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TORRENT DETAILS
Learn To Build Machine Learning Systems - That Don't Suck
A live, interactive program that'll show you how to design, build, and deploy production-ready systems from scratch — without the fluff.
Note: I tried to upload NF with single parts but not working. So I upload course as 1 file
This program is for builders looking to solve real-world problems using AI/ML.
Most Machine Learning courses are boring, too academic, and never talk about how to ship actual products.
This program is different. This is a practical, no-nonsense, hands-on program that will teach you the skills you need for building production systems in weeks, not months.
You'll walk away from this program having designed, built, and deployed an end-to-end Machine Learning system, plus a proven playbook for selling, planning, and delivering world-class work backed by 30 years of real-world experience.
What Will You Learn?
This is a live, hands-on program that focuses on real-world Machine Learning.
This program is a world apart from any of those courses you've taken before
You'll join 20+ hours of live, interactive sessions where you'll learn how to build production-ready Machine Learning systems.
You'll discover best practices for building, evaluating, running, monitoring, and maintaining systems in production.
You'll get hands-on access and a complete walkthrough of an end-to-end Machine Learning system built entirely from scratch.
You'll learn how to build systems once and deploy them anywhere using state-of-the-art techniques and open-source tools.
You'll enjoy lifetime access to every future cohort and a private community where you can collaborate with thousands of students like you.
This program will completely change the way you think about Machine Learning. You'll ditch the typical classroom fluff in favor of practical strategies that actually work.
Day 1 - How To Start (Almost) Any Project
In this session, you'll learn how to pitch, sell, structure, and launch a new Machine Learning project. You'll find out how to frame complex problems in ways that set you up for successful solutions. Then, you'll cover how to run a discovery phase, address selection bias, manage data collection and labeling, and build an initial prototype.
Day 2 - How To Build A Model (That Works)
In this session, you'll explore data cleaning and feature engineering, and learn how to preprocess data using vectorization, normalization, and imputation. Next, you'll cover strategies for selecting the best model for your problem and discuss how to iteratively build an end-to-end training pipeline. Finally, you'll walk through distributed training so you can scale your models with data and model parallelism.
Day 3 - How To Ensure Models Aren't Lying to Us
In this session, you'll explore different evaluation strategies, such as cross-validation, LLM-as-a-judge, LLM juries, backtesting, invariance, and behavioral testing. Next, you'll see how to frame evaluation metrics in the context of business goals, ensuring your models work in real-world scenarios. Finally, you'll learn to prevent data leakages, perform error analysis, and handle imbalanced data.
Day 4 - How To Serve Model Predictions (In A Clever Way)
In this session, you'll explore how to version and deploy models while dealing with key trade-offs and operational considerations. Next, you'll examine different strategies for serving predictions, including human-in-the-loop and cost-sensitive workflows. Finally, you'll learn about pruning, quantization, knowledge distillation, and Low-Rank Adaptation (LoRA) to compress and optimize models for real-world applications.
Day 5 - How To Monitor A Model (Drift Is Awful)
In this session, you'll learn how to handle edge cases and outliers, address feedback loops, and detect and understand distribution shifts like covariate shift, label shift, and concept drift. Next, you'll see how to use adversarial validation and explore practical strategies for monitoring models in production. Finally, you'll explore different techniques to build resilient models that adapt to distribution shifts.
Day 6 - How To Build Continual Learning Systems
In this session, you'll learn how to automate the entire process of building, deploying, and maintaining a model in production to create systems that learn and improve over time. You'll explore incremental training techniques, how to avoid catastrophic forgetting and different methods for retraining your models. Finally, you'll see how to test models in production using A/B testing, canary releases, shadow deployments, and interleaving experiments.
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FILE LIST
Filename
Size
~Get Your Files Here !/001 - Lesson 1 - Getting Started.mp4
122.6 MB
~Get Your Files Here !/002 - Lesson 2 - Preparing Your Local Environment.mp4
128.3 MB
~Get Your Files Here !/003 - Lesson 3 - Introduction to Metaflow.mp4
132.3 MB
~Get Your Files Here !/004 - Lesson 4 - Training the Model.mp4
161.9 MB
~Get Your Files Here !/005 - Lesson 5 - The Training Pipeline.mp4
303.3 MB
~Get Your Files Here !/006 - Lesson 6 - Building a Custom Inference Process.mp4
187.6 MB
~Get Your Files Here !/007 - Lesson 7 - Deploying The Model.mp4
118.2 MB
~Get Your Files Here !/008 - Lesson 8 - The Endpoint Pipeline.mp4
170.3 MB
~Get Your Files Here !/009 - Lesson 9 - Monitoring The Model.mp4
85.2 MB
~Get Your Files Here !/010 - Lesson 10 - The Monitoring Pipeline.mp4
122.6 MB
~Get Your Files Here !/011 - Lesson 11 - Production Pipelines in Amazon Web Services.mp4
180.8 MB
~Get Your Files Here !/012 - Lesson 12 - Deploying the Model to SageMaker.mp4
107.6 MB
~Get Your Files Here !/013 - Lesson 13 - The Deployment Pipeline.mp4
116.8 MB
~Get Your Files Here !/014 - Lesson 14 - Monitoring the SageMaker Endpoint.mp4
46.2 MB
~Get Your Files Here !/015 - Lesson 15 - Running Pipelines Remotely.mp4
152.6 MB
~Get Your Files Here !/016 - Session 1 - Introduction and Initial Setup.mp4
322.9 MB
~Get Your Files Here !/017 - Session 2 - Exploratory Data Analysis.mp4
145.5 MB
~Get Your Files Here !/018 - Session 3 - Splitting and Transforming the Data.mp4
403.5 MB
~Get Your Files Here !/019 - Session 4 - Training the Model.mp4
281.5 MB
~Get Your Files Here !/020 - Session 5 - Custom Training Container.mp4
168 MB
~Get Your Files Here !/021 - Session 6 - Tuning the Model.mp4
121.5 MB
~Get Your Files Here !/022 - Session 7 - Evaluating the Model.mp4
156.1 MB
~Get Your Files Here !/023 - Session 8 - Registering the Model.mp4
77.4 MB
~Get Your Files Here !/024 - Session 9 - Conditional Registration.mp4
64.3 MB
~Get Your Files Here !/025 - Session 10 - Serving the Model.mp4
88.7 MB
~Get Your Files Here !/026 - Session 11 - Deploying the Model.mp4
86.8 MB
~Get Your Files Here !/027 - Session 12 - Deploying From the Pipeline.mp4
230.2 MB
~Get Your Files Here !/028 - Session 13 - Deploying From an Event.mp4
75.6 MB
~Get Your Files Here !/029 - Session 14 - Building an Inference Pipeline.mp4
159.4 MB
~Get Your Files Here !/030 - Session 15 - Custom Inference Script.mp4
114.3 MB
~Get Your Files Here !/031 - Session 16 - Data Quality Baseline.mp4
102.2 MB
~Get Your Files Here !/032 - Session 17 - Model Quality Baseline.mp4
102.8 MB
~Get Your Files Here !/033 - Session 18 - Data Monitoring.mp4
130 MB
~Get Your Files Here !/034 - Session 19 - Model Monitoring.mp4
73.2 MB
~Get Your Files Here !/035 - Session 20 - Shadow Deployments.mp4
65.7 MB
~Get Your Files Here !/Bonus Resources.txt
70 B
~Get Your Files Here !/github.txt
89 B
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (1).html
480.4 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (10).html
470.9 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (11).html
471.5 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (12).html
472 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (13).html
466.8 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (14).html
472.8 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (15).html
470.5 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (16).html
519.7 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (17).html
512.6 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (18).html
512.9 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (19).html
515.6 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (2).html
478.1 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (20).html
518.2 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (21).html
512.9 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (22).html
511.1 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (23).html
521.2 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (24).html
521.9 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (25).html
520.5 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (26).html
509 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (27).html
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~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (28).html
520.8 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (29).html
513.7 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (3).html
444.5 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (30).html
502.9 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (31).html
512.1 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (32).html
513.4 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (33).html
514.2 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (34).html
504.2 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (35).html
504.5 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (4).html
477.4 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (5).html
442.7 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (6).html
474.6 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (7).html
474 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (8).html
470.5 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck (9).html
472.7 KB
~Get Your Files Here !/HTML/Building Machine Learning Systems That Don't Suck.html
975.8 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/cohort.ipynb
463.3 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/container/Dockerfile
495 B
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/container/ml-dependencies.yml
154 B
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/basic-model.png
165.3 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/condition-step.png
222.9 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/culmen.jpeg
268.8 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/data-quality-baseline.png
92.7 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/deploy-step.png
225 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/deploying-flask.png
219.2 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/deploying-from-event.png
171.4 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/deploying-model.png
184.3 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/diagram.png
488.6 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/endpoint.png
156.9 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/evaluation-step.png
167.1 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/inference-pipeline.png
243.6 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/model-quality-baseline.png
231.6 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/penguins.png
2.8 MB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/processing-job.png
294.7 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/processing-step.png
185.9 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/registration-step.png
186.4 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/shadow-deployment.png
245.2 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/training-job.png
297 KB
~Get Your Files Here !/ml.school-cohort-16/backup/sagemaker/images/training-step.png
162.7 KB
~Get Your Files Here !/ml.school-cohort-16/LICENSE
11.1 KB
~Get Your Files Here !/ml.school-cohort-16/README.md
