Snapchat ML Interview Prep

• Preparation for a technical screening at Snapchat needs software engineering rigor, ML expertise and data intuition.
• ML roles at Snap usually include coding skills, ML fundamentals, applied ML design and ML system design.

Coding

• Focus on solving algorithmic problems based on data structures, algorithms, and writing clean, efficient code.

ML Fundamentals

• Core ML theory and models are discussed, such as supervised vs. unsupervised learning, recommendation systems, ranking algorithms, model evaluation metrics and optimization techniques.

Applied ML Design

• End-to-end ML solutions for real-world problems are walked through, which includes selecting models, feature engineering and evaluating performance (often in ambiguous problem settings).

ML System Design

• Focus on scalable, robust ML systems for production, addressing large-scale data, deployment strategies, infrastructure trade-offs and monitoring of the models in production.
• Roles like Technical Program Manager (TPM) or ML Platform require broad knowledge.
• Solid software engineering principles, familiarity with data science workflows, database knowledge and ML Ops are key for ML Platform roles.
• A structured overview is provided for these areas with explanations, examples, and quick Q&A.

Machine Learning Fundamentals

• Designing algorithms that learn patterns from data is key in order to make predictions or decisions.

Supervised vs. Unsupervised Learning

• Models learn from labeled data (input-output pairs) in supervised learning to predict outputs for new inputs, such as classifying an image or predicting a number.
• Models find pattern or groupings in unlabeled data with unsupervised learning, such as clustering users by behavior.

Model Training and Evaluation

• Training optimizes a model's parameters on a dataset.
• Evaluation of performance involves metrics like accuracy, precision/recall, and RMSE on held-out test data.
• Splitting data (train/validation/test) is good practice to detect overfitting, which occurs when a model memorizes training data and performs poorly on new data.
• Combatting overfitting happens through techniques like cross-validation and regularization including dropout in neural nets.

Feature Engineering

• The process involved with transforming raw data input features that make machine learning algorithms work.
• Transforming timestamps into day-of-week, extracting word frequencies from test, or normalizing numerical ranges are all examples of Feature Engineering.
• Understanding feature creation remains important even with representation learning through automation since modern ML is especially deep learning.

Common Algorithms

• Linear models (linear/logistic regression), decision trees and ensembles (random forests, gradient boosting), and basic neural networks are all key to know.
• Decision trees handle heterogenous features and can rank features importance.
• Neural networks excel with large data and complex patterns including image or speech recognition.

Model Tuning

• Performance improving involves adjusting hyperparameters including learning rate, tree depth, and regularization strength.
• Grid search, random search, or advanced techniques like Bayesian optimization are included here.

MLOps Concepts and Industry Trends

• Reliably and efficiently deploying/maintaining ML models in production is the goal of MLOps through it's combination of of ML, software engineering and data engineering.
• Key components include automation, versioning, testing and monitoring across the ML lifecycle.

Reproducible Workflows

• Source control for code and data, environment management, and experiment tracking are necessary here.
• Git, and tracking datasets and models with version IDs, or a model registry can all be helpful.

Continuous Integration/Continuous Deployment (CI/CD)

• Automated pipelines help train, test and deploy the model.
• New model versions can be quickly validated and rolled out efficiently by 2025. -Companies are adopting MLOps so they can have faster model deployment cycles (30-50% reduction in time) because of automation.

Model Deployment & Serving

• Packaging models (as microservices or via a cloud ML platform) helps serve predictions at scale.
• Batch prediction jobs and real-time serving through APIs are included.
• Tools such as Docker/Kubernetes, and cloud services (AWS SageMaker, Google Vertex AI) are used normally.
• Consideration for scalability and latency is shown through an awareness of the inference architecture (caching features or using GPUs for heavy deep learning models).

Monitoring and Model Performance

• Monitoring deployed models ensures there is model drift (incoming data changes over time and model accuracy degrades).
• Setting up logging of predictions and actual outcomes, and enabling detection of anomalies in model outputs or datapipelines is done through MLOps.
• Alert may trigger retraining or fallback to a previous model in a model's performance drops.

Collaboration Lifecycle Management

• Collaboration is encourage between data scientists, engineers, and stakeholders, which can be facilitated through shared tools (feature stores for reusing features or dashboards for metrics).
• Enforcing governance is important for industries that are regulated with audit trails, reproducibility, and compliance.
• Prediction of the global MLOps market is to reach around $16.6 billion by 2030 via a nearly 40% annual growth.
• Many organizations are building internal ML platforms to streamline the ML lifecycle.
• Uber uses Michelangelo to manage data, training models, deployment, and monitoring predictions at scale.
• Google uses Tensorflow Extended (TFX) to reduce time-to-production for ML models by providing components for data ingestion, validation, training and serving.
• Productionizing ML at scale (focus on reliably operationalizing) is a trend that these platforms illustrate beyond just building accurate models.

Real-World Example

• A data pipeline will be used by a recommendation system for a social app to aggregate user interactions into features, retrain models weekly, deploy the new model behind an API and monitor click-through rates.
• Alert/Automatic model update can be triggered by metrics dip or shifts in input data.
• End-to-end cycle from data to deployed model to feedback is facilitated with MLOps.

Software Engineering Fundamentals

• Successful ML platforms require strong software engineering underpinnings.
• System design and coding best practices interview questions are common because ML solutions must build on solid architecture.

Scalability

• Designing components that can handle growth in lad, horizontal scaling (adding new servers behind load balancers), and caching frequently accessed data to reduce load on databases are important.
• You can use Content Delivery Networks (CDNs) for static context and partitioning workloads.

Reliability and Redundacy

• Ensuring that there are no single points of failure, deploying services in multiple availability zones, using master-slave, or multiple primary databases all helps ensure reliability and reduncancy.
• Gracefull degradation implementation should be designed with the assumption that components will fail.

Consistency vs Availability

• Trade-offs should be understood with reference to the CAP theorem for distributed systems.
• Some systems choose eventual consistency to get higher availability and partition tolerance whereas others need strict consistency for transactional accuracy.
• Feature store can use eventual consistency for speed, whereas a payment system requires strong consistency in ML platform example.

Loose Coupling & HIgh Cohesion

• Modularity, or services shouldn't have a minimal knowledge of each other's internals or clear responsibilities.
• Modifying parts of the system would then be easier to develop/modify on their own using well-defined APIs between services to unsure changes in one service will not break others unexpectedly.

Microservices Architecture

• Structuring apps collection of small services are modern large-scale systems employ a microservices architecture which can be deployed on their own.
• Small teams owns these independently deployable and loosely coupled services communicate over API or messaging systems.

Benefits of Microservices:

• Different pieces of infrastructure would be able scale on their platform.
• Improved reliability is accomplished along with using specialized toolkits for each service.

Best Practices for Microservices:

• Defined well, version interfaces must be had by services or the REST API needs to be communicated among all services or without issues to any one of the components of the microservice.
• To get the services which are needed on the same page, the implemented service discovery is registry, load balancing, or in coordination with others.
• The use of accurate observance must be executed with appropriate measures by the system as a whole.

Engineering Best Practices

• Code readability is a priority.
• Git version control is utilized for all ongoing projects.
• Highlighting with unit tests, integrating with proper systems, and continuous integration must be maintained within the project.
• Profiling issues should be understood with high regard as appropriate and clear with documentation.
• Ensuring documentation is kept as transparent as possible.

Data Science Fundamentals

• Pure engineering comes with the requirements of the data science and workflow process.
• SQL, data analysis, code, review, etc. should all be included.

SQL and Data Querying

• You should be comfortable with writing queries to select, filter, join, and aggregate data from tables in SQL.
• SQL is fundamental for being able to interact with relational databases.

SQL Key Points:

• INNER, LEFT, RIGHT, FULL JOINS types require understanding of use.
• GROUP BY aggregation require understanding of use.
• Subqueries and understanding of function require advanced analytics.
• Index query speeds optimization.

Dashboarding and Visualization

• Metrics must be monitored.
• Interactive dashboards show metrics like daily active users, snaps, and ML health.
• Visualization must be up-to-date

Python Notebooks and Analysis

• Jupyter Notebooks are staples for data scientists that allow visualization narrative documentation.

Code, Reviews, and Collaboration

• Pull requests are good for analyzing code.
• Data and results are extensions of reviewing output.
• Ensures higher quality output with chances of sharing domain knowledge.

Analytics and Experimentation

• Data-driven decision-making in core at Snap.
• KPI metrics must be defined as well as A/B Testing.
• Statistics and techniques must come with analytics.

Database Fundamentals

• Databases that store and retrieve data rely most on applications like Snapchat.
• Standard databases contain ML in a platform sense.
• Key database concepts to know include data modeling, query optimization, and transaction management.

Principles of Data Modeling

• Tables with data types should be enforced when necessary.
• Minimize redundancy of data.
• There is sometimes a need to intentionally add some redundancy when denormalizing data.
• ER modeling think in terms of entities and relationship.
• NoSQL modeling design based on access patterns.

Query Optimization

• Efficient retrieval of data with large data sets.
• Refers to minimal latency and resource usage.

Transactions Managment

• Sequence of operation executes at work.
• Must exhibit ACID properties; Atomicity, Consistency, Isolation, Durability.

Atomicity

• "All or nothing", a transaction with multiple steps guarantee of only successful steps or none.
• Data should not violate integrity, the correct sum updated, and foreign keys unbroken.

Isolation

• Concurrent transactions shouldn't interfere with each other.
• Isolation often has balance, with the main priority of performance.

Durability

• Results persist even if system crashes.

Snapchats Bento ML platform

• Snap's ML Engineering platform, you can expect questions on that in the interview.
• They have a high-level look at the ops landscape with details on publicly familiar info.

Overview of Bento

• End-to-end platform which handles most ML workflows launched in 2020.
• Supports large personalized content with unification and experimentation.

Design Objectives:

• One-stop seamless execution of data deployment with many integrated schedulers.
• Optimize scaled used cases to exceed off the shelves assumptions.
• Provides an ML infrastructure with tech hooks.

Key Components:

• Build custom infrastructure via raw streams to features.
• Training occurs, evaluation, and model exports.
• Bento automates data deployment with design.

Model and Version:

• Multiple serving patterns for online processing when needed.
• Implements ML-specific modeling, and feature tracking.

Comparison:

• Shares goals with other end-to-end applications.
• Is not a publicly familiar thing yet

Benefits

• Allows experimentation and ensures consistency.
• Tackles scale challenges with a product ecosystem.
• In-house provides support quick

Technical Questions and Answers

• Precision, recall, and model evaluations
• Data overfit strategies
• Feature store, and its use
• How to monitor performance in production
• URL Shortener design
• Monolith vs Microservice
• How to find duplicate SQL enters
• A/B testing and pitfall
• Explain ACID
• Troubleshooting database queries
• ML Advantages to platforms

Soft Skills

• Tackle tough concerns
• Can be technical
• Clear communication
• Constant feedback

Qualities to Improve

• Clear setting of goal timelines in terms of what is expected.
• What the planning and prioritization will be throughout the process.
• Effective methods of transparency and communication.
• Collaborating with other teams with data that will be fluent.

Conclusion

• Strong experience and knowledge on the topic.
• Technical screening for a position must be done.
• Stay calm.

ML Fundamentals in Ranking, Recommendation, and Ads

• Key Concepts about Key Probability Distributions
• Paradigms about machine learning and much more with these types of roles.

Key Points about Machine Learning Paradigms

• Supervised learning training and examples need to be known
• Linear regression need to come to mind randomly
• Unsupervised learning is needed to find out when new data needs more explicit details
• Reinforcement learning is a goal that maxes cumulative rewards.

Probability Distributions

Normal Distribution

• A normal visual and data that are near will become frequent.

Bernoulli and binomial

• A discrete with two outcomes.
• A fixed number with both distributions.

Poisson and exponential

• The same time as above.
• Continous modeling that is independent.

Uniform

• A distribution where the likely outcomes are equal.
• Underfitting and Overfitting*\
• Variance bias where models relate to predict errors.
• Too simple and underfits the data.
• Too complex to generalize to new data.

Regularization and L1 and L2

• Reduce overfit caused by too complex models.
• Shrinking the use of parameters.
• Adding a number to penalize the mode and discourages it from being complex.
• L2 regularizations are weights smooth and shrink toward zero.
• L1 regularization is an important aspect, and it selects the most important features.

Gradient Descent & Optimizers

• Algorith utilized for optimization with descent gradient.
• Each has an adjustment with a certain error.

Descent Methods

• Using appropriate sets that work well to create the most applicable data output.

Feature Engine Design

• The process of transferring raw data to find that predictive models can be explained on a personal level.
• This is a very important thing that shows the output of the model is relevant with better convenient data.

Feature selection

• Remove what isn't useful to help reduce overfitting, improve model interpretation, and decrease computation cost to remove.

Curse of Dimensionality

• High dimensions that will data to be far apart by scaling in dimensions fast.

Model Interpretability

• Building boxes must come by both ways or more easily by looking with visual aspects

Ranking and Advertisements

• Recommend items with relelvlance by using algorithms.
• How do we give highness in the order that follows the high level trade-offs?

Recommendation Algorithm

• User and or item based that people will want to use depending on things such as the content that they're involved with.

Advantages

• Collaborative elements have patterns.
• Improve from crowd.

Challenges

• High computation
• Start from new data, no data is too hard.

Snapchat's Bento and ML Platform

ML needs that it can be designed to power personalized elements.

• Full stack and at Snapchat scales is designed by ML.
• Integrations need to have strength, which enables us to look at area experimentation for new innovation.

Integration of Bento

• The start began in 2020 with all serving sides for things in all shapes to start by making up that process better with practice.
• For example the ML platform that implements and supports integration with multiple sources.

Bento's Design

• Provides consistent feature to not need to start reinventing all plans for each new thing.
• They can then easily help support the users better and then provide the help when needed.