104 docs tagged with "ai-systems"

End-to-end design of a production ad click prediction system - covering Wide and Deep learning, feature engineering at scale, online learning, calibration, and serving under 10ms.

Design and build production ML systems - model serving, real-time inference, vector databases, GPU infrastructure, cost optimization, and platform engineering.

Deep dive into HNSW, IVF, Product Quantization, IVFPQ, LSH, and DiskANN - how each algorithm trades recall for speed and how to choose the right one for your dataset.

How to estimate storage, compute, memory, and infrastructure requirements for ML systems before writing a line of code - including the 6PD training compute rule and model sizing.

Designing efficient batch inference pipelines for scoring millions of examples - architecture, GPU utilization, failure recovery, and production patterns.

How Apache Spark processes terabyte-scale training data - architecture, DataFrames, partitioning, joins, and integration with Delta Lake for ML feature engineering.

How static, dynamic, and continuous batching work - and how to go from 20% GPU utilization to 85% without increasing latency.

A rigorous financial and risk framework for deciding when to build ML infrastructure in-house vs use managed services - applied to feature stores, vector DBs, LLMs, and more.

Design production embedding pipelines - model selection, batch ingestion, incremental indexing, zero-downtime model upgrades, embedding drift detection, normalization, and dimensionality reduction.

How to reduce ML serving cost and latency with result caching, semantic similarity caching, KV cache for transformers, prefix caching for LLMs, and feature caching with Redis.

Safe model rollout strategies - canary deployments for gradual traffic migration, blue-green for instant switch, and automated rollback triggers.

How multi-stage ranking systems reduce millions of candidates to a final ranked list within strict latency budgets - the architecture behind every major search and recommendation system.

Build automated CI/CD pipelines for machine learning - from unit tests on transforms to canary deployments - so model degradation gets caught before it reaches users.

Implement full FinOps practice for ML teams - from commitment-based discounts and tagging strategies to budget alerts and spot instance automation.

Production computer vision at scale - autonomous vehicle perception with 30 cameras at 100Hz, real-time object detection, model compression for edge, active learning, and quality metrics.

How CAP theorem, eventual consistency, and training-serving skew apply to ML systems - feature stores, model versioning, multi-region serving, and when consistency actually matters.

The evolution from database to data lake to lakehouse - when to use each storage architecture for ML training data, feature engineering, and model serving.

Why data quality is the number-one cause of ML failures in production - Great Expectations, data contracts, PSI distribution monitoring, and pipeline quality gates.

The complete ML data stack - from raw storage through feature engineering to model training and serving, including data lakes, warehouses, lakehouses, and temporal joins.

ACID transactions, time travel, schema evolution, and training data versioning with Delta Lake - building reproducible ML pipelines on object storage.

End-to-end design of a large-scale content moderation system - covering multi-modal ML pipelines, human review integration, active learning, adversarial robustness, and platform-scale architecture.

End-to-end design of a real-time fraud detection system - covering feature engineering, imbalanced learning, streaming scoring, delayed labels, and graph-based fraud ring detection.

End-to-end design of a recommendation system serving billions of items to millions of users - covering two-stage architecture, candidate generation, ranking, cold start, and serving at scale.

End-to-end design of a production search ranking system - covering query understanding, BM25 + dense retrieval, Learning to Rank, semantic reranking, and A/B testing metrics.

Master data parallelism (DDP, FSDP), tensor parallelism, pipeline parallelism, 3D parallelism, gradient accumulation, all-reduce communication, and bandwidth requirements for training large models.

Deploying ML models to smartphones, IoT devices, and embedded systems - model compression, edge runtimes, OTA updates, federated learning, and real-world examples.

Learn how event sourcing enables auditable, reproducible ML systems - covering the event log, Kafka as an event store, temporal queries, and the projection pattern.

Event sourcing and CQRS patterns for ML systems - event-driven state management, Kafka Streams for ML pipelines, event schema design, dead letter queues, and event replay for debugging.

Designing ML systems around events - event sourcing, CQRS for feature stores, the outbox pattern, and how LinkedIn's unified messaging platform drives ML at scale.

Design and govern ML experiment tracking at scale - from MLflow architecture to organizing 50 data scientists' experiments without chaos.

How to design statistically rigorous experiments for ML systems - Bayesian vs frequentist A/B tests, network interference, interleaving, switchback experiments, and guardrail metrics.

Build a shared feature platform that eliminates cross-team feature duplication, ensures training-serving consistency, and serves fresh features at millisecond latency.

How feature stores solve training-serving skew with a dual-store architecture - offline store for training, online store for serving, and point-in-time correct retrieval.

How recommendation systems create self-reinforcing feedback loops, how to detect them, and how inverse propensity weighting and exploration strategies break them to enable unbiased learning.

Real-time payment fraud detection at Stripe scale - rule-based baselines, graph fraud detection, session-level features, adversarial robustness, and false positive cost analysis.

Understand CUDA cores vs Tensor Cores, GPU memory hierarchy, FLOPS vs memory bandwidth, the roofline model, warp execution, and NVLink - the hardware knowledge that drives ML optimization.

Systematically reduce GPU infrastructure costs with spot instances, GPU sharing via MPS and MIG, right-sizing, reserved instances, efficient batching, utilization monitoring, and GPU marketplace strategies.

Master VRAM capacity planning, activation checkpointing, mixed precision training, ZeRO optimizer stages, CPU offloading, and OOM debugging for production ML workloads.

Combine BM25 keyword search with dense vector search using SPLADE, Reciprocal Rank Fusion, and learned sparse models to build retrieval systems that beat pure semantic search.

Reduce LLM inference costs by 60–80% through quantization, intelligent batching, right-sizing, and autoscaling - turning an $80K/month bill into $20K.

Horizontal and vertical scaling for ML inference - autoscaling policies, KEDA with custom GPU metrics, spot instances, global load balancing, and handling traffic spikes.

Use Kubernetes as ML infrastructure - from GPU scheduling and device plugins to Kubeflow Pipelines and autoscaling - migrating ML workloads from VMs to K8s without disruption.

Lakehouse architecture for ML systems - Delta Lake, Apache Iceberg, Apache Hudi, medallion architecture, query engines, and ML pipelines on the lakehouse.

Master Lambda and Kappa architecture - the two dominant patterns for building ML systems that handle both historical and real-time data at scale.

Deploying Llama-3-70B for a 100K DAU application - vLLM serving, tensor parallelism, KV cache management, speculative decoding, LoRA serving, cost management, and RAG integration.

Understanding the fundamental tension between latency and throughput in ML serving - Little's Law, tail latency, batching strategies, and caching for production ML systems.

End-to-end design of a production LLM-powered product - covering the serving stack, prompt management, RAG architecture, multi-LLM routing, streaming, cost management, and observability.

Engineering ML predictions under 10ms p99 - hardware choices, model optimization, batching strategies, pre-computation, memory layout, and real production targets.

Engineering for ultra-low latency inference - NUMA awareness, CPU affinity, memory pre-allocation, lock-free data structures, cache line optimization, zero-copy inference, CUDA streams, and kernel profiling.

Master pre-filtering vs post-filtering, the ACORN algorithm for filtered HNSW, namespace sharding for multi-tenancy, payload index design, and performance impact of filters in vector databases.

Learn when and how to decompose ML systems into microservices - covering feature services, model services, service mesh, gRPC, and circuit breakers.

Learn to build a complete ML cost model - from compute and storage to hidden data transfer costs - so your team never gets blindsided by a $300K quarterly cloud bill.

Designing an internal ML platform for a team of 50 data scientists - feature stores, experiment tracking, model registry, serving infrastructure, and platform adoption strategies.

Learn how to design internal ML platforms that enable data scientists and engineers to train, deploy, and monitor models efficiently - covering platform components, build vs buy, and real-world case studies.

Build iron-clad ROI cases for ML investments - from quantifying recommendation system value to attributing A/B test results to long-term business outcomes.

Understand the MLOps maturity model from Level 0 to Level 3, design the components of a complete ML platform, and build a realistic 12-month roadmap from ad-hoc to automated.

Compiler-level optimizations for ML inference - TensorRT, torch.compile, ONNX export, kernel fusion, layer fusion, XLA, and profiling bottlenecks.

Analyze the accuracy-cost Pareto frontier to determine when model improvements are economically justified - and how to build the business case for the current model being cost-optimal.

Build production model monitoring infrastructure that catches data drift, prediction drift, and concept drift - detecting model degradation within 24 hours instead of two months.

How quantization reduces model size and inference latency - from FP32 to INT8 to INT4 - covering PTQ, QAT, GPTQ, AWQ, and GGUF with accuracy tradeoffs.

Design a model registry that enables 3-minute rollbacks, full model lineage, and controlled staging-to-production promotion - turning model lifecycle management from a manual process into a reliable system.

Master the foundational principles of AI system design - from requirements gathering to distributed systems theory applied to machine learning.

Production patterns for serving ML model predictions - from protocol choice and batching to quantization, compilation, caching, and autoscaling.

Architecture patterns for real-time machine learning - from sub-10ms inference at scale to online learning, streaming inference pipelines, and ultra-low-latency optimization.

A deep dive into the architectural patterns that power production ML systems - from Lambda/Kappa to multi-tenant platforms.

Real-world end-to-end case studies of production ML systems - recommendation, search, fraud, content moderation, ad click prediction, and LLM-powered products.

Build the internal platform that lets data scientists ship models to production in days, not months - covering MLOps architecture, experiment tracking, CI/CD for ML, and Kubernetes-native ML infrastructure.

A complete map of the Data Infrastructure module covering data lakes, Spark, Kafka, feature stores, data quality, Delta Lake, and lakehouse architecture for ML.

Master vector similarity search, ANN algorithms, embedding pipelines, hybrid search, and production vector database deployment.

Master GPU architecture, memory management, distributed training, fault-tolerant clusters, TPU workloads, inference hardware, and cost optimization for ML infrastructure.

Master AI infrastructure economics - from cost modeling to FinOps culture - so you can build powerful systems without burning your budget.

Production monitoring for ML serving - inference latency histograms, GPU metrics, throughput monitoring, error rates, distributed tracing with OpenTelemetry, and drift detection.

How to serve hundreds of models efficiently - model multiplexing, ensembles in production, A/B testing infrastructure, shadow mode, canary deployments, and multi-tenant GPU resource isolation.

How production ML systems share representations across multiple objectives simultaneously - covering hard vs soft parameter sharing, loss balancing, gradient conflicts, and negative transfer detection.

Learn how to design ML platforms that safely serve multiple teams from shared GPU infrastructure - covering Kubernetes isolation, fair scheduling, data isolation, cost attribution, and quota management.

How to compute ML features at request time without blowing your latency budget - caching strategies, vectorized computation, and production patterns.

Continuous learning in production - online learning vs mini-batch, concept drift adaptation, Vowpal Wabbit, streaming gradient descent, bandit algorithms, and preventing catastrophic forgetting.

How to design Retrieval Augmented Generation systems for production - from naive RAG to advanced pipelines with chunking strategies, hybrid search, reranking, and RAG evaluation.

Computing ML features from raw events within milliseconds - Redis patterns, sliding window aggregations, session detection, and Uber's Michelangelo real-time pipeline.

Architecture for ML inference at 1M QPS with sub-10ms SLA - synchronous vs async real-time, circuit breakers, fallback models, and timeout budget management.

End-to-end system design for YouTube-scale video recommendation - candidate generation, multi-stage ranking, post-processing for diversity, cold start, and session modeling.

Learn how to build fully reproducible ML systems - covering the reproducibility stack, DVC, MLflow, Docker, seed management, GDPR compliance, and financial model audits.

How to gather, prioritize, and translate business requirements into technical specifications for ML systems - including latency budgets, SLOs, and ML-specific constraints.

A production engineer's guide to choosing between REST and gRPC for ML APIs - protocol mechanics, performance trade-offs, and when each wins.

Master monitoring, capacity planning, index building strategy, warm-up, disaster recovery, index versioning, gradual rollout, and cost optimization for production vector database operations.

Architect horizontal sharding, replication, consistent hashing, hot-cold tiering, distributed HNSW, geographic distribution, and backup strategies for production vector databases at billion-vector scale.

Redesigning an Elasticsearch-only search system with neural search - from BM25 baseline through dense retrieval, learning to rank, query understanding, and search quality evaluation.

Build ML platforms that data scientists actually use - applying product thinking to internal tooling, from user research and notebook-to-production workflows to adoption metrics and guardrails.

How to choose the right serving protocol for ML models - REST, gRPC, and WebSocket compared across latency, throughput, streaming, and operational complexity.

How to validate new ML models on real production traffic without affecting users - traffic mirroring, prediction comparison, and graduation criteria.

Compare AWS Inferentia/Trainium, NVIDIA L4/L40S, edge inference hardware (Jetson, Apple Neural Engine), hardware-specific quantization, and cost-performance tradeoffs for production AI inference.

Continuous feature computation on unbounded data streams using Apache Flink - windowing, watermarks, state management, and production ML feature pipelines.

How Apache Kafka and Flink enable real-time ML features - topics, consumer groups, exactly-once semantics, streaming feature computation, and architecture patterns.

Running ML inference on data streams - Kafka integration, Flink ML, stateful stream processing, windowed feature aggregations, exactly-once inference, and time semantics.

When to use synchronous versus asynchronous inference patterns for ML systems - queue architectures, streaming, timeout handling, and production trade-offs.

Engineering time-based features for real-time ML - recency-weighted features, session features, sliding window aggregations, point-in-time joins, temporal leakage prevention, and clock skew in distributed systems.

A structured 4-step framework for approaching ML system design interviews and real production projects - from requirements to deep dive.

Deep dive into Google TPU v4/v5 architecture, systolic arrays, XLA compilation, TPU pods, JAX programming model, cost comparison with GPUs, and when TPUs outperform GPU clusters.

Reduce model training costs by 60–80% through spot instances, gradient checkpointing, mixed precision, and compute-optimal training - without sacrificing accuracy.

Build fault-tolerant GPU training clusters with InfiniBand, NCCL collective operations, Slurm and Kubernetes job scheduling, elastic training, and automatic checkpointing for multi-day training runs.

Production-grade ML serving frameworks - NVIDIA Triton's dynamic batching and multi-backend support, TorchServe's PyTorch-native serving, and when to use each.

How dual encoder architectures power billion-scale recommendation and search by separating user and item representations and querying them with approximate nearest neighbor search.

Systematic comparison of the major vector databases - architecture, managed vs self-hosted, hybrid search, filtering, update performance, consistency, and cost.

Master cosine similarity, dot product, L2 distance, exact vs approximate search, the curse of dimensionality, and how to evaluate vector search quality with recall@K.