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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,33 +1,333 @@ | ||
| # Roadmap to Cracking MAANG Interviews | ||
|
|
||
| ## Coding | ||
| 1. DSA | ||
| 2. Data Processing and Manipulation (w/Python and SQL) | ||
| 3. Math and Statistics | ||
| 4. ML Functions | ||
| 5. ML Applications | ||
|
|
||
| ## Applied Statistics (w/ Probability) | ||
| 1. Make the shortest and most concise notes | ||
| 2. Top Questions | ||
| [](https://www.python.org) | ||
|  | ||
|  | ||
|  | ||
|  | ||
|
|
||
| ## Software Engineer (ML) | ||
| - Coding | ||
| - DSA (LeetCode) | ||
| - ML Algos from scratch | ||
| 1. [Uber] Write an AUC from scratch using vanilla Python | ||
| 2. [Google] Write the K-Means algorithm using Numpy only | ||
| - ML Theory (Breadth) - General Understanding | ||
| 1. [Amazon] Explain how the cross-validation work | ||
| 2. [Etsy] How do you handle imbalanced labels in classification models | ||
| 3. [McKinsey] What is the variance-bias trade-off? | ||
| - ML Algorithms (Depth) - Depth in 1-2 algorithms | ||
| 1. [Amazon] What is the pseudocode of the Random Forest model? | ||
| 2. [Amazon] What is the variance and bias of the Random Forest model? | ||
| 3. [Amazon] How is the Random Forest different from Gradient Boosted Trees? | ||
| - Applied ML / Business Case | ||
| 1. [Google] Given a dataset that contains purchase history on PlayStore, how would you build a propensity score model? | ||
| 2. [PayPal] How would you build a fraud model without labels? | ||
| 3. [Apple] How would you identify meaningful segmentation? | ||
| - ML System Design | ||
| 1. [Google] Build a real-time translation system | ||
| 2. [Uber] Build a real-time ETA model | ||
| 3. [Amazon] Build a recommender system for product search | ||
|
|
||
| ## Data Scientist | ||
| - Coding | ||
| - DSA (if Full Stack i.e., deploy models in production) | ||
| - Data Manipulation (SQL and Pandas) | ||
| - Statistical Coding | ||
| - ML Functions (From Scratch) | ||
| - Applied Statistics | ||
| - ML & MLOps | ||
| - Product Sense & AB Testing | ||
| - Causal Inference | ||
|
|
||
| ====================================== | ||
|
|
||
| To check gpu utilisation | ||
|
|
||
| !watch -n1 nvidia-smi | ||
|
|
||
| 𝟭. 𝗢𝗟𝗦 | ||
| ↳ Price optimization | ||
| ↳ Customer lifetime value prediction | ||
| ↳ Demand forecasting | ||
| ↳ Factor analysis | ||
|
|
||
| 𝟮. 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 | ||
| ↳ Customer propensity model | ||
| ↳ Uplift modeling | ||
| ↳ Attribution modeling | ||
| ↳ Fraud modeling | ||
|
|
||
| 𝟯. 𝗞-𝗠𝗲𝗮𝗻𝘀 | ||
| ↳ Customer segmentation | ||
| ↳ Time-series clustering | ||
| ↳ Market basket analysis | ||
| ↳ Product grouping | ||
|
|
||
| 𝗕𝗮𝘀𝗶𝗰𝘀 | ||
| - Univariate statistics - mean, median, mode | ||
| - Standard deviation and variance | ||
| - Covariance and correlation | ||
| - Population and sample | ||
| - Nominal, ordinal and continuous, discrete data types | ||
| - Outlines | ||
| - The Simpson’s Paradox | ||
| - Selection Bias | ||
|
|
||
| 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 | ||
| - Hypothesis Statements | ||
| - Z-Test | ||
| - T-Test | ||
| - T-Test for sample means | ||
| - Z-Test for proportions | ||
| - Paired and unpaired T-Tests | ||
| - Variance test | ||
| - ANOVA | ||
| - Chi-Squared test | ||
| - Goodness of Fit test for categorical data | ||
| - Nominal, ordinal and continuous, discrete data types | ||
| - Pairwise tests | ||
| - T-Test assumptions | ||
| - Non-parametric tests | ||
| - Type 1 & 2 Errors | ||
|
|
||
| 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 | ||
| - The Bayes Theorem | ||
| - Conditional probability | ||
| - Normal distribution | ||
| - Uniform distribution | ||
| - Bernoulli distribution | ||
| - Binomial distribution | ||
| - Geometric distribution | ||
| - Poisson distribution | ||
| - Exponential distribution | ||
| - Deriving the mean and variance of distributions | ||
| - Central Limit Theorem | ||
| - The Birthday problem | ||
| - Card probability problems | ||
| - Die roll problems | ||
|
|
||
| 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 | ||
| - OLS regression | ||
| - Confidence vs prediction intervals | ||
| - Logistic regression | ||
| - Regression model assumptions | ||
| - Model diagnostic checks | ||
| - R-Square vs R-Square Adjusted | ||
| - AIC, BIC, CP Statistics | ||
| - Model Interpretation | ||
|
|
||
| # Large Scale Machine Learning (with MLOps) | ||
|
|
||
| For use cases in computer vision and NLP, features can be computed using a small number of data sources. However, for use cases with tabular data like fraud, recommender system, ETA, dynamic pricing, etc. features are computed from many sources, | ||
|
|
||
| ## Data Support | ||
| 1. Data Ingestion | ||
| - Streaming Ingestion: Click Stream (millions/sec) | ||
| Clickstream is user interactions with some interface: Mobile App, Voice Assistant, Desktop | ||
| Clickstream Tools (Pub/Sub): Kafka (Apache open source), Kinesis (AWS Managed) | ||
|
|
||
| - install kafka | ||
| - setup zookeeper setup (directory and port) and run the zookeeper server. | ||
| - setup broker and run the broker server | ||
|
|
||
| Note: while setting up you would also set topics | ||
|
|
||
| setup a producer: | ||
| - webapp with click stream | ||
| - connect the falsk app with kafka broker | ||
| - send the response from web app to kafka broker to specific topic in broker | ||
|
|
||
| You can setup a consumer message that validates whether we are able to receive message from producer in terminal. | ||
|
|
||
| Notes: | ||
| I Clickstream | ||
| An ordered series of interactions that users have with some interface. In the traditional sense, this can be literal clicks of a mouse on a | ||
| desktop browser. Interactions can also come from touchscreens and conversational user interfaces. | ||
| | Change Data Capture | ||
| The process of recording changes In the data within a database system. For Instance, If a user cancels their Netflix subscription, then | ||
| the row In some table will change to indicate that they're no longer a subscriber. | ||
| The change In this row can be recorded and referenced later for analysis or audit purposes. | ||
| | Apache Kafka % | ||
| An open-source software platform which provides a way to handle real-time data streaming. | ||
| | Amazon Kinesis % | ||
| An AWS product that provides a way to handle real-time data streaming. | ||
|
|
||
| | Zookeeper % | ||
| Aservice designed to reliably coordinate distributed systems via naming service, configuration management, data synchronization, | ||
| leader election, message queuing, or notification systems. | ||
|
|
||
| | Database | ||
| Atool used to collect and organize data. Typically, database management systems allow users to Interact with the database. | ||
|
|
||
| I oLtp | ||
| Online transaction processing. A system that handles (near) real-time business processes. For example, a database that maintains a | ||
| table of the users subscribed to Netflix and which Is then used to enable successful log-ins would be considered OLTP. This is opposed | ||
| to OLAP. | ||
|
|
||
| I oLap | ||
| online analytical processing. A system that handles the analytical processes of a business, including reporting, auditing, and business | ||
| Intelligence. For example, this may be a Hadoop cluster which maintains user subscription history for Netflix. This is opposed to OLTP. | ||
|
|
||
| | Availability Zone (AZ) | ||
| Typically a single data center within a region which has two or more data centers. The term "multi-AZ" implies that an application or | ||
| software resource is present across more than one AZ within a reglon. This strategy allows the software resource to continue operating, | ||
| even if a data center becomes unavailable. | ||
|
|
||
| ## ML and MLOps | ||
| 1. ML Crash Course | ||
| 2. Large Scale ML | ||
|
|
||
| ## Product Sense and AB Testing | ||
| 1. Make the shortest and most concise notes | ||
| 2. Top Questions | ||
|
|
||
| ## Causal Inference | ||
| 1. Make the shortest and most concise notes | ||
| 2. Top Questions | ||
|
|
||
| ## System Design | ||
| 1. ML System Design | ||
| 2. Large Scale System Design (System Expert Course) | ||
|
|
||
| ## Basic SWE | ||
| 1. OOPs | ||
| 2. LLD (Object Oriented Design) | ||
| 3. Infrastructure (Infra Expert Course) | ||
|
|
||
| - Change Data Capture | ||
| - Live Video | ||
|
|
||
| 2. Data Storage | ||
| configure hdfs sink through kafka | ||
| pass the address of name node or master node | ||
| then run the connector | ||
|
|
||
| you can see the name node and verify what are getting stored | ||
|
|
||
| Notes: | ||
| | Hard Disk Drive (HDD) | ||
| Astorage device which operates by setting bits on a spinning magnetic disk. The capacity and the read/write performance of the HDD | ||
| are the main characteristics to consider when using an HDD within a particular system. | ||
|
|
||
| | Data Replication | ||
| Astrategy used to mitigate the potential of data loss In the event of a system or component failure. In the most basic form, it involves | ||
| writing Identical data to more than one device or location. More efficient techniques like erasure coding incorporate mathematics to | ||
| recover lost data without referring to an explicit copy of the data | ||
|
|
||
| | Hadoop Distributed File System (HDFS) | ||
| An open-source Apache software product which provides a distributed storage framework. | ||
|
|
||
| I Avro | ||
| Arow-oriented data serializer provided by Apache. | ||
|
|
||
| | Parquet | ||
| A column-oriented data storage format provided by Apache. | ||
| | Exactly-once Semantics § | ||
| Guarantees that an object within a distributed system is processed exactly once. Other semantics include maybe, at-least-once, and at- | ||
| most-once § | ||
|
|
||
| 3. Data Processing | ||
| SSH into name node of hdfs cluster | ||
| use AWS EMR to setup the processing cluster | ||
| Jupyter notebook outisde of spark cluster also can interact with it and submit jobs in cluster. The tool to do that is livy. | ||
| I Recommendation Carousel | ||
| A component within a graphical or conversational user interface which presents recommendations to a user. This can include | ||
| products, ads, and media content. | ||
| | Central Processing Unit (CPU) | ||
| A general purpose compute resource responsible for executing computer programs. | ||
| | Seasonality | ||
| The predictable changes of data throughout the calendar year. | ||
| I Parallelization | ||
| When two or more computer programs are executed at the same Instant across more than one processor. | ||
| | Random Access Memory (RAM) | ||
| A device on a computer which stores the data and machine code of a running computer program. | ||
| | Apache Spark | ||
| Asoftware interface which provides implicit parallelism and fault tolerance across a cluster of machines. | ||
| | Apache YARN | ||
| Asoftware product responsible for managing compute resources across a cluster of machines. | ||
| | Elastic MapReduce (EMR) % | ||
| An Amazon Web Services product which provides users access to a Hadoop cluster. | ||
| | Jupyter Notebook | ||
| A Project jupyter product which provides an interactive workspace to execute code. | ||
| 4. Process Orchestration | ||
| The airflow dag defines how worker first ssh into name node of hadoop cluster. The worker trigger spark jobs. | ||
| So we can say airflow automates the data processing pipelines. | ||
| | Apache Airflow | ||
| Aworkflow management system that provides users a way to author, schedule, and execute software. | ||
|
|
||
| ## Exploration | ||
| Our jupyter notebook should have access to hdfs and spark clusters for compute. So we have explored the data and its applications to models. | ||
| | Automated Machine Learning (Auto-ML) | ||
| A strategy which automates the process of applying features and labels to a machine learning model. | | ||
| | Data Governance | ||
| The method of managing, using, and protecting an organization's data. | ||
|
|
||
| ## Experimentation | ||
| Once we are satisfied on offline evaluations during exploration, we want online evaluations with real users. | ||
| 1. Frequentist AB Testing | ||
| - A/B Testing | ||
| The process of providing two or more different experiences across two or more subgroups of a population. The goal is to measure the change in behavior of the subgroups upon receiving the respective experiences. | ||
|
|
||
| - A/ATest | ||
| An A/B test in which the experiences being tested are identical to one another. This is done in an effort to determine statistical validity of the A/B tool, the metric being monitored, and the analysis process being used. | ||
|
|
||
| - User Agent | ||
| An identifier used to describe the software application which a user is using to Interact with another software application. For instance, | ||
| an HTTP request to a website typically includes the user agent so that the website knows how to render the webpage. | ||
|
|
||
| - Session ID | ||
| A unique identifier assigned to a user to keep track of a user's connected interactions. For instance, a session may include a user logging In, purchasing an item, and logging out. Here, the session ID would be used to reference the group of those three Interactions. | ||
| This session ID can be stored in the user's Internet browser as a cookie. | ||
|
|
||
| - Cookie | ||
| A small piece of data stored by a browser which indicates stateful information for a particular website. For instance, a cookle can be | ||
| stored in your browser after you log in to a website to indicate that you are logged In. This will stop subsequent pages from asking you | ||
| to log In again. | ||
|
|
||
| Frequentist approach uses p-value and confidence interval | ||
| 3. Bayesian AB Testing | ||
| Here we use prior knowledge. | ||
| The posterior and prior of same distribution initially. | ||
| In bayesian we sample from distribution and infer results with probability. | ||
| Beta Distribution | ||
| This distribution is used to model percentages and proportions such as click-through probabilities. | ||
| 5. Multi Armed Bandit | ||
| | Multi-Armed Bandit (MAB) | ||
| A process which provides a number of choices | | ||
|
|
||
| 7. Impact Estimation | ||
| THis is determine if experiment is worth is pre-experiment and whether to launch post-experimentation | ||
| 3 Key Terms - | ||
| | Shadow Test 3 | ||
| Running two or more versions of software In parallel while only surfacing the result of one of the experiences to the end user. Thisfs | | ||
| done in an effort to gauge the differences between the software versions. 1 | ||
| | Sample Selection Bias | ||
| The bias that occurs when sampling a population into one or more subgroups at random results in a systematic inclusion or exclusion | ||
| of some data. § | ||
| | Experiment Collision | | ||
| The event where one experiment unintentionally influences the result of one or more separate experiments. § | ||
|
|
||
| ## Large Scale Training | ||
| 1. Basic Models (Random Forest, Boosted Trees, Matrix Factorization, Logistic Regression etc). | ||
| Read parquet files from HDFS Node and then run experiments. | ||
| | MLlib | ||
| Alibrary provided by Apache Spark which provides Spark clusters access to machine learning algorithms and refated utiities. MLIIb | | ||
| provides a Dataframe-based APl which Is unofficially referred to as Spark ML, § | ||
| ~ provides a Dataframe-based APl which is unofficially referred to as Spark ML | ||
| 2. Deep Learning Models | ||
| For distributed DL we will use PyTorch or Tensorflow....or some more advanced tools like Horovod by Uber | ||
| | Model Parallelism | | ||
| Amachine learning model training strategy used to maximize the utilization of compute resources (CPUs/GPUS) In which the model Is 3 | ||
| distributed across two or more devices. 1 | ||
| | Data Parallelism | ||
| Amachine learning model training strategy used to maximize the utilization of compute resources (CPUs/GPUS) In which the datais | | ||
| distributed across two or more devices § | ||
| | Graphics Processing Unit § | ||
| A specialized device that has many cores, allowing it to perform many operations at a time. § | ||
| GPUs are often used within deep learning to accelerate training of neural networks by taking advantage of their ability to perform 1 | ||
| many parallel computations. | ||
| | Concurrency | ||
| When two or computer programs share a single processor. | ||
| 3. Model Validation | ||
| Bayesian Optimization is good but it is not non-parallizable. In Bayesian Op, the prior keep changing with iterations. | ||
|
|
||
| ## Productionization | ||
| This is the MLOps (Writing test to reduce error, versioning, tracking, environment syncronization etc) | ||
|
|
||
| ## Hosting | ||
| 1. Data Hosting | ||
| | In-memory Database 1 | ||
| A database which relies either solely or primarily on the RAM of a computer. | ||
| | Distributed Cache % | ||
| A cache which s distributed across two or more machines | ||
| 2. Model Hosting | ||
| I Numba | ||
| Ajust-in-time Python compller which resolves a subset of the python programming language down to machine code | ||
|
|
||
| Metrics | ||
| Lift, CTR | ||
|
|
||
| Offline metrics: Precision, Recall, Logloss, Ranking Loss | ||
|
|
||
| Online metrics: CTR, Watch Time, Conversion Rate | ||
|
|
||
|  |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,333 +1,33 @@ | ||
| [](https://www.python.org) | ||
|  | ||
|  | ||
|  | ||
|  | ||
|
|
||
| ## Software Engineer (ML) | ||
| - Coding | ||
| - DSA (LeetCode) | ||
| - ML Algos from scratch | ||
| 1. [Uber] Write an AUC from scratch using vanilla Python | ||
| 2. [Google] Write the K-Means algorithm using Numpy only | ||
| - ML Theory (Breadth) - General Understanding | ||
| 1. [Amazon] Explain how the cross-validation work | ||
| 2. [Etsy] How do you handle imbalanced labels in classification models | ||
| 3. [McKinsey] What is the variance-bias trade-off? | ||
| - ML Algorithms (Depth) - Depth in 1-2 algorithms | ||
| 1. [Amazon] What is the pseudocode of the Random Forest model? | ||
| 2. [Amazon] What is the variance and bias of the Random Forest model? | ||
| 3. [Amazon] How is the Random Forest different from Gradient Boosted Trees? | ||
| - Applied ML / Business Case | ||
| 1. [Google] Given a dataset that contains purchase history on PlayStore, how would you build a propensity score model? | ||
| 2. [PayPal] How would you build a fraud model without labels? | ||
| 3. [Apple] How would you identify meaningful segmentation? | ||
| - ML System Design | ||
| 1. [Google] Build a real-time translation system | ||
| 2. [Uber] Build a real-time ETA model | ||
| 3. [Amazon] Build a recommender system for product search | ||
|
|
||
| ## Data Scientist | ||
| - Coding | ||
| - DSA (if Full Stack i.e., deploy models in production) | ||
| - Data Manipulation (SQL and Pandas) | ||
| - Statistical Coding | ||
| - ML Functions (From Scratch) | ||
| - Applied Statistics | ||
| - ML & MLOps | ||
| - Product Sense & AB Testing | ||
| - Causal Inference | ||
|
|
||
| ====================================== | ||
|
|
||
| To check gpu utilisation | ||
|
|
||
| !watch -n1 nvidia-smi | ||
|
|
||
| 𝟭. 𝗢𝗟𝗦 | ||
| ↳ Price optimization | ||
| ↳ Customer lifetime value prediction | ||
| ↳ Demand forecasting | ||
| ↳ Factor analysis | ||
|
|
||
| 𝟮. 𝗟𝗼𝗴𝗶𝘀𝘁𝗶𝗰 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 | ||
| ↳ Customer propensity model | ||
| ↳ Uplift modeling | ||
| ↳ Attribution modeling | ||
| ↳ Fraud modeling | ||
|
|
||
| 𝟯. 𝗞-𝗠𝗲𝗮𝗻𝘀 | ||
| ↳ Customer segmentation | ||
| ↳ Time-series clustering | ||
| ↳ Market basket analysis | ||
| ↳ Product grouping | ||
|
|
||
| 𝗕𝗮𝘀𝗶𝗰𝘀 | ||
| - Univariate statistics - mean, median, mode | ||
| - Standard deviation and variance | ||
| - Covariance and correlation | ||
| - Population and sample | ||
| - Nominal, ordinal and continuous, discrete data types | ||
| - Outlines | ||
| - The Simpson’s Paradox | ||
| - Selection Bias | ||
|
|
||
| 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 | ||
| - Hypothesis Statements | ||
| - Z-Test | ||
| - T-Test | ||
| - T-Test for sample means | ||
| - Z-Test for proportions | ||
| - Paired and unpaired T-Tests | ||
| - Variance test | ||
| - ANOVA | ||
| - Chi-Squared test | ||
| - Goodness of Fit test for categorical data | ||
| - Nominal, ordinal and continuous, discrete data types | ||
| - Pairwise tests | ||
| - T-Test assumptions | ||
| - Non-parametric tests | ||
| - Type 1 & 2 Errors | ||
|
|
||
| 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 | ||
| - The Bayes Theorem | ||
| - Conditional probability | ||
| - Normal distribution | ||
| - Uniform distribution | ||
| - Bernoulli distribution | ||
| - Binomial distribution | ||
| - Geometric distribution | ||
| - Poisson distribution | ||
| - Exponential distribution | ||
| - Deriving the mean and variance of distributions | ||
| - Central Limit Theorem | ||
| - The Birthday problem | ||
| - Card probability problems | ||
| - Die roll problems | ||
|
|
||
| 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 | ||
| - OLS regression | ||
| - Confidence vs prediction intervals | ||
| - Logistic regression | ||
| - Regression model assumptions | ||
| - Model diagnostic checks | ||
| - R-Square vs R-Square Adjusted | ||
| - AIC, BIC, CP Statistics | ||
| - Model Interpretation | ||
|
|
||
| # Large Scale Machine Learning (with MLOps) | ||
|
|
||
| For use cases in computer vision and NLP, features can be computed using a small number of data sources. However, for use cases with tabular data like fraud, recommender system, ETA, dynamic pricing, etc. features are computed from many sources, | ||
|
|
||
| ## Data Support | ||
| 1. Data Ingestion | ||
| - Streaming Ingestion: Click Stream (millions/sec) | ||
| Clickstream is user interactions with some interface: Mobile App, Voice Assistant, Desktop | ||
| Clickstream Tools (Pub/Sub): Kafka (Apache open source), Kinesis (AWS Managed) | ||
|
|
||
| - install kafka | ||
| - setup zookeeper setup (directory and port) and run the zookeeper server. | ||
| - setup broker and run the broker server | ||
|
|
||
| Note: while setting up you would also set topics | ||
|
|
||
| setup a producer: | ||
| - webapp with click stream | ||
| - connect the falsk app with kafka broker | ||
| - send the response from web app to kafka broker to specific topic in broker | ||
|
|
||
| You can setup a consumer message that validates whether we are able to receive message from producer in terminal. | ||
|
|
||
| Notes: | ||
| I Clickstream | ||
| An ordered series of interactions that users have with some interface. In the traditional sense, this can be literal clicks of a mouse on a | ||
| desktop browser. Interactions can also come from touchscreens and conversational user interfaces. | ||
| | Change Data Capture | ||
| The process of recording changes In the data within a database system. For Instance, If a user cancels their Netflix subscription, then | ||
| the row In some table will change to indicate that they're no longer a subscriber. | ||
| The change In this row can be recorded and referenced later for analysis or audit purposes. | ||
| | Apache Kafka % | ||
| An open-source software platform which provides a way to handle real-time data streaming. | ||
| | Amazon Kinesis % | ||
| An AWS product that provides a way to handle real-time data streaming. | ||
|
|
||
| | Zookeeper % | ||
| Aservice designed to reliably coordinate distributed systems via naming service, configuration management, data synchronization, | ||
| leader election, message queuing, or notification systems. | ||
|
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||
| | Database | ||
| Atool used to collect and organize data. Typically, database management systems allow users to Interact with the database. | ||
|
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||
| I oLtp | ||
| Online transaction processing. A system that handles (near) real-time business processes. For example, a database that maintains a | ||
| table of the users subscribed to Netflix and which Is then used to enable successful log-ins would be considered OLTP. This is opposed | ||
| to OLAP. | ||
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| I oLap | ||
| online analytical processing. A system that handles the analytical processes of a business, including reporting, auditing, and business | ||
| Intelligence. For example, this may be a Hadoop cluster which maintains user subscription history for Netflix. This is opposed to OLTP. | ||
|
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||
| | Availability Zone (AZ) | ||
| Typically a single data center within a region which has two or more data centers. The term "multi-AZ" implies that an application or | ||
| software resource is present across more than one AZ within a reglon. This strategy allows the software resource to continue operating, | ||
| even if a data center becomes unavailable. | ||
| # Roadmap to Cracking MAANG Interviews | ||
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| ## Coding | ||
| 1. DSA | ||
| 2. Data Processing and Manipulation (w/Python and SQL) | ||
| 3. Math and Statistics | ||
| 4. ML Functions | ||
| 5. ML Applications | ||
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||
| ## Applied Statistics (w/ Probability) | ||
| 1. Make the shortest and most concise notes | ||
| 2. Top Questions | ||
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| - Change Data Capture | ||
| - Live Video | ||
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| 2. Data Storage | ||
| configure hdfs sink through kafka | ||
| pass the address of name node or master node | ||
| then run the connector | ||
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| you can see the name node and verify what are getting stored | ||
|
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||
| Notes: | ||
| | Hard Disk Drive (HDD) | ||
| Astorage device which operates by setting bits on a spinning magnetic disk. The capacity and the read/write performance of the HDD | ||
| are the main characteristics to consider when using an HDD within a particular system. | ||
|
|
||
| | Data Replication | ||
| Astrategy used to mitigate the potential of data loss In the event of a system or component failure. In the most basic form, it involves | ||
| writing Identical data to more than one device or location. More efficient techniques like erasure coding incorporate mathematics to | ||
| recover lost data without referring to an explicit copy of the data | ||
|
|
||
| | Hadoop Distributed File System (HDFS) | ||
| An open-source Apache software product which provides a distributed storage framework. | ||
|
|
||
| I Avro | ||
| Arow-oriented data serializer provided by Apache. | ||
|
|
||
| | Parquet | ||
| A column-oriented data storage format provided by Apache. | ||
| | Exactly-once Semantics § | ||
| Guarantees that an object within a distributed system is processed exactly once. Other semantics include maybe, at-least-once, and at- | ||
| most-once § | ||
|
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| 3. Data Processing | ||
| SSH into name node of hdfs cluster | ||
| use AWS EMR to setup the processing cluster | ||
| Jupyter notebook outisde of spark cluster also can interact with it and submit jobs in cluster. The tool to do that is livy. | ||
| I Recommendation Carousel | ||
| A component within a graphical or conversational user interface which presents recommendations to a user. This can include | ||
| products, ads, and media content. | ||
| | Central Processing Unit (CPU) | ||
| A general purpose compute resource responsible for executing computer programs. | ||
| | Seasonality | ||
| The predictable changes of data throughout the calendar year. | ||
| I Parallelization | ||
| When two or more computer programs are executed at the same Instant across more than one processor. | ||
| | Random Access Memory (RAM) | ||
| A device on a computer which stores the data and machine code of a running computer program. | ||
| | Apache Spark | ||
| Asoftware interface which provides implicit parallelism and fault tolerance across a cluster of machines. | ||
| | Apache YARN | ||
| Asoftware product responsible for managing compute resources across a cluster of machines. | ||
| | Elastic MapReduce (EMR) % | ||
| An Amazon Web Services product which provides users access to a Hadoop cluster. | ||
| | Jupyter Notebook | ||
| A Project jupyter product which provides an interactive workspace to execute code. | ||
| 4. Process Orchestration | ||
| The airflow dag defines how worker first ssh into name node of hadoop cluster. The worker trigger spark jobs. | ||
| So we can say airflow automates the data processing pipelines. | ||
| | Apache Airflow | ||
| Aworkflow management system that provides users a way to author, schedule, and execute software. | ||
|
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||
| ## Exploration | ||
| Our jupyter notebook should have access to hdfs and spark clusters for compute. So we have explored the data and its applications to models. | ||
| | Automated Machine Learning (Auto-ML) | ||
| A strategy which automates the process of applying features and labels to a machine learning model. | | ||
| | Data Governance | ||
| The method of managing, using, and protecting an organization's data. | ||
|
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||
| ## Experimentation | ||
| Once we are satisfied on offline evaluations during exploration, we want online evaluations with real users. | ||
| 1. Frequentist AB Testing | ||
| - A/B Testing | ||
| The process of providing two or more different experiences across two or more subgroups of a population. The goal is to measure the change in behavior of the subgroups upon receiving the respective experiences. | ||
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|
||
| - A/ATest | ||
| An A/B test in which the experiences being tested are identical to one another. This is done in an effort to determine statistical validity of the A/B tool, the metric being monitored, and the analysis process being used. | ||
|
|
||
| - User Agent | ||
| An identifier used to describe the software application which a user is using to Interact with another software application. For instance, | ||
| an HTTP request to a website typically includes the user agent so that the website knows how to render the webpage. | ||
|
|
||
| - Session ID | ||
| A unique identifier assigned to a user to keep track of a user's connected interactions. For instance, a session may include a user logging In, purchasing an item, and logging out. Here, the session ID would be used to reference the group of those three Interactions. | ||
| This session ID can be stored in the user's Internet browser as a cookie. | ||
|
|
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| - Cookie | ||
| A small piece of data stored by a browser which indicates stateful information for a particular website. For instance, a cookle can be | ||
| stored in your browser after you log in to a website to indicate that you are logged In. This will stop subsequent pages from asking you | ||
| to log In again. | ||
|
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||
| Frequentist approach uses p-value and confidence interval | ||
| 3. Bayesian AB Testing | ||
| Here we use prior knowledge. | ||
| The posterior and prior of same distribution initially. | ||
| In bayesian we sample from distribution and infer results with probability. | ||
| Beta Distribution | ||
| This distribution is used to model percentages and proportions such as click-through probabilities. | ||
| 5. Multi Armed Bandit | ||
| | Multi-Armed Bandit (MAB) | ||
| A process which provides a number of choices | | ||
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|
||
| 7. Impact Estimation | ||
| THis is determine if experiment is worth is pre-experiment and whether to launch post-experimentation | ||
| 3 Key Terms - | ||
| | Shadow Test 3 | ||
| Running two or more versions of software In parallel while only surfacing the result of one of the experiences to the end user. Thisfs | | ||
| done in an effort to gauge the differences between the software versions. 1 | ||
| | Sample Selection Bias | ||
| The bias that occurs when sampling a population into one or more subgroups at random results in a systematic inclusion or exclusion | ||
| of some data. § | ||
| | Experiment Collision | | ||
| The event where one experiment unintentionally influences the result of one or more separate experiments. § | ||
|
|
||
| ## Large Scale Training | ||
| 1. Basic Models (Random Forest, Boosted Trees, Matrix Factorization, Logistic Regression etc). | ||
| Read parquet files from HDFS Node and then run experiments. | ||
| | MLlib | ||
| Alibrary provided by Apache Spark which provides Spark clusters access to machine learning algorithms and refated utiities. MLIIb | | ||
| provides a Dataframe-based APl which Is unofficially referred to as Spark ML, § | ||
| ~ provides a Dataframe-based APl which is unofficially referred to as Spark ML | ||
| 2. Deep Learning Models | ||
| For distributed DL we will use PyTorch or Tensorflow....or some more advanced tools like Horovod by Uber | ||
| | Model Parallelism | | ||
| Amachine learning model training strategy used to maximize the utilization of compute resources (CPUs/GPUS) In which the model Is 3 | ||
| distributed across two or more devices. 1 | ||
| | Data Parallelism | ||
| Amachine learning model training strategy used to maximize the utilization of compute resources (CPUs/GPUS) In which the datais | | ||
| distributed across two or more devices § | ||
| | Graphics Processing Unit § | ||
| A specialized device that has many cores, allowing it to perform many operations at a time. § | ||
| GPUs are often used within deep learning to accelerate training of neural networks by taking advantage of their ability to perform 1 | ||
| many parallel computations. | ||
| | Concurrency | ||
| When two or computer programs share a single processor. | ||
| 3. Model Validation | ||
| Bayesian Optimization is good but it is not non-parallizable. In Bayesian Op, the prior keep changing with iterations. | ||
|
|
||
| ## Productionization | ||
| This is the MLOps (Writing test to reduce error, versioning, tracking, environment syncronization etc) | ||
|
|
||
| ## Hosting | ||
| 1. Data Hosting | ||
| | In-memory Database 1 | ||
| A database which relies either solely or primarily on the RAM of a computer. | ||
| | Distributed Cache % | ||
| A cache which s distributed across two or more machines | ||
| 2. Model Hosting | ||
| I Numba | ||
| Ajust-in-time Python compller which resolves a subset of the python programming language down to machine code | ||
|
|
||
| Metrics | ||
| Lift, CTR | ||
|
|
||
| Offline metrics: Precision, Recall, Logloss, Ranking Loss | ||
|
|
||
| Online metrics: CTR, Watch Time, Conversion Rate | ||
|
|
||
|  | ||
| ## ML and MLOps | ||
| 1. ML Crash Course | ||
| 2. Large Scale ML | ||
|
|
||
| ## Product Sense and AB Testing | ||
| 1. Make the shortest and most concise notes | ||
| 2. Top Questions | ||
|
|
||
| ## Causal Inference | ||
| 1. Make the shortest and most concise notes | ||
| 2. Top Questions | ||
|
|
||
| ## System Design | ||
| 1. ML System Design | ||
| 2. Large Scale System Design (System Expert Course) | ||
|
|
||
| ## Basic SWE | ||
| 1. OOPs | ||
| 2. LLD (Object Oriented Design) | ||
| 3. Infrastructure (Infra Expert Course) |