How to Build Building Machine Learning Powered Applications Step by Step

Download Building Machine Learning Power Application Free in PDF. This practical guide is very helpful and useful in this notes you will learn how to design, develop and maintain a powerful machine learning applications. By this practical guide you’ll also learn how to design or develop a good product.

This notes is helpful for researchers , engineers and anyone who wants to learn machine learning deeply. You’ll learn in this notes how to establish a powerful machine learning application. In this notes there is given some examples for more practice.

Tutorial	How to Build Building Machine Learning Powered Applications step by step
Format	PDF
Language	English

By this Notes we learn about these topic’s in detail:-

 From Product Goal to ML Framing

• Estimate What Is Possible
• Models
• Data
• Framing the ML Editor
• Trying to Do It All with ML: An End-to-End Framework
• The Simplest Approach: Being the Algorithm
• Middle Ground: Learning from Our Experience
• Monica Rogati : How to Choose and Prioritize ML Projects
• Conclusion

Create a Plan

• Measuring Success
• Business Performance
• Model Performance
• Freshness and Distribution Shift
• Speed
• Estimate Scope and Challenges
• Leverage Domain Expertise
• Stand on the Shoulders of Giants
• ML Editor Planning
• Initial Plan for an Editor
• Always Start with a Simple Model
• To Make Regular Progress: Start Simple
• Start with a Simple Pipeline
• Pipeline for the ML Editor
• Conclusion

Build a Working Pipeline

• Build Your First End-to-End Pipeline
• The Simplest Scaffolding
• Prototype of an ML Editor
• Parse and Clean Data Tokenizing Text
• Generating Feature
• Test Your Workflow
• User Experience
• Modeling Results
• ML Editor Prototype Evaluation
• Model
• User Experience
• Conclusion

Acquire an Initial Dataset

• Iterate on Datasets
• Do Data Science
• Explore Your First Dataset
• Be Efficient, Start Small
• Insights Versus Products
• A Data Quality Rubric
• Label to Find Data Trends
• Summary Statistics
• Explore and Label Efficiently
• Be the Algorithm
• Data Trends
• Let Data Inform Features and Models
• Build Features Out of Patterns
• ML Editor Features
• Robert Munro: How Do You Find, Label, and Leverage Data?
• Conclusion

Iterate on Models

• Train and Evaluate Your Model
• The Simplest Appropriate Model
• Simple Models
• From Patterns to Models
• Split Your Dataset
• ML Editor Data Split
• Judge Performance
• Evaluate Your Model: Look Beyond Accuracy
• Contrast Data and Predictions
• Confusion Matrix
• ROC Curve
• Calibration Curve
• Dimensionality Reduction for Errors
• The Top-k Method
• Other Models
• Evaluate Feature Importance
• Directly from a Classifier
• Black-Box Explainers
• Conclusion

Debug Your ML Problems

• Software Best Practices ML-Specific Best Practices
• Debug Wiring: Visualizing and Testing
• Start with One Example
• Test Your ML Code
• Debug Training: Make Your Model Learn
• Task Difficulty
• Optimization Problems
• Debug Generalization: Make Your Model Useful
• Data Leakage
• Overfitting
• Consider the Task at Hand
• Conclusion

Using Classifers for Writing Recommendations

• Extracting Recommendations from Models
• What Can We Achieve Without a Model?
• Extracting Global Feature Importance
• Using a Model’s Score
• Extracting Local Feature Importance
• Comparing Models
• Version 1: The Report Card
• Version 2: More Powerful, More Unclear
• Version 3: Understandable Recommendations
• Generating Editing Recommendations
• Conclusion

Considerations When Deploying Models

• Data Concerns
• Data Ownership
• Data Bias
• Systemic Bias
• Modeling Concerns
• Feedback Loops
• Inclusive Model Performance
• Considering Context
• Adversaries
• Abuse Concerns and Dual-Use
• Chris Harland: Shipping Experiments
• Conclusion

Choose Your Deployment Option

• Server-Side Deployment
• Streaming Application or API
• Batch Predictions
• Client-Side Deployment
• On Device
• Browser Side
• Federated Learning: A Hybrid Approach
• Conclusion

Build Safeguards for Models

• Engineer Around Failures
• Input and Output Checks
• Model Failure Fallbacks
• Engineer for Performance
• Scale to Multiple Users
• Model and Data Life Cycle Management
• Data Processing and DAGs
• Ask for Feedback
• Chris Moody: Empowering Data Scientists to Deploy Models
• Conclusion

Monitor and Update Models

• Monitoring Saves Lives
• Monitoring to Inform Refresh Rate
• Monitor to Detect Abuse
• Choose What to Monitor
• Performance Metrics
• Business Metrics
• CI/CD for ML
• A/B Testing and Experimentation
• Other Approaches
• Conclusion

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