Videos LrGCT7G_rU8

Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

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AI Engineer· published 2026-06-29· 0:14:40· en-US· indexed 2026-08-11 05:51

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33 shot(s).

keyframes kept every frame deduplicated

What was stored

cues: 146; whisperx 146
chunks: 26; from 146 cues
keyframes: 17; kept of 33 captured
frames with text: 17; 319 lines read
chapters: 0; from the source metadata
keyframe bytes: 7.2 MB; word timings on 146 cues

Provenance

Each pipeline stage, its state and the model that produced it
stage	state	model	started	took
`fetch`	done	—	2026-08-11 05:48	1m 26s
`stt`	done	—	2026-08-11 05:49	15s
`chunk`	done	—	2026-08-11 05:50	0s
`text_embed`	done	—	2026-08-11 05:50	0s
`keyframe`	done	—	2026-08-11 05:50	1m 20s
`ocr`	done	—	2026-08-11 05:51	8s
`frame_embed`	done	—	2026-08-11 05:51	3s

Frames, and what the machine read

0:05 #0 done6 line(s)

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1. Using RL-based Agent to1.00
2. Detect and Remediat0.99
3. Presenter:1.00
4. Anna Marie Benzon1.00
5. University of the Philippines, Diliman1.00
6. github.com/ambenzon27/rl-etl-remediation-agent1.00
0:27 #1 done7 line(s)

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2. Detect and Remediate0.99
3. ETL Pipeline Failures0.98
4. Presenter:1.00
5. Anna Marie Benzon1.00
6. University of the Philippines, Diliman1.00
7. github.com/ambenzon27/rl-etl-remediation-agent1.00
0:38 #2 skipped

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6. Validate1.00
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8. The1.00
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10. X0.95
11. Problem1.00
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18. STEN0.90
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31. Solution1.00
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33. 4.Select a bounded remediation action1.00
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11. Error classifier0.99
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13. Risk scorer1.00
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shot 13·sharpness 2852.3
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2. The Learned Policy Does Not Have Final Authority1.00
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5. 3.Unsafe passive actions are overridden0.99
6. 4.High-risk and unknown conditions escalate1.00
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34. confidence1.00
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28. 4.Four controlled experiments: El-E40.99
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33. 6.Results reported with 95% confidence0.99
34. Public benchmark and tests are1.00
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33. external guardrails produced1.00
34. most of the reliability1.00
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Transcript

146 cues· 1,835 words· 12,067 chars

chunk 0 · 0:00–0:44 · 99 words · 586 chars
0:00 Imagine you are this engineer.
0:03 A production data job failed hours ago.
0:06 The dashboard went stale.
0:08 You have spent all day checking the logs, the schema, and the upstream data.
0:12 And now it is past midnight.
0:15 The same question keeps coming back.
0:18 What changed?
0:19 The failure itself may be small, but the expensive part is everything around it.
0:26 inspection, diagnosis, choosing a safe response, re-running the job, and confirming that we did not make the data worse.
chunk 1 · 0:35–1:17 · 93 words · 587 chars
0:35 Hi, I'm Anna Marie Benzon.
0:37 In this talk, I will show an RL-guided system that selects bounded remediation action for ETL failures.
0:45 The central question is not simply whether an agent can act, but whether it can act usefully, explainably, and within boundaries that an operations team would actually trust.
0:59 Cloud ETL failures rarely arrive as one clean, well-labeled exemption.
1:04 We see late or unavailable sources, schema drift, daytime incompatibilities, null rate spikes, type changes, and runtime errors that do not match anything in the runbook.
chunk 2 · 1:15–1:58 · 93 words · 597 chars
1:15 The usual response is a human workflow.
1:17 Inspect the logs, form a diagnosis, attempt a repair, rerun the job, and validate the output.
1:25 Each step is reasonable.
1:27 The latency comes from handoffs in complete context and the need to avoid an unsafe fix.
1:34 In the capstone evaluation, the manual recovery baseline was modeled at roughly 2.5 working days.
1:41 This represents an incident moving through normal queuing, investigation, and approval.
chunk 3 · 1:47–2:32 · 97 words · 634 chars
1:47 So the engineering objective is specific.
1:50 Compress that loop for routine, recognizable failures while escalating the cases that are uncertain, novel, or high risk.
1:59 This diagram shows the end-to-end AWS architecture from my capstone.
2:04 An existing AWS Glue ETL job emits a job failed event.
2:09 Amazon EventBridge catches that event and triggers the Lambda function that runs the agent.
2:15 Lambda gathers evidence from two read-only sources.
chunk 4 · 2:19–3:00 · 88 words · 550 chars
2:19 CloudWatch provides the error logs, while the Glue Data Catalog provides the current schema metadata.
2:26 The system uses those signals to classify the failure, assess the data quality and operational risk.
2:33 and construct the state pass to the RL decision engine.
2:37 The policy then proposes a bounded response.
2:40 The safety layer checks that proposal before the executor can use the Glue API to re-trigger the job or apply an approved remediation.
chunk 5 · 2:50–3:34 · 81 words · 580 chars
2:50 Amazon S3 stores agent artifacts, audit logs, and quarantined outputs.
2:57 Finally, the job is rerun and validated.
3:01 So this is close operational look, monitor, diagnose, score, decide, check safety, act and verify recovery.
3:12 The capstone implementation use synthetic data provided by the client.
3:17 The public repository preserve this pattern through a sanitized generalized deployment template.
chunk 6 · 3:23–4:01 · 87 words · 595 chars
3:23 The intelligence layer deliberately separates three concerns.
3:27 Deterministic anomaly rules establish observable facts.
3:31 A field disappeared, a tide changed, or the null rate crossed a threshold.
3:35 The Q-learning policy handles contextual action selection.
3:38 Given the current incident state, should the system retry, coerce the schema, rollback, quarantine, escalate, or simply log the event?
3:48 Then, a safety override sits outside the learned policy.
3:52 For example, if the anomaly is critical and the policy proposes a passive action such as logging, the override converts that choice into an escalation.
chunk 7 · 4:02–4:44 · 92 words · 626 chars
4:02 This separation is the design thesis of the project, rules for facts, learning for bounded choices, and guardrails for authority.
4:11 Before selecting an action, the system has to establish what actually happened.
4:15 The schema profiler extracts a structure
4:18 types, nesting, and null rate statistics.
4:21 The drift detector compares the current profiler with the baseline area and identifies additions, removals, and type changes.
4:29 The data quality analyzer checks completeness, validity, and consistency.
chunk 8 · 4:34–5:19 · 94 words · 631 chars
4:34 The error classifier maps log patterns into failure families, and the risk scorer turns those signals into an operational risk level.

Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

Scene timeline

What was stored

Provenance

Frames, and what the machine read

Transcript