projects.

7 Days risk-free trial

We offer an unconditional 7-day money-back guarantee. Use the product for 7 days and if you don't like it we will make a 100% full refund. No terms or conditions.

Payment Options

Payment Options

0% interest monthly payment schemes available for all countries.

listed companies

Testimonials

white grid

Comparison with other platforms

We provide ready-made project templates that solve real business problems, end-to-end and comes with solution code,
explanation videos, cloud lab environment and tech support.

End-to-end implementation
Real industry grade projects
by industry experts
Ready-made solutions to real
business problems
Detailed Explanations
kaggle
icon
Courses/ Tutorials
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon
icon

Our expert panel

world bg

Project Description

Overview

The project addresses a critical challenge faced by the logistics industry. Delayed truck shipments not only result in increased operational costs but also impact customer satisfaction. Timely delivery of goods is essential to meet customer expectations and maintain the competitiveness of logistics companies.

By accurately predicting truck delays, logistics companies can:

  • Improve operational efficiency by allocating resources more effectively
  • Enhance customer satisfaction by providing more reliable delivery schedules
  • Optimize route planning to reduce delays caused by traffic or adverse weather conditions
  • Reduce costs associated with delayed shipments, such as penalties or compensation to customers

 

In the first phase of our three-part series, Learn to Build an End-to-End Machine Learning Pipeline - Part 1, we laid the groundwork by utilizing PostgreSQL and MySQL in AWS RDS for data storage, setting up an AWS Sagemaker Notebook, performing data retrieval, conducting exploratory data analysis, and creating feature groups with Hopsworks. 

 

In Part 2, we delve deeper into the machine-learning pipeline. Focusing on data retrieval from the feature store, train-validation-test split, one-hot encoding, scaling numerical features, and leveraging Weights and Biases for model experimentation, we will build our pipeline for model building with logistic regression, random forest, and XGBoost models. Further, we explore hyperparameter tuning with sweeps, discuss grid and random search, and, ultimately, the deployment of a Streamlit application on AWS.

Note:  AWS Usage Charges
This project leverages the AWS cloud platform to build the end-to-end machine learning pipeline. While using AWS services, it's important to note that certain activities may incur charges. We recommend exploring the AWS Free Tier, which provides limited access to a wide range of AWS services for 12 months. Please refer to the AWS Free Tier page for detailed information, including eligible services and usage limitations.

 

Aim

The project aims to develop an end-to-end machine learning pipeline leveraging Hopsworks' feature store and model experimentation with Weights and Biases, ultimately deploying a Streamlit application on AWS.



Data Description 

The project involves the following data tables:

  • City Weather: Weather data for various cities
  • Routes: Information about truck routes, including origin, destination, distance, and travel time
  • Drivers: Details about truck drivers, including names and experience
  • Routes Weather: Weather conditions specific to each route
  • Trucks: Information about the trucks used in logistics operations
  • Traffic: Traffic-related data
  • Truck Schedule: Schedules and timing information for trucks

 

Tech Stack

  • Language: Python 3.10
  • Libraries: NumPy, Pandas
  • Data: Hopsworks Feature Store
  • Experiment Tracking: Weights and Biases
  • Model Building: Scikit-learn, XGBoost
  • Cloud Platform: AWS Sagemaker, AWS EC2

 

Approach

  • Data Retrieval from Hopsworks
    • Connecting Hopsworks with Python
    • Retrieving data directly from the feature store
  • Train-Validation-Test Split
  • One-Hot Encoding
  • Scaling Numerical Features
  • Model Experimentation and Tracking
    • Weights and Biases Introduction
    • Setting up a new project and connecting it to Python
  • Model Building
    • Logistic Regression
    • Random Forest
    • XGBoost
  • Hyperparameter Tuning with Sweeps
  • Streamlit Application Development and Fetching the Best Model
  • Deployment on AWS EC2 Instance

Latest Blogs

30+ Python Pandas Interview Questions and Answers

30+ Python Pandas Interview Questions and Answers

Prepare for Data Science interviews like a pro! Check out our blog with 30+ Python Pandas Interview questions and answers. | ProjectPro

Generative AI Application Landscape: All That You Need to Know

Generative AI Application Landscape: All That You Need to Know

Explore the Generative AI Application Landscape with industry expert Rajdeep Arora to explore insights on its evolution, challenges, and prospects | ProjectPro

Data Engineer’s Guide to 6 Essential Snowflake Data Types

Data Engineer’s Guide to 6 Essential Snowflake Data Types

From strings to timestamps, six key snowflake datatypes a data engineer must know for optimized analytics and storage | ProjectPro

View all blogs

We power Data Science & Data Engineering
projects at

projectpro i trusted leader projectpro i trusted leader projectpro i trusted leader

Join more than
115,000+ developers worldwide

Get a free demo