Chip ML design

ch1 Overview

when to use ML (1) lean (2) complex patterns with (3) existing data to (4) make predictions (5) on unseen data

  • repetitive: each pattern is repeated and machine learnable
  • cost of prediction is cheap
  • at scale to acquire benefit
  • the pattern are constantly changing.

ML in Research vs. in Production

  • different stakeholder
  • fast inference, low latency (more swe)
  • data constantly shifting
  • fairness
  • interpretablity

ML algo is smaller part, ML leaderboard batch prediction latency is important ML vs SWE, versioning data, 怎么评估?

ch2 ML Basic

Business and ML objectives 要综合考虑

  • solve problem faster, spend less money on you
  • magically: yes, but not overnight

ML system:

  • relibility, fail silently
  • scalability, peak on prime day
  • maintainability, DevOps, SRE
  • adaptability, data distribution shift

Framing ML problem classification: binary: is cat? multiclass: cat or dog, cardinality种类 multilabel: cat and dog

Objective functions, Mind vs Data

ch3 Data Engineering:

Row/column parquet (pandas)

OLTP vs OLAP insert vs aggregate Dataflow not db, services but real-time transport

real-time transport (kakfa, kinesis): pub/sub (topic) vs message queue

computation on straming data: flink it is stateful, only compute the delta new data and then join with old data.

ch4 training data

How to handle data from BA’s view?

sampling logic simple random sampling stratifield sampling, define the group weighted sampling reservoir sampling importance sampling

labeled hand vs natural

weak supervision: heuristics transfer learning active learning: label the sample the least concern

class imbalance

insufficient signal to learn to detect the minority classes use the right evaluation metrics. ROC curve data level method: resampling: x oversampling: SMOTE algorithem-level method:

data augmentation

ch5 feature engineering:

the jobs is to come up with new useful features. problem? data leakage and how to detect and avoid it.

deep learning => feature learning, extracted automatically The process of choosing what information to use and how to extract this information into a format usable by your ML models is feature engineering or domain-specific tasks such as predicting whether a transaction is fraudulent, you might need subject matter expertise with banking and frauds to be able to come up with useful features.

Common Feature Engineering Operations

missing data:

missing not at random: the data is natually missing value missing ar random

deletion for missing values or fill in (imputation) column deletion remove important information that your model needs to make predictions, especially if the missing values are not at random (MNAR). row deletion create biases in your model avoid filling missing values with possible values


Before inputting features into models, it’s important to scale them to be similar ranges ML models tend to struggle with features that follow a skewed distribution

Note: 1. it’s a common source of data leakage. 2. often requires global statistics from training for inference


problem is category boundaries

Encoding Categorical Features

categories change, not static. decide the bucket is hard. hashing trick: The hashed value will become the index of that category, Because you can specify the hash space, you can fix the number of encoded values for a feature in advance, without having to know how many categories there will be One problem with hashed functions is collision

Feature Crossing

This technique is useful to model the nonlinear relationships between features con: it can make your feature space blow up and overfitting 直接上AutoML

Discrete and Continuous Positional Embeddings

“Attention Is All You Need” words’ positions need to be explicitly inputted in parallel: (“a dog bites a child” is very different from “a child bites a dog”) neural networks don’t work well with inputs that aren’t unit-variance (that’s why we scale our features A way to handle position embeddings is to treat it the way we’d treat word embedding,

Data Leakage

models were “found to be picking up on the text font that certain hospitals used a phenomenon when a form of the label “leaks” into the set of features used for making predictions, and this same information is not available during inference.

cause: Splitting time-correlated data randomly instead of by time

Detecting Data Leakage

Do ablation studies,if removing a feature causes the model’s performance to deteriorate significantly, investigate why that feature is so important. subject matter expertise Be very careful every time you look at the test split (不能漏题)

Engineering Good Features

Having too many features can be bad both during training and serving In theory, if a feature doesn’t help a model make good predictions, regularization techniques like L1 regularization should reduce that feature’s weight to 0。 or removed, prioritizing good features.

Feature Importance

XGBoost => importance of your features SHapley Additive exPlanations, is great because it not only measures a feature’s importance to an entire model, it also measures each feature’s contribution to a model’s specific prediction

generalization to unseen data

Measuring feature generalization is a lot less scientific than measuring feature importance, and it requires both intuition and subject matter expertise on top of statistical knowledge.


  • Split data by time into train/valid/test splits instead of doing it randomly.
  • If you oversample your data, do it after splitting.
  • Scale and normalize your data after splitting to avoid data leakage.
  • Use statistics from only the train split, instead of the entire data, to scale your features and handle missing values.
  • Understand how your data is generated, collected, and processed. Involve domain experts if possible.
  • Keep track of your data’s lineage.
  • Understand feature importance to your model.
  • Use features that generalize well.
  • Remove no longer useful features from your models.

ch6 model development

When selecting a model for your problem, you don’t choose from every possible model out there, but usually focus on a set of models suitable for your problem.

detect fraudulent transactions, you know that this is the classic abnormality detection problem—fraudulent transactions are abnormalities that you want to detect—and common algorithms for this problem are many, including k-nearest neighbors, isolation forest, clustering, and neural networks.


avoid state of the art and start with the simplest aviud human bias in selecting models evaluate good performace now vs. later evaluate trade off, false positives and false negatives trade-off understand model’s assumption.


bagging: boostrapping, aggregating boosting: enhance the signal stacking: meta-learning

distributed taining

data parallelism: model paralleisam.

Experiment Tracking and Versioning

An artifact is a file generated during an experiment—examples of artifacts can be files that show the loss curve, evaluation loss graph, logs, or intermediate results of a model throughout a training process. data is often much larger than code, we can’t use the same strategy that people usually use to version code to version data.

Distributed Training

data parallelism vs Pipeline parallelism


Hyperparameter tuning Hard AutoML: Architecture search and learned optimizer


permutation test vs noise: 主动加噪音 invariance test vs certain change won’t change output directional expection test: some change should cause predictable change in output

vs baseline. 去production找

ch7 prediction servie

online prediction: faud detection, time sensitive. batch prediction: recommender system

model compression:

low rank facorization: convolutional filter knowledge disillation: smaller representation pruning: remove low signal quantization: down-size long -> int

ch 8 monitoring: data distribution shifts …

software system failure: deps/deploy/hw failure ML specific failure: production data diff, edges case, degenerate feedback loop

data distribution shift

sovariate shift: p(x) change but P(Y|X) remain the same, age goes up. label shift: p(y) change but p(x|y) remain the same. concept shift: P(Y|X) change but P(x) remain the same

detect distribution shift

statistical method or time scale window to detect the shift.

address distribution shift

use massive dataset adapt a trained model to target distribution without requiring new label retrain the model (most common)

ML sepecific metrics

raw input, features, prediction, accuracy-related metrics

ch 9

continual learning and testing in production

continual learning to update ML model, it is mostly infra problem. stateful training.

continual learning challenges

fresh data access challenge evaluation challenge algorithem challenge

### 学习机器学习的方法



作者:王庆东 链接: 来源:知乎 著作权归作者所有。商业转载请联系作者获得授权,非商业转载请注明出处。

feature engineering.

  1. access: access to feature info, transparency, lineage
  2. serving: availability in production at high throughput and low lantency. the user don’t need to sql retrieval from data warehouse. integration with offline storage(s3) with online storage(redis). real-time feature transformation.
  3. integrity: minimize train-serve skew, point-in-time correct data. 以确保历史特征和标签被用于训练和评估时不存在 data leaks
  4. convenience: easy quick to use. interactivity
  5. autopilot: automated backfill and alerts, feature selection.
Written on January 20, 2024