Ml.School - Learn to Build Machine Learning Systems That Don't Suck [Update 04/2025]
English | Size: 5 GB
Genre: eLearning[/center]

What You'll Learn
This program focuses on real-world AI and Machine Learning engineering skills.
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 AI/ML 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 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 Artificial Intelligence and Machine Learning. You'll ditch the typical classroom fluff in favor of practical strategies that actually work.

Session 1 - How To Start (Almost) Any Project
How to start every project with a discovery phase and use a simple 8-question checklist to frame complex problems in ways that make useful solutions inevitable.
Understanding the first rule of machine learning and how to use simple rules to build prototypes to validate assumptions and gather feedback.
Determining the value of collecting additional data and understanding what a good dataset looks like.
Converting data into numerical vectors with label encoding, one-hot encoding, target encoding, and tokenization.
Engineering predictive features from raw data using feature engineering techniques and implementing strategies for handling missing values.
Designing a labeling strategy and using Active Learning, uncertainty, and diversity sampling to automate labeling data at scale.
Session 2 - How To Build Better Software (That Works)
Building Retrieval-Augmented Generation (RAG) systems to enhance language models with external, up-to-date knowledge.
Understanding the tradeoffs between term-based retrieval and embedding-based semantic retrieval.
Developing model-selection strategies by weighing performance, latency, and cost.
Implementing an initial evaluation protocol by establishing a strong baseline, and using holdout sets, cross-validation, prompt engineering, and scoring rubrics.
Implementing model versioning and tracing to keep experiments and data reproducible.
Understanding model-centric and data-centric AI and how to use them to build and improve your models.
Session 3 - How To Build Software You Can Trust
Implementing input and output guardrails to block harmful content and fix responses before users see them.
Performing error analysis to find and fix the most critical failures of your application.
Using an LLM-as-a-judge to automate the evaluation of generative models based on custom, nuanced criteria.
Using backtesting to evaluate models on historical data, and implementing Invariance and Behavioral Testing to verify a model's consistency and behavior in critical edge cases.
Ensuring data quality and integrity by preventing data leakages and handling class imbalance using techniques like resampling, threshold moving, and cost-sensitive learning.
Session 4 - How To Serve Model Predictions (In A Clever Way)
Understanding model deployment strategies and the trade-offs between static, dynamic, and hybrid serving.
Using a model gateway to route requests, manage costs, and decouple your application from the underlying models.
Implementing human-in-the-loop and cost-sensitive workflows to combine machine predictions with human expertise.
Making models faster and more efficient using compression techniques like pruning, quantization, knowledge distillation, and Low-Rank Adaptation (LoRA).
Reducing latency and cost by implementing caching strategies and understanding the difference between exact and semantic caching.
Session 5 - How To Monitor Your Models (Drift Is Awful)
Detecting and understanding distribution shifts by learning to differentiate between covariate shift, label shift, and concept drift.
Identifying and mitigating the impact of edge cases and feedback loops that can silently degrade your model's performance over time.
Implementing a robust production monitoring strategy to track model inputs, operational metrics, prediction distributions, and user feedback.
Understanding the three pillars of observability-metrics, logs, and traces-to move beyond observability into debugging of production applications.
Implementing different strategies for testing in production, including A/B testing, canary releases, shadow deployments, and interleaving experiments.
Session 6 - How To Build Continual Learning And Agentic Systems
Understanding different retraining strategies by comparing stateless training with stateful training.
Learning how to prevent catastrophic forgetting, which is the tendency of models to forget previously acquired knowledge.
Understanding the difference between simple, reliable agentic workflows and complex, autonomous agents with planning, memory, and tools.
Using the Model Context Protocol (MCP) to standardize how agents interact with external tools and data, simplifying complex integrations.
Leveraging the Agent-to-Agent (A2A) protocol to enable multiple agents to discover each other, delegate tasks, and collaborate to solve complex problems.
Code walkthroughs
You'll get access to an end-to-end, production-ready template system for training, evaluating, deploying, and monitoring a system.

The codebase comes with extensive documentation to help you understand how the code works and how you could change it to accommodate your needs.

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