{"slug": "how-to-eliminate-training-serving-skew-with-a-unified-real-time-streaming-ml", "title": "How to Eliminate Training-Serving Skew With a Unified Real-Time Streaming ML Pipeline (2026 Guide)", "summary": "Confluent released a 2026 guide recommending a unified streaming (kappa) architecture to eliminate training-serving skew in ML pipelines, citing DoorDash's 35.7% feature-value mismatch and Netflix's $93 million dual-pipeline cost. The approach uses Apache Flink for single-pass feature computation with dual writes to offline and online stores, reducing infrastructure costs and improving model accuracy.", "body_md": "New in Confluent Cloud: Making Data & Pipelines Accessible for AI-Ready Streaming | [Learn More](https://www.confluent.io/blog/2026-q2-confluent-cloud-launch)\n\n**The problem.** Predictive ML pipelines that maintain separate batch and streaming code paths for the same features carry training-serving skew, the gap between the features a model was trained on and the features it sees at inference time. Skew silently degrades model accuracy and doubles infrastructure cost.\n\n**The recommendation.** Adopt a unified streaming (kappa) architecture. Compute features once in Apache Flink®, dual-write to an offline store (Apache Iceberg™ or Delta Lake) for training and an online cache for serving. Same transformation logic, same values, both directions.\n\n**The evidence.**\n\n[ DoorDash measured a 35.7% feature-value mismatch](https://careersatdoordash.com/blog/how-to-investigate-the-online-vs-offline-performance-for-dnn-models/) in their dual-pipeline setup before unifying.\n\n[ Netflix replaced a $93 million per year dual-pipeline backfill with a $2 million per year kappa replay](https://sundaram.io/slides/dais22.pdf) over their Iceberg lake.\n\n[ SAS cut annual Apache Kafka® costs by 69%](/customers/sas/) after consolidating on Confluent Cloud; Audacy cut time-to-implementation in half; Michelin saved 8–9 months on time-to-market.\n\n**The build.** Production-grade kappa requires the Kora engine for Apache Kafka® ingestion (GBps+ throughput, 99.99% uptime SLA, Infinite Storage), serverless Apache Flink® with deterministic event-time semantics, exactly-once processing, and in-stream ML inference (ML_PREDICT, AI_COMPLETE), and automated Apache Iceberg™ or Delta Lake materialization for the offline store. The[ Confluent Data Streaming Platform](/) ships all four in a seamlessly unified fully-managed platform.\n\n**Scope.** This guide is for engineering directors, MLOps leads, and platform architects. It covers the reference architecture, the distributed-systems challenges (event-time, state, exactly-once), governance and lineage, the 2026 tooling landscape (Databricks, SageMaker, Tecton, Feast, Confluent), and a decision framework keyed to your actual bottleneck.\n\nThe classic lambda architecture forces engineering teams to build one batch pipeline (think Apache Spark) for an offline feature store and a separate streaming pipeline for online inference.\n\nBecause these pipelines rely on different compute engines and disparate codebases, the features you train on inevitably diverge from what the model sees during live inference.\n\nTraining-serving skew silently degrades model accuracy in production. DoorDash engineers investigating online-offline performance gaps discovered that feature staleness and cached residuals in their dual-pipeline setup[ led to a feature-value mismatch as high as 35.7%](https://careersatdoordash.com/blog/how-to-investigate-the-online-vs-offline-performance-for-dnn-models/). That's measurable predictive power lost to an architecture choice.\n\nAnd it's expensive. Managing two infrastructures doubles compute costs, complicates historical backfilling, and makes debugging feature drift expensive and slow.\n\nNetflix ran the numbers on this. Backfilling historical data by extending Kafka retention to 30 days would[ cost approximately $93 million per year](https://sundaram.io/slides/dais22.pdf). A kappa-style replay running the same transformation logic from their Iceberg data lake?\n\nCommon checks from a [ training-serving skew diagnostic checklist](https://www.linkedin.com/pulse/how-detect-training-serving-skew-production-ml-2026-guide-chawla-vrzkc/):\n\nDuplicate codebases exist for computing the same business metric in both batch and streaming contexts.\n\nDelayed feature rollouts because data scientists must wait for data engineers to rewrite Python batch logic into Java streaming logic.\n\nManual backfills require fragile, days-long orchestration jobs across multiple data warehouses and object stores.\n\nFeature value mismatches routinely trigger model performance alerts shortly after deployment.\n\nKappa architecture replaces the dual-pipeline model with a single streaming layer that serves both historical replay for offline training and low-latency feature serving for online inference. It is structurally simpler. You eliminate the batch layer entirely. All data, historical and real-time, becomes an unbounded stream of events.\n\nThe feature-as-code principle is simple: define your feature transformations once in a stream processing engine like Apache Flink®, and you get parity between batch and real-time execution automatically. The logic that aggregates user clicks over a 30-day window for training is the same as that used to run the real-time feature update when someone clicks a button in production.\n\nThe same unified stream populates the offline store, which ensures structural consistency between training and serving features.\n\nReplay works in two modes. For low-latency debugging and short-term recovery, rewind Kafka topic offsets and replay recent data. This is inexpensive thanks to the Kora engine's Infinite Storage, which tiers cold segments to object storage automatically. For cost-effective, large-scale historical training and massive backfills, replay directly from Apache Iceberg™ or Delta Lake in cloud object storage, where columnar format wins on scan cost.\n\nThe shift is conceptual as much as architectural. ML teams move from static databases updated by scheduled jobs to immutable events processed continuously by stateful stream operators. Discrete batch windows give way to continuous processing.\n\nTo eliminate training-serving skew by design, you need a concrete reference architecture that standardizes how raw events become ML features. This architecture maps to the four capabilities of the[ Confluent Data Streaming Platform](/) (stream, connect, govern, process) applied to the MLOps lifecycle.\n\nMost ML feature data doesn't start in Kafka. It starts in operational databases, application logs, and SaaS systems. Confluent's managed[ Kafka Connect](https://docs.confluent.io/cloud/current/connectors/overview.html) ecosystem ships more than 120 pre-built connectors, and Debezium-based change data capture (CDC) sources stream row-level changes from Postgres, MySQL, MongoDB, and Oracle into Kafka topics with sub-second latency. The \"connect\" capability is what lets the rest of the architecture treat every feature input as an event stream, regardless of where it originated.\n\nOnce events arrive in the topic, the Confluent Cloud-native Kafka engine ([ Kora](/blog/cloud-native-data-streaming-kafka-engine/)) handles GBps+ throughput,\n\nFlink also runs model inference natively.[ Confluent Intelligence's built-in ML functions](https://docs.confluent.io/cloud/current/ai/overview.html) (ML_PREDICT for invoking remote models, AI_COMPLETE for LLM calls) let you embed prediction directly into the same Flink SQL job that computes the feature, eliminating the network hop between feature serving and inference.\n\nOn the offline path, the streaming backbone continuously populates the data lake. Using[ Confluent Tableflow](https://docs.confluent.io/cloud/current/topics/tableflow/overview.html), you can automate materializing Kafka topics directly into analytics-ready Iceberg or Delta Lake tables (bronze and silver tables in the medallion architecture) with automated file compaction for read performance. Confluent reports a\n\n[ Audacy](/customers/audacy/) describes the same pattern: \"With Flink, we now have the opportunity to shift left and do a lot of early data transformations and computation on our data before it reaches Snowflake. This will optimize our data processing costs to increase the amount of data we have available.\" That is precisely the unified-pipeline shape: features computed once in Flink, materialized to the lake via Tableflow, served online through the same stream.\n\nData scientists query these open table formats using their preferred tools. They can generate training datasets that mirror production data.\n\nOn the online path, the same stream pushes computed features to a low-latency key-value store and triggers event-driven inference services. Computation happens in the streaming layer before data is persisted. The model endpoint accesses fully materialized features in single-digit milliseconds without running any complex aggregations at request time.\n\nThe Confluent Data Streaming Platform owns the upstream pipeline: ingestion (Kora-powered Kafka), processing and in-stream inference (Flink, with ML_PREDICT and AI_COMPLETE), governance (Schema Registry, Stream Catalog, Stream Lineage), and offline materialization (Tableflow to Iceberg or Delta). It does not ship an online feature store or a model-serving gateway. Pair Confluent with the online tier of your choice: Redis Enterprise, Amazon DynamoDB, or Aerospike for the Key-Value layer, and your existing model-serving runtime (SageMaker, Vertex, KServe, BentoML) for the inference endpoint. Confluent's job is to ensure that any feature your model produces is computed identically to the feature it was trained on.\n\nThis reference architecture runs on[ Confluent Cloud](/confluent-cloud/) (more than 100 regions across AWS, Azure, Google Cloud), Confluent Platform (self-managed on-prem or private cloud), Confluent Private Cloud (cloud-native operations behind your firewall), or\n\nA user interacts with an application, generating a raw interaction event that's immediately published to a partitioned Kafka topic.\n\nA Flink application consumes raw events, applies strict event-time watermarks, and joins them with a continuous stream of customer metadata.\n\nFlink calculates a complex sliding-window aggregation to update the user's transaction frequency over the past hour.\n\nFlink pushes the updated feature vector downstream, dual-writing the result.\n\nThe result streams into Confluent Tableflow, which appends or upserts the record to an Iceberg or Delta Lake table in cloud storage for future model training.\n\nSimultaneously, the result is pushed via a sink connector to an online Redis cluster, updating the cache in real time.\n\nThe ML inference gateway receives the prediction request, fetches the pre-computed feature from Redis with single-digit millisecond latency, and returns the real-time score.\n\nThose seven steps describe the steady-state architecture. Three distributed-systems challenges determine whether it is production-grade: how it handles out-of-order events, how it scales window state, and how it recovers from failure.\n\nThe most critical distinction in streaming ML is the difference between event-time and processing-time.\n\nML models require strict event-time correctness to prevent future data leakage during training. If a pipeline processes events based on wall-clock time when they arrived at the server, network delays will inevitably place events into the wrong feature windows.\n\n[ Apache Flink solves this using watermarks](https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/). Watermarks are programmatic assertions that all events up to a given timestamp have been observed. They enable deterministic replay, producing consistent feature values across both real-time processing and historical backfills.\n\nConfluent's serverless Flink ships built-in watermark strategy implementations and event-time semantics out of the box, removing the need to build the leakage-prevention layer yourself.\n\nSliding-window aggregations create write amplification. An event might belong to multiple simultaneous windows, requiring the system to retain and update massive amounts of intermediate state.\n\nModern stateful stream processing handles this by relying on embedded local storage. It spills state to disk and manages compute resources efficiently for per-entity ML features without overwhelming system memory.\n\nConfluent's managed Flink handles state backends, incremental checkpointing, and per-job Confluent Unit for Flink (CFU) autoscaling, with no infrastructure to operate and no state-layer capacity planning.\n\nExactly-once semantics is non-negotiable for feature accuracy.\n\nIf a stream processor crashes and restarts, at-least-once processing will re-process messages. That leads to double-counting in aggregations like transaction frequency.\n\nApache Kafka integrates with Apache Flink to enable exactly-once processing through a distributed two-phase commit protocol. Flink coordinates checkpoints across all operators. The Kafka sink only commits the transaction once a global snapshot is successfully recorded. Feature integrity preserved.\n\nConfluent's managed Kafka and Flink deliver this exactly-once guarantee end-to-end, with the two-phase commit coordinator already running for you.\n\n|\n|\n|\nLate-arriving events | Corrupts time-windowed features and causes training data leakage. | Implement event-time processing with Flink watermarks to handle out-of-order data deterministically. |\nOverlapping sliding windows | Exhausts memory due to massive state amplification across millions of users. | Use embedded state backends and incremental checkpointing to manage vast local state efficiently on disk. |\nNode failures and restarts | Causes double-counting in aggregations, destroying metric accuracy. | Enforce end-to-end exactly-once semantics using the Kafka-Flink two-phase commit protocol. |\n\nUnified data pipelines require unified data governance: quality enforcement at ingestion, not after the fact.[ Stream Governance](/product/stream-governance/) organizes that work around three pillars (Stream Quality, Stream Catalog, Stream Lineage) and ships with the compliance posture (SOC 2, ISO 27001, PCI DSS, HIPAA) that regulated MLOps teams need to pass security review.\n\nBeyond data correctness, unified streaming changes how ML pipelines are *operated*. Batch feature jobs fail silently. A DAG gets disabled, a cron drifts, a backfill quietly skips a partition, and the model degrades for a week before anyone notices. Streaming jobs are long-running services. They expose Prometheus metrics, run under [ Kubernetes-style orchestration](https://thesystemguide.com/a-guide-to-modern-application-orchestration), fit standard service-level objective (SLO) and on-call practices, and ship through the same CI/CD pipelines as the rest of the application stack. For ML platform teams trying to bring SRE discipline to feature pipelines, this shift, from \"scheduled jobs that might have run\" to \"running services with uptime guarantees,\" is at least as valuable as the parity guarantee.\n\nEnforcing data contracts at ingestion is mandatory. [ Schema Registry](http://docs.confluent.io/cloud/current/sr/index.html) holds versioned schemas for Avro, Protobuf, and JSON Schema; data contracts encode the semantic rules and business logic; and broker-level schema validation rejects incompatible payloads before they enter the topic. If a software engineer changes a user-ID field from an integer to a string, the producer's write fails immediately rather than silently corrupting downstream feature computation. Client-side field level encryption (CSFLE) lets you encrypt specific fields, such as personally identifiable information (PII), payment data, and health data, before they ever leave the client, so sensitive columns never touch the broker in plaintext.\n\nStream Catalog turns Kafka topics into searchable, ownable data products. The Data Portal UI lets data scientists discover existing feature streams, see ownership and freshness metadata, request access, and stop rebuilding what an adjacent team already shipped. REST and GraphQL APIs expose the same metadata for programmatic feature-store integration.\n\nCompliance and debugging require comprehensive stream lineage. MLOps and auditing teams need to track exactly how a raw transaction event was transformed into an aggregated feature, which offline table it was materialized into, and which specific ML model consumed it during inference. Stream Lineage visualizes that flow end-to-end, so debugging feature drift or assessing the blast radius of a schema change takes minutes rather than days of grepping logs across distributed systems.\n\nThe tooling landscape for ML data pipelines has fractured. Engineering leaders now navigate overlapping capabilities across platforms: lakehouses, AWS-native platform as a service (PaaS), dedicated feature platforms, and unified streaming runtimes. The question is rarely \"which one tool.\" Rather, it's \"which combination, and what each one actually owns.\" Across the major options, only one combination delivers write-once feature parity, sub-millisecond latency, and managed compute in a single stack.\n\n|\n|\n|\n|\n|\n|\n| A few ms up to 300 ms (Real-Time Mode) | 15–20 ms feature retrieval; 50–60 ms total | Low single-digit ms (managed) | Depends on your own compute | Sub-millisecond with KV cache |\n| High (Spark + Unity Catalog) | Extreme (AWS-only services) | Medium (managed SaaS) | Low (open source) | Low (Apache Kafka + Flink, multi-cloud) |\n| Yes (unified Spark API; sub-ms SLAs still require an external KV store) | Partial (depends on ingestion pipeline) | Yes within Tecton SDK | Yes, with caveats (On-Demand + experimental Streaming Transformations; you operate the underlying compute) | Yes (single Flink job feeds both paths) |\n| Unity Catalog | AWS-native (SageMaker Lineage, Unified Studio with visual DAGs and schema history, CloudWatch, IAM) | Feature monitoring; Time-Travel debugging for point-in-time feature reconstruction | OpenLineage + Web UI; drift and freshness monitoring require an external tool (Arize, WhyLabs) | Stream Governance with broker-level schema enforcement, Schema Registry, Stream Catalog, and end-to-end Stream Lineage |\n| Separate endpoint hop | Separate endpoint hop | Feature serving only | None | ML_PREDICT, AI_COMPLETE inside the Flink job |\n\nDatabricks ships a unified Spark API for batch and streaming, paired with Real-Time Mode for online serving. For Lakehouse-first teams heavily invested in Apache Spark and Unity Catalog, this is a write-once feature path that fits inside their existing stack. The latency floor is the constraint: Real-Time Mode shows[ p99 latencies ranging from a few milliseconds up to 300 milliseconds](https://www.databricks.com/blog/introducing-real-time-mode-apache-sparktm-structured-streaming), depending on transformation complexity. Strict sub-millisecond SLAs for high-frequency trading or live fraud detection? You will need to front Databricks with a separate key-value store like Redis, which reintroduces the architectural complexity the Lakehouse promised to eliminate.\n\nAmazon SageMaker paired with Amazon Kinesis provides a cohesive, heavily managed AWS-native option. The SageMaker Feature Store handles both offline and online feature serving, with online retrieval at[ p99 of 15–20 ms and total inference overhead of 50–60 ms](https://stackoverflow.com/questions/76205646/high-latency-observed-while-calling-aws-sagemaker-and-online-feature-store-servi). SageMaker Lineage and Unified Studio cover the governance side with visual DAGs and schema history across Spark and Glue jobs. The lock-in cost is the real constraint: this stack significantly limits your ability to ingest and process multi-cloud or hybrid data streams without routing everything back through AWS proprietary services.\n\nDedicated feature platforms manage feature definitions, registry, monitoring, and serving. Tecton is the premium managed option. Feast is the flexible open source alternative. Feast supports On-Demand Transformations and experimental Streaming Transformations through a[ Flink provider](https://docs.feast.dev/), and ships native OpenLineage integration with a refreshed Web UI for catalog and lineage views. Two constraints apply. Operationally, Feast specifies the transformation, but you provide and operate the underlying Spark, Flink, or Ray cluster that executes it. For monitoring, drift and freshness still require an external tool such as Arize or WhyLabs. Tecton ships compute, monitoring, and Time-Travel debugging for point-in-time feature reconstruction, useful for explaining why a model produced a specific prediction. The trade-off is vendor dependency on top of whatever stream layer you already run.\n\nThe Confluent Data Streaming Platform handles the full lifecycle from a single layer: Kora-powered Kafka ingestion (GBps+ throughput, 99.99% uptime SLA, Infinite Storage), serverless Apache Flink for stream processing (ANSI SQL, Python, or Java; usage-based CFU pricing), Tableflow for offline materialization to Apache Iceberg or Delta Lake, and Stream Governance for schema enforcement, cataloging, and lineage. Built-in ML functions (ML_PREDICT for invoking remote models, AI_COMPLETE for LLM calls) run inference inside the same Flink job that computes the feature, eliminating the network hop between feature serving and inference.\n\nThose ML capabilities ship under[ Confluent Intelligence](/product/confluent-intelligence/), Confluent's AI layer for the streaming platform. It bundles three things relevant to ML pipelines: invocation functions (ML_PREDICT, AI_COMPLETE) that call remote models and LLMs from Flink SQL, built-in models that run locally inside the Flink task managers (DETECT_ANOMALIES and similar) so common patterns get zero-hop inference, and real-time grounding primitives for agentic AI use cases that need fresh streaming context. For predictive ML pipelines, the practical effect is that feature engineering and inference stop being two systems with a network hop between them; the same Flink job that computes the feature can score it.\n\nThat changes the build-versus-buy question on feature platforms. In a lambda world, you needed Tecton or Feast on top of your streaming layer to get parity, registry, and serving. In a Confluent-backed kappa world, the streaming layer already gives you write-once feature logic in Flink, schema-enforced contracts at ingestion, lineage from raw event to materialized feature to model, and Iceberg and Delta-shaped offline tables your data scientists can query directly. Teams whose feature complexity stays inside Flink-expressible aggregations can skip a separate feature platform and consolidate on Confluent; teams with heavy existing Tecton or Feast investment keep them and integrate, with Confluent as the streaming, governance, and offline-materialization layer underneath.\n\n**Feature freshness is the bottleneck (Sub-10ms SLAs) ** (dynamic pricing, in-session recommendations, real-time payment fraud): Go streaming-first. Use Confluent + Flink to compute features in flight and push them directly to a key-value store like Redis Enterprise, DynamoDB, or Aerospike to meet sub-millisecond SLAs.\n\n**Pipeline fragmentation is the bottleneck** (data engineering and data science maintaining duplicate batch and streaming codebases): Unify in Flink before adding new tooling. Migrate feature engineering to Flink SQL, then evaluate whether a separate feature platform still adds value.\n\n**Already deep in the AWS data stack and willing to absorb the lock-in**: SageMaker + Kinesis is the path of least resistance, accepting 50–60 ms inference overhead and limited multi-cloud flexibility.\n\n**Already deep in Databricks and your latency budget tolerates 50–300 ms**: Databricks Real-Time Mode plus Unity Catalog stays coherent, with a Redis sidecar for the strictest SLAs.\n\n**Mature ML org with rich feature catalog needs and specialized governance**: Tecton on top of a streaming layer (Confluent or DIY Flink) gives you the catalog and serving primitives without rebuilding them.\n\nBridging the gap between offline model training and online inference isn't about buying a single ML tool. Implement a unified streaming backbone based on the kappa architecture, and you structurally address training-serving skew. The same event streams and Flink processing jobs powering your business analytics can, and should, power your ML feature stores and real-time inference gateways.\n\nUnifying your ML data pipelines requires reliable stream processing. The[ Confluent Data Streaming Platform](/) gives you all four pieces in one stack: Kora-powered Kafka ingestion at GBps+ throughput with a 99.99% uptime SLA and Infinite Storage,\n\nConfluent customers see this play out in production. SAS reported a[ 69% reduction in annual Kafka costs](/customers/sas) after consolidating on Confluent Cloud, Michelin estimated\n\nNew developers get[ $400 in Confluent Cloud credits in the first 30 days](/get-started/), enough to stand up a unified streaming feature pipeline against your own data, run a Flink job that computes a feature and calls ML_PREDICT, materialize the result through Tableflow into an Iceberg table, and prove the architecture against your own SLAs before you commit.\n\nTraining-serving skew happens when offline training features are computed differently from online serving features, causing the model to see different data distributions in production and reducing accuracy.\n\nThe architecture computes features once from the same immutable event streams and uses the same transformation logic for both offline training datasets and online feature serving or inference.\n\nYes. Most teams still need an offline store for training and backtesting. But the offline store should be materialized from the same stream to preserve parity with online features.\n\nEvent-time uses the timestamp when the event occurred. Processing-time uses when the system processed the event. ML features must use event-time. Processing-time corrupts windowed features whenever network delays place events in the wrong window, and breaks deterministic replay.\n\nWatermarks define how the system handles late events and ensure that windowed aggregations are deterministic, allowing the system to wait for late-arriving events and place them in their correct historical windows. This ensures that replaying historical data produces the exact same feature values as real-time processing, which is critical for preventing data leakage and ensuring reproducible model training\n\nExactly-once ensures each event affects the feature state only once, even after failures. It's critical for count, sum, and rate features where duplicates would corrupt training data and online predictions.\n\nReplay the same streaming transformations over historical data. For recent windows, rewind Kafka offsets, which is cost-effective thanks to the Kora engine's Infinite Storage tiering cold segments to object storage. For massive historical backfills, replay from Iceberg or Delta tables via Tableflow, where columnar format wins on scan cost. For state-heavy features (e.g., long sliding windows, per-user counters), the more efficient approach is to bootstrap Flink state directly from a batch job using the[ State Processor API](https://nightlies.apache.org/flink/flink-docs-stable/docs/libs/state_processor_api/). Process historical data with batch semantics, write state snapshots, and start the streaming job from those snapshots instead of replaying every event.\n\nChoose kappa when you need parity, simpler operations, and real-time features. Lambda is typically chosen when legacy batch systems dominate, but it increases skew risk and code duplication.\n\nYes. Streaming computes and updates features, while Feast or Tecton can provide feature definitions, registry, and serving integration on top of that backbone.\n\nPrecomputed features in a key-value store (e.g., Redis) are retrieved in single-digit millisecond time. End-to-end inference latency depends on the model endpoint but is far lower than that of micro-batch alternatives in practice.\n\nLate or out-of-order events, state growth from windowed aggregations, and duplicate processing after failures. Mitigate with event-time processing and watermarks, robust state backends and checkpointing, and exactly-once semantics.\n\nSchema enforcement prevents breaking changes at ingestion. Lineage helps trace which events and transformations produced a feature used by a specific model prediction.\n\nYes. Confluent Intelligence's built-in ML functions (ML_PREDICT for invoking remote models, AI_COMPLETE for LLM calls) run inference inside the same Flink SQL job that computes the feature. Confluent also offers **Built-in ML Functions** (such as DETECT_ANOMALIES) that run locally on the Flink task managers. This provides **zero-latency** inference for common patterns, only requiring an external \"hop\" to a model-serving gateway (SageMaker, KServe) for proprietary or large-scale custom models.\n\nBatch CDPs can't capture user intent as it forms. By the time a nightly sync runs, the moment is gone. This guide covers the streaming architecture behind real-time personalization, from sub-100ms ad bidding to cross-channel orchestration, with recommendation patterns built on Kafka and Flink.\n\nEU AI Act obligations for high-risk systems hit in August 2026. Stateless agent frameworks can't satisfy them. This guide covers seven types of state compliant agents must maintain, four streaming patterns for auditability, and a reference architecture using Kafka and Flink as the control plane.", "url": "https://wpnews.pro/news/how-to-eliminate-training-serving-skew-with-a-unified-real-time-streaming-ml", "canonical_source": "https://www.confluent.io/blog/eliminate-training-serving-skew-mlops/", "published_at": "2026-06-23 19:06:15+00:00", "updated_at": "2026-06-24 01:00:06.343438+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence", "mlops", "ai-infrastructure", "ai-tools"], "entities": ["Confluent", "Apache Flink", "Apache Iceberg", "Delta Lake", "DoorDash", "Netflix", "Apache Kafka", "SAS"], "alternates": {"html": "https://wpnews.pro/news/how-to-eliminate-training-serving-skew-with-a-unified-real-time-streaming-ml", "markdown": "https://wpnews.pro/news/how-to-eliminate-training-serving-skew-with-a-unified-real-time-streaming-ml.md", "text": "https://wpnews.pro/news/how-to-eliminate-training-serving-skew-with-a-unified-real-time-streaming-ml.txt", "jsonld": "https://wpnews.pro/news/how-to-eliminate-training-serving-skew-with-a-unified-real-time-streaming-ml.jsonld"}}