Modernization Report — AIINFRA Non-Credit Certificate of Completion (AI Infrastructure & Architecture)

Prepared for: Program-revision team Scope: Regulatory/accreditation compliance, California labor-market justification, and curriculum modernization for the non-credit CDCP Certificate of Completion in AI Infrastructure & Architecture (162h + 162h + 216h = 540h; 10 × 54 contact hours). Evidence basis: Research findings with adversarial verification verdicts. Claims marked refuted have been dropped or corrected; claims marked uncertain are hedged and collected in §7 for human verification.

Evidence note. The initial automated CCCCO research pass returned unusable placeholder data (a search-tool auth failure), so §2 was re-researched by hand against primary Chancellor's Office / Title 5 / Education Code sources on 2026-07-02 and is now verified — see references [C1]–[C6]. The ACCJC (§3), labor-market (§4), and industry/skills (§5–§6) sections rest on verified primary sources and are cited with confidence.

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1. Executive Summary

The certificate has a sound skeleton — Python/Git dev environments, containers/Kubernetes, cloud ML platforms, self-hosting, provider APIs, Model Context Protocol (MCP), and vector-DB/RAG workflows — but it is built around a 2023–2024 "run models locally + basic RAG" worldview and has not kept pace with the 2025–2026 production-agentic-systems stack. The single biggest technical gap is that the program teaches prototyping tools (Ollama, LM Studio, AnythingLLM/Open WebUI, N8N/Langflow/Flowise) as if they were production infrastructure, while a course branded "AI Infrastructure and Architecture" is expected to cover high-throughput inference serving, GPU orchestration at scale, LLMOps observability, guardrails/security, and AI governance [10][12][15][19][20].

What to change (headline recommendations):

1. Keep the non-credit CDCP structure for now, but formally decide the credential path. Non-credit is the fastest, lowest-friction way to ship and update a fast-moving AI curriculum, and it is expressly exempt from ACCJC substantive-change review [5]. The recommendation (§2) is a stackable non-credit → for-credit pathway, mirroring the strongest CCC precedents (Grossmont, De Anza) [22][23].
2. Reframe the "local model" and "low-code" content as dev-only, and add production serving (vLLM/TGI/TensorRT-LLM), GPU orchestration (GPU Operator, DRA, KAI Scheduler), and disaggregated inference (NVIDIA Dynamo) [15][16][17].
3. Add the missing production competencies: agent frameworks (code-first), LLMOps observability/eval, fine-tuning/quantization, modern multi-stage RAG with rerankers and evals, guardrails/security (OWASP LLM Top 10 2025), AI governance (NIST AI RMF / ISO 42001 / EU AI Act), and GPU FinOps [10][12][14][19][20][26].
4. Expand MCP coverage — it is current and correctly placed; MCP is now under the Linux Foundation's Agentic AI Foundation and is an industry standard, so deepen it (server-building, tool security) rather than replace it [11].
5. Anchor SLOs, program review, and disaggregated completion/job-placement tracking from day one to satisfy ACCJC Standard 2 and Eligibility Requirement 11 even though no ACCJC filing is required for a non-credit offering [6][7].
6. Ground the labor-market justification in verified EDD data: Data Scientists (+37.1%) and Computer & Information Research Scientists (+26.1%) are the fastest-growing target occupations statewide, Software Developers is the largest-employment and a top-5 bachelor's-level opening occupation, and Network/Systems Administrators is the lone decliner (−4.5%) — reinforcing a retrain-toward-cloud/MLOps thesis [1][2][3][8][9].

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2. Regulatory & Compliance Update (CCCCO)

Verified section (re-researched 2026-07-02). The items below are sourced to primary Chancellor's Office / Title 5 / Education Code references [C1]–[C6] listed in §9.

2.1 Governing handbook — PCAH 8th Edition (November 2019)

• The controlling reference for CCCCO curriculum/program approval is the Program and Course Approval Handbook (PCAH), 8th Edition (November 2019) — still the current edition as of mid-2026 [C1]. It is organized into credit and noncredit components and governs course/program submission, approval, chaptering, and maintenance. Build the revised program narrative and Course Outlines of Record (CORs) to the PCAH 8th-edition noncredit specifications.
• Approval flows college curriculum committee → district governing board → CCCCO Curriculum & Instruction Unit (Curriculum Inventory submission) [C1][C2]. Noncredit certificates are approved through this CCCCO noncredit process; they are outside ACCJC substantive-change jurisdiction (§3.3) [5] but not outside Title 5 / CCCCO approval — a separate, required track.

2.2 Noncredit certificate types & CDCP enhanced funding

Statutory/regulatory basis: Ed Code §84760.5 (defines CDCP categories), Ed Code §84750.5 (uniform FTES apportionment; §84750.5(d)(4)(A)(i) codifies CDCP funding at credit parity, enacted by SB 860 (2014), effective 2015–16), and Title 5 §55151 (CDCP program approval) [C3][C4].

• Enhanced (credit-parity) funding is available only for noncredit courses in four instructional domains — ESL, Elementary & Secondary Basic Skills, Short-Term Vocational, and Workforce Preparation — and only when the courses are sequenced (≥2) into an approved certificate [C3].
• Certificate of Completion = a course sequence leading to improved employment (short-term vocational / workforce prep). ← Correct classification for the AI Infrastructure certificate.
• Certificate of Competency = a sequence that prepares students to transition into credit coursework (credit certificate, associate degree, or transfer) [C3].
• Bottom line: the 540-hour AI Infrastructure certificate — classified Short-Term Vocational → Certificate of Completion — is CDCP-eligible for enhanced (credit-parity) funding once CCCCO-approved and documented as leading to improved employment with a demonstrated labor-market need (which §4 supplies). This confirms and now properly sources the original proposal's funding premise. Enhanced funding is not automatic — it follows CCCCO approval of the certificate and its course sequence [C3][C4].

2.3 Course Outline of Record (COR) & Distance Education

• Each noncredit COR must meet Title 5 §55002(c) standards (objectives, content, methods of instruction and evaluation, SLOs) and the PCAH 8th-edition noncredit COR fields [C1]. The program already includes a COR template — align it to these fields.
• Distance Education: each course offered online needs a DE addendum (Title 5 §55200 et seq.), and instruction must meet the federal Regular and Substantive Interaction (RSI) standard (34 CFR 600.2) [4]. Note the separate ACCJC 50%-of-courses DE substantive-change trigger in §3.3 [4][5].

2.4 Compliance checklist mapped to existing program sections

2.5 Credential-structure analysis and recommendation

Three options were weighed:

Recommendation: Option C (stackable non-credit → credit). Launch/maintain the current non-credit CDCP Certificate of Completion as the accessible entry tier (preserving the ACCJC non-credit exemption and enhanced CDCP funding, pending category verification), and design the courses so they can later articulate into a for-credit Certificate of Achievement / AS. This mirrors the two strongest verified CCC precedents:

• Grossmont College (Fall 2025) — first CCC Applied AI AS with a noncredit → degree stack (AI Fundamentals and AI Developer non-credit Certificates of Completion → Certificate of Achievement/AS, 32 units), including a dedicated Applied AI in Cloud Computing course [22].
• De Anza College (Fall 2026) — stackable no-cost non-credit foundational certificate → technical certificates (ML, deep learning, NLP, prompt engineering, AI agents) → AS in Applied AI; reported ~222-hour Applied AI Certificate of Completion [23] (medium confidence — see §7).

Credit-conversion thresholds to plan around (Title 5 §55070, as amended 2025) [C5][C5a]: a credit grouping of ≥16 semester units of degree-applicable coursework must be Chancellor-approved and can only be titled "Certificate of Achievement" (transcriptable); only ≥16-unit certificates are federal Title IV financial-aid eligible. A district may optionally submit 8 to <16 semester units for Certificate-of-Achievement approval. Sequences <8 units (or not submitted) are locally-approved certificates that cannot use the "Certificate of Achievement / Completion / Competency" titles or appear on the transcript, and a certificate cannot consist solely of basic-skills courses. (The April 2025 §55070 revision refined wording — "grouping" vs "sequence," the intersegmental-GE definition — but kept these unit thresholds.) Separately, ACCJC treats a new ≥16-unit credit certificate requiring significant institutional resources as an Administrative Approval substantive change; non-credit tiers remain exempt throughout [5].

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3. Accreditation (ACCJC)

3.1 The 2024 Standards touchpoints

The 2024 ACCJC Accreditation Standards (adopted June 2023; June 2025 edition) are organized into four standards: Standard 1 (Institutional Mission and Effectiveness), Standard 2 (Student Success), Standard 3 (Infrastructure and Resources), Standard 4 (Governance and Decision-Making) [6]. Standard 2 governs instructional program design for this certificate:

• 2.1 — academic programs must be in fields of study consistent with the institution's mission, with appropriate breadth, depth, and expected learning outcomes (ER 3, ER 9, ER 12) [6].
• 2.2 — the institution, relying on faculty and other stakeholders, designs and delivers programs reflecting relevant discipline and industry standards (ER 3, 9, 11, 14) — directly supports justifying the AI curriculum against current industry practice (which §5–§6 supply) [6].
• 2.5 — scheduling must ensure certificate programs can be completed in the expected period of time (ER 9) [6].
• 2.9 — the institution conducts systematic review and assessment of program quality (ER 11, 14) — the program-review/SLO engine [6].
• 1.3 and required documentation item 1v — institution-set standards for certificate completion specifically, alongside course completion, degree/transfer, job placement, and licensure pass rates, reviewed via meaningfully-disaggregated data [6].

3.2 SLO / assessment obligations (ER 9 and ER 11)

Eligibility Requirement 11 is the program-level SLO/assessment anchor: publish each program's expected SLOs and any program-specific achievement outcomes, and demonstrate through regular, systematic assessment that completers achieve them — including review of meaningfully-disaggregated data to identify and close achievement gaps [7]. ER 9 requires programs of sufficient content and length, at appropriate rigor, culminating in identified outcomes [7].

Action: Even though a non-credit certificate triggers no ACCJC filing, the college must still (a) publish program SLOs, (b) run a program-review cycle, and (c) track completion and job-placement data disaggregated by student group, in the program's Evaluation section [6][7]. (Caveat: ER 9/ER 11 documentation is framed around degree programs; applying it to a non-credit certificate is a reasonable inference but not named explicitly in the ERs — see §7.)

3.3 Substantive-change implications

• Non-credit programs are exempt from ACCJC's substantive-change process — no notification, administrative approval, or committee review. This is confirmed verbatim in the Substantive Change Manual (May 2026), which lists "Non-credit courses and/or programs" as an exempt category [5]. (Correction per verdict: the manual lists five exempt categories, not four; the load-bearing conclusion — non-credit triggers no substantive-change filing — holds [5].)
• Distance Education exception applies regardless: if 50% or more of the courses are delivered via distance/correspondence education, advance notice through the substantive-change process is still expected, and the first course in a DE modality requires Administrative Approval; federal RSI requirements apply [4][5]. This matters if the certificate is delivered substantially online.
• Teach-out / program discontinuance: ACCJC required-documentation item 2viii requires policies ensuring enrolled students can complete in a timely way if a program is eliminated [6]. Given how fast AI tooling changes, build a teach-out plan into the Implementation section for any future course sunset.
• The specific substantive-change process timelines (ALO inquiry form; 15-day notification acknowledgment; 30/90/180-day administrative-approval windows) are largely confirmed, but the committee-approval payment deadline is internally inconsistent in ACCJC's own manual (Section 2 prose says "within 2 weeks," the flowchart says "at least 30 days") — flagged in §7 [5].

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4. Labor Market Justification (California)

Data sources & vintage. Figures below come from EDD's OEWS wage file (California-Statewide; May 2024 employment estimates with wages escalated to Q1 2025 dollars) and EDD's Long-Term Occupational Employment Projections 2024–2034, cross-referenced with the 2025 California Jobs Market Report [1][2][3]. All eight occupation figures were verified exactly against source [verdicts: confirmed].

BLS-API / OEWS data caveat. (1) The bulk EDD OEWS CSV used here is one release cycle behind the live EDD OEWS page, which now shows May 2025 employment / Q1 2026 wages — so employment counts differ slightly between the OEWS survey file and the projections file (different release vintages); both are legitimate official EDD numbers and are cited separately [9]. (2) There is no standalone SOC code for "MLOps Engineer" or "AI Engineer"; those roles are absorbed into 15-1252, 15-2051, and 15-1299, and any role-specific demand figures come from private vendors (Lightcast), not government statistics — treat as directional only [13]. (3) The statewide 8.8% baseline is on the 2023–2033 cycle while occupation growth rates are 2024–2034 — a minor cross-cycle comparison [8].

4.1 Per-occupation table (California statewide)

4.2 Demand narrative

• The AI/ML-adjacent occupations are the fastest-growing. Data Scientists (+37.1%) and Computer & Information Research Scientists (+26.1%) far outpace the statewide 8.8% all-occupations baseline (2023–2033, >21 million jobs) [8]. Seven of the eight target occupations exceed that baseline [8].
• Software Developers is both the largest and a flagged high-demand occupation. At 290,800 base employment and +18.4% growth, it is one of EDD's named high-demand careers and ranks #2 among all bachelor's-level occupations by projected openings (222,110, 2023–2033 cycle; $175,555 median; 9,838 online job ads June 12–Aug 10 2025 via Lightcast/CalJOBS) [2][3].
• The one decliner reinforces the retraining thesis. Network & Computer Systems Administrators is the only shrinking target occupation (−4.5%), consistent with cloud automation and managed services absorbing traditional on-prem sysadmin work — a direct argument for retraining toward cloud/MLOps-adjacent skills rather than legacy administration [1].
• AI is simultaneously displacing and creating tech roles. EDD's 2025 report attributes part of recent high-tech job losses (Professional & Business Services −179,100; Information −105,100) to "the rapid increase in the integration of artificial intelligence-related cost-saving measures," citing AI-driven layoffs shrinking entry-level jobs [3]. This supports training workers toward higher-value AI-building/AI-operating roles rather than commoditized ones.
• Emerging roles corroborate direction (low confidence). Vendor data (Lightcast) shows generative-AI unique job postings rising from ~55 (Jan 2021) to nearly 10,000 (May 2025), with California/Bay Area a top concentration — directional support for an AI-infrastructure credential, not a primary justification figure [13].

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5. Curriculum Modernization

5.1 Course-by-course disposition

Semester 1 — Foundations

Semester 2 — Deployment

Semester 3 — Advanced

5.2 New courses / modules to fill gaps

1. Agentic AI & Agent Frameworks (code-first) — LangGraph (graph-based), CrewAI (role-based), OpenAI Agents SDK (handoff-based, released March 2025), Pydantic AI (2025 breakout); agent state/graph control flow, tool-calling, handoffs, human-in-the-loop interrupts/approvals, multi-agent orchestration. Teach the cost/efficiency lesson (LangGraph fastest on latency; CrewAI ~3× token footprint on simple flows). Fills the gap left by N8N/Langflow/Flowise, which are visual workflow tools, not production agent frameworks [10].
2. LLMOps: Observability, Tracing & Evaluation — Langfuse (MIT, self-hostable), Arize Phoenix (50+ faithfulness/relevance/toxicity/hallucination metrics), LangSmith, OpenTelemetry/OpenLLMetry, MLflow tracing; the CLEAR evaluation framework (Cost, Latency, Efficacy, Assurance, Reliability). Currently entirely missing [12].
3. High-Performance Inference Serving & GPU Orchestration — vLLM/SGLang/TensorRT-LLM/TGI; Ray Serve, Triton, KServe; continuous batching, PagedAttention/KV-cache, tensor/pipeline parallelism; GPU Operator, DRA, KAI Scheduler (gang/topology-aware scheduling), MIG, Slurm-on-K8s (Slinky), SkyPilot; NVIDIA Dynamo disaggregated serving. The biggest technical gap for a course branded "Infrastructure and Architecture" [15][16][17].
4. Model Adaptation: Fine-Tuning & Quantization — LoRA/QLoRA/PEFT, DPO (has displaced classic RLHF), emerging GRPO; quantization formats GGUF/AWQ/GPTQ/FP8, KV-cache quantization; the heuristic "a larger model at lower precision usually beats a smaller model at higher precision." Single-GPU adaptation is accessible to this exact community-college audience [14][18].
5. Production RAG: Rerankers, GraphRAG & Evals — hybrid search (dense+BM25), query rewriting, cross-encoder rerankers, GraphRAG, agentic RAG; mandatory evaluation with RAGAS and DeepEval (CI/CD quality gates). Replaces naive embed-and-retrieve [10].
6. AI Security & Guardrails — OWASP LLM Top 10 (2025); NeMo Guardrails, Llama Guard 4 (12B multimodal, released Apr 30 2025), LLM Guard/Rebuff/Prompt Guard, Presidio (PII); direct vs. indirect prompt injection. Currently missing; essential given the agentic focus [19].
7. AI Governance & Compliance — NIST AI RMF (methodology), ISO/IEC 42001 (auditable AMS), EU AI Act (binding law; prohibited practices + AI literacy applied Feb 2 2025, GPAI obligations Aug 2 2025, most high-risk requirements Aug 2 2026). Increasingly an employer/hiring requirement [20].
8. GPU FinOps — cost per 1,000 requests (not per GPU-hour); spot/preemptible for batch; quantization + KV-cache savings; right-sizing; fractional GPUs/MIG. Ties together serving, quantization, and orchestration [26] (some FinOps case-study figures are vendor-sourced — see §7).

5.3 Revised program map (162h + 162h + 216h = 540h)

At 54 contact hours per course (the Chancellor's-Office-recommended 18×3 standard) the full 10-course sequence totals 540 contact hours — a 30-unit credit-equivalent (Semesters 1–2 = three courses/162h each; Semester 3 = four courses/216h, including the AIINFRA 303 capstone). Content is modernized and redistributed within this envelope (an accelerated 6-month/Kubernetes-fundamentals variant remains a valid alternate format).

• Semester 1 — Foundations (162h): 100 Intro to AI Systems & Infrastructure (updated) · 101 Dev Environments & Tools (kept) · 102 Containerization & GPU-Aware Orchestration (updated)
• Semester 2 — Serving & Deployment (162h): 200 Cloud Platforms for AI + CI/CD-for-ML (updated) · 201 Production Inference Serving & GPU Orchestration (replaces "Self-Hosting"; Ollama/LM Studio reframed dev-only) · 202 Model Adaptation: Fine-Tuning & Quantization (new; replaces "All-in-One AI Apps")
• Semester 3 — Applied Production Systems (216h): 300 Agentic AI & MCP (merges enterprise APIs + expanded MCP + code-first agents) · 301 Production RAG + LLMOps Observability/Evals (replaces vector-DB/low-code course) · 302 AI Security, Guardrails & Governance (+ GPU FinOps) (new) · 303 Capstone (kept; production-grade, instrumented artifact)

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6. In-Demand Skills & Certifications Alignment

Employer demand for AI Infrastructure Engineer / MLOps / ML Platform / AI/ML Engineer roles in California converges on a consistent stack. Two live California postings verify the direction: Scale AI (Model Serving Platform) requires Python/Go/Rust/C++, Docker/Kubernetes, AWS/GCP + Terraform, and LLM-serving know-how with vLLM/SGLang/TensorRT-LLM as nice-to-haves [24] (salary/URL specifics flagged in §7); Hashlist requires production GenAI experience, MCP servers and agentic AI architectures, and CI/CD-integrated AI tooling [24].

Certification strategy. Following the Coastline College model (AI-for-Cybersecurity AS with NSA CAE-C validation and CompTIA alignment) [22-analog], the certificate can map explicitly to vendor-neutral Kubernetes certs (CKA/CKAD) and cloud ML certs as preferred qualifications. Reported salary premiums are directional (industry compensation analysis, not government wage data): CKA ~18–25%, CKS ~25–35% [24] (see §7 for the specific ML-postings-share figure).

Statewide positioning. The CCC Vision 2030 AI Workplan (July 2025) funds shared infrastructure (a $10M initial + $12M scaling Common Cloud Data Platform) and workforce MOUs with Nvidia, Google, OpenAI, Microsoft, IBM, Intel and others, but does not prescribe a technical AI-infrastructure curriculum — so a college proposing a specific AI-infrastructure/MLOps non-credit certificate is filling a real gap the system has not centrally defined, while aligning with the state's named priorities [27].

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7. Risks & Human-Verification List

A. CCCCO items — now verified (2026-07-02), with residual procedural confirmations

1. §2 was re-researched against primary sources after the automated pass failed, and is no longer an open gap: PCAH 8th Edition (Nov 2019) [C1]; CDCP basis Ed Code §84760.5 / §84750.5 + Title 5 §55151, credit-parity funding per SB 860 [C3][C4]; Certificate-of-Achievement thresholds Title 5 §55070 as amended 2025 [C5][C5a]; Vision 2030 stackable/CPL support [C6]. [resolved]
2. CDCP eligibility classification is correct but approval is procedural. The Short-Term Vocational → Certificate of Completion mapping is right, but enhanced funding still requires CCCCO approval of the certificate + its course sequence; confirm the local submission path with your college Curriculum Committee / Articulation Officer before asserting funding in the proposal. [procedural]

B. ACCJC nuances to verify

1. Committee-approval payment deadline is internally inconsistent in ACCJC's own manual ("2 weeks" in prose vs. "30 days" in the flowchart). Do not cite a single figure without checking the current manual [5]. [uncertain]
2. "Category F" label for the substantive-change category was not verifiable from the retrieved primary source; the manual does not use lettered categories. Do not cite "category F." [uncertain]
3. ER 9 / ER 11 are degree-framed; applying them to a non-credit certificate is a reasonable inference, not explicit text. Present as best practice, not a naming citation [7]. [uncertain]
4. RSI citation set (34 CFR 600.2; Standards 1.4/2.1/2.2/2.6/2.9/3.9) comes from a DE-policy PDF not independently re-retrieved; the RSI requirement itself is well-established [4]. [uncertain]

C. Labor-market / skills figures to hedge

1. MLOps/AI-Engineer demand numbers are vendor-sourced (Lightcast), not government data — directional only [13]. [confirmed as low-confidence]
2. Scale AI posting: technical requirements confirmed, but the cited salary band ($180K–$225K) matches a non-senior listing while a near-duplicate "Senior" listing shows $216,200–$270,250, and the LinkedIn URL digit may be mistyped. Verify exact posting/title/URL before quoting [24]. [refuted as cited — corrected]
3. California ML-postings share: cited as ~29% but at least one source shows 32%. Verify against the actual CompTIA report table before quoting precisely [24]. [uncertain]
4. Certification salary premiums are industry compensation analysis, not government wage data — label directional [24]. [uncertain]

D. Curriculum-claim figures to soften

1. "~80% of RAG failures are retrieval, not generation" — corrected to ~73%; the 80% figure applies to overall RAG project failure rate, a different metric. Use 73% [10]. [refuted — corrected]
2. "60% of new 2026 RAG deployments include eval from day one (up from <30%)" — single aggregator source; treat as unverified [10]. [uncertain]
3. GPU FinOps case study ($39K→$16K/month) and "55–80% of AI GPU spend is inference" — single vendor/marketing source; present as illustrative [26]. [uncertain]
4. "Token consumption grows ~20x by 2030" — closest verified figure is Goldman Sachs' 24× (2026→2030); adjust base year/figure before citing precisely [14/26-adjacent]. [confirmed-with-adjustment]
5. "LoRA/QLoRA cover ~95% of production fine-tuning" and vector-DB scale thresholds (e.g., "Qdrant 500K–50M vectors") are soft rules-of-thumb, not hard statistics [14][25]. [confirmed-as-illustrative]
6. KAI Scheduler "de facto reference" is promotional framing (CNCF Sandbox project); accurate underlying facts, soften the superlative [17]. [confirmed-with-nuance]

E. Comparative/precedent claims to re-confirm

1. De Anza (222h figure), Coastline (NSA CAE-C through 2030), Chaffey/Bakersfield, MiraCosta/Saddleback precedents were medium/low confidence or not independently re-fetched [22][23]. Confirm before citing specific hours/structures. [uncertain]
2. CCC AI Consortium (cccaic.org) was unreachable; member list/curriculum templates unverified — confirm via ASCCC/BACCC [22-analog]. [low confidence]

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8. Phase 2 & Phase 3 Groundwork

Phase 2 — Capstone project research (to tee up). The revised 303 Capstone should require a production-grade, instrumented artifact rather than a prototype: an agentic or RAG system that (a) serves a quantized model via vLLM/TGI, (b) is instrumented with LLMOps tracing (Langfuse/Phoenix) and an automated eval harness (RAGAS/DeepEval), (c) enforces at least one guardrail (NeMo Guardrails / Llama Guard) mapped to an OWASP LLM Top 10 risk, and (d) reports a FinOps metric (cost per 1,000 requests) [12][15][19][26]. Phase 2 research should draft the capstone rubric, define SLO-linked assessment tied to ER 11 [7], and specify facility/GPU-access needs (the CCC Common Cloud Data Platform and NVIDIA/cloud MOUs are potential resources [27]).

Phase 3 — Interactive web conversion (to tee up). The final program-revision proposal (Overview, Requirements, Core Curriculum, COR template, Implementation, Evaluation, Career Pathways, References) is a strong candidate for conversion to an interactive web artifact: a stackable-pathway visualizer (non-credit → credit), a labor-market dashboard driven by the §4 EDD table, a course-modernization matrix (KEEP/UPDATE/REPLACE), and a skills-to-curriculum crosswalk. Phase 3 groundwork: confirm the canonical section list, freeze the verified data tables, and resolve the §7 verification items so the interactive version ships only confirmed claims.

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9. References

1. California Long-Term Occupational Employment Projections 2024–2034 (EDD CSV, lt-occ-emp-2024-2034.csv). https://data.ca.gov/dataset/715d1324-ac02-4b11-b922-86bafa6eb80f/resource/274e273c-d18c-4d84-b8df-49b4d13c14ce/download/lt-occ-emp-2024-2034.csv
2. California OEWS Wage Data (EDD, oews-2009-2025_original.csv). https://data.ca.gov/dataset/6411456b-594b-4b73-af57-ce8dd401f2e2/resource/aef4c53a-7e17-418c-acc1-d189e39b6caa/download/oews-2009-2025_original.csv
3. 2025 California Jobs Market Report (EDD, DE 864 Rev. 3 8-25). https://edd.ca.gov/siteassets/files/pdf_pub_ctr/2025_california_jobs_market_report.pdf
4. ACCJC Policy on Distance Education and on Correspondence Education, June 2025. https://accjc.org/wp-content/uploads/Policy-on-Distance-and-on-Correspondence-Education.pdf
5. ACCJC Substantive Change Manual, May 2026 Edition. https://accjc.org/wp-content/uploads/Substantive-Change-Manual.pdf
6. ACCJC 2024 Accreditation Standards (June 2025 edition). https://accjc.org/wp-content/uploads/ACCJC-2024-Accreditation-Standards.pdf
7. ACCJC Eligibility Requirements for Accreditation, January 2024. https://accjc.org/wp-content/uploads/Eligibility-Requirements-for-Accreditation.pdf
8. EDD Employment Projections page (confirms 2024–2034 vintage). https://labormarketinfo.edd.ca.gov/data/employment-projections.html
9. EDD OEWS Employment and Wage Statistics page. https://labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html
10. RAG in Production 2026: GraphRAG, Hybrid Retrieval, and Evals. https://ailearningguides.com/rag-production-patterns-2026/ · Top 5 Reranking Models (MachineLearningMastery). https://machinelearningmastery.com/top-5-reranking-models-to-improve-rag-results/
11. Donating the Model Context Protocol and establishing the Agentic AI Foundation (Anthropic). https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation · Linux Foundation press release. https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation
12. Best LLM Observability Tools (Firecrawl). https://www.firecrawl.dev/blog/best-llm-observability-tools · Top 5 LLM and Agent Observability Tools (MLflow). https://mlflow.org/top-5-agent-observability-tools/
13. Lightcast: The Generative AI Job Market: 2025 Data Insights. https://lightcast.io/resources/blog/the-generative-ai-job-market-2025-data-insights
14. LLM Fine-Tuning Guide: LoRA, QLoRA, DPO, GRPO, RLHF (Future AGI). https://futureagi.com/blog/llm-fine-tuning-guide-2025/
15. Comparing the Top 6 Inference Runtimes for LLM Serving in 2025 (MarkTechPost). https://www.marktechpost.com/2025/11/07/comparing-the-top-6-inference-runtimes-for-llm-serving-in-2025/
16. Introducing NVIDIA Dynamo (NVIDIA). https://developer.nvidia.com/blog/introducing-nvidia-dynamo-a-low-latency-distributed-inference-framework-for-scaling-reasoning-ai-models/
17. Kubernetes GPU Scheduling: DRA, KAI, MIG (TechPlained). https://www.techplained.com/kubernetes-gpu-scheduling · Slurm-on-Kubernetes (NVIDIA). https://developer.nvidia.com/blog/running-large-scale-gpu-workloads-on-kubernetes-with-slurm/
18. Quantization Techniques for AI Inference 2026: GGUF, AWQ, GPTQ, FP8 (Sesame Disk). https://sesamedisk.com/quantization-techniques-ai-inference-2026/
19. OWASP Top 10 for LLM Applications 2025 (official). https://genai.owasp.org/llm-top-10/
20. EU AI Act Implementation Timeline (official). https://artificialintelligenceact.eu/implementation-timeline/ · EU AI Act vs NIST AI RMF vs ISO/IEC 42001 (EC-Council). https://www.eccouncil.org/cybersecurity-exchange/responsible-ai-governance/eu-ai-act-nist-ai-rmf-and-iso-iec-42001-a-plain-english-comparison/
21. AWS Certified ML Engineer – Associate. https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/ · Google Cloud Professional ML Engineer. https://cloud.google.com/learn/certification/machine-learning-engineer · Databricks ML Associate. https://www.databricks.com/learn/certification/machine-learning-associate
22. Grossmont College Applied AI AS/Certificate (catalog). https://catalog.gcccd.edu/grossmont/associate-degree-programs-certificates/stem/artificial-intelligence-as-cert-achievement/ · Grossmont launch news. https://www.gcccd.edu/news/articles/2025/2025-11-06-applied-ai-at-grossmont.php · Foothill College AI Certificate. https://catalog.foothill.edu/degrees-certificates/artificial-intelligence/ · Coastline AI for Cybersecurity AS. https://catalog.cccd.edu/coastline/pathways/computer-cyber-sciences/cybersecurity/ai-cybersecurity-as/
23. De Anza College Applied AI degree. https://www.deanza.edu/news/2026/applied-artificial-intelligence-degree-fall-2026.html · GovTech coverage. https://www.govtech.com/education/higher-ed/de-anza-college-launches-associate-degree-in-applied-ai
24. AI Infrastructure Engineer, Model Serving Platform (Scale AI). https://www.linkedin.com/jobs/view/ai-infrastructure-engineer-model-serving-platform-at-scale-ai-4196120822 (URL/salary to verify — §7) · AI Infrastructure Engineer (Hashlist). https://www.linkedin.com/jobs/view/ai-infrastructure-engineer-at-hashlist-4422814745 · CompTIA State of the Tech Workforce 2026. https://www.comptia.org/en-us/resources/research/state-of-the-tech-workforce-2026/ · LinkedIn Jobs on the Rise 2026. https://www.linkedin.com/pulse/linkedin-jobs-rise-2026-25-fastest-growing-roles-us-linkedin-news-dlb1c
25. Best Vector Databases 2026 (Firecrawl). https://www.firecrawl.dev/blog/best-vector-databases · pgvector vs Pinecone vs Qdrant vs Weaviate (Kalvium). https://www.kalviumlabs.ai/blog/vector-databases-compared-pgvector-pinecone-qdrant-weaviate/
26. AI Inference Cost Economics in 2026: GPU FinOps Playbook (Spheron). https://www.spheron.network/blog/ai-inference-cost-economics-2026/ (vendor source — treat figures as illustrative, §7) · FinOps for AI (Flexera). https://www.flexera.com/blog/finops/finops-for-ai-governing-the-unique-economics-of-intelligent-workloads/
27. California Community Colleges Vision 2030 Artificial Intelligence Workplan, July 2025. https://www.cccco.edu/-/media/CCCCO-Website/docs/vision2030/artificial-intelligence-workplan.pdf · CCCCO AI initiative. https://ai.cccco.edu/

CCCCO Primary Sources — §2 (hand-verified 2026-07-02)

• [C1] Program and Course Approval Handbook (PCAH), 8th Edition (Nov 2019) — current edition; credit + noncredit components. https://www.cccco.edu/-/media/CCCCO-Website/docs/curriculum/program-course-approval-handbook-8th-edition.pdf
• [C2] CCCCO Curriculum & Instruction Unit (approval authority for 116 colleges). https://www.cccco.edu/About-Us/Chancellors-Office/Divisions/Educational-Services-and-Support/What-we-do/Curriculum-and-Instruction-Unit
• [C3] CCCCO Noncredit Curriculum & Instructional Programs — CDCP categories; Certificate of Completion vs Certificate of Competency; enhanced-funding rules. https://www.cccco.edu/About-Us/Chancellors-Office/Divisions/Educational-Services-and-Support/What-we-do/Curriculum-and-Instruction-Unit/Curriculum/Noncredit-Curriculum-and-Instructional-Programs
• [C4] ASCCC Noncredit Instruction / Adult Education Basics — Ed Code §84750.5 / §84760.5, Title 5 §55151, SB 860 (2014) credit-parity funding. https://www.asccc.org/sites/default/files/Noncredit_Instruction_Adult_Education_Basics.pdf
• [C5] Title 5 §55070 Credit Certificates (Cornell LII). https://www.law.cornell.edu/regulations/california/5-CCR-55070
• [C5a] CCCCO Final Revisions to Title 5 §55070 re Credit Certificates of Achievement (adopted 2025). https://www.cccco.edu/-/media/CCCCO-Website/docs/regulatory-action/final-reg-text-certificate-of-achievement-final-v2-4-25-a11y-002.pdf
• [C6] CCCCO Vision 2030: A Roadmap for California Community Colleges — mirrored credit/noncredit pathways; Credit for Prior Learning via the MAP (Mapping Articulated Pathways) Initiative. https://www.cccco.edu/About-Us/Vision-2030 · https://vision2030.cccco.edu/

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Appendix: Compiled References

Reference List

A. CCCCO / Title 5 / Ed Code

1. CCCCO Artificial Intelligence Workplan, July 2025
2. CCCCO AI initiative overview
3. California Community Colleges AI Consortium

B. ACCJC

1. ACCJC 2024 Accreditation Standards (June 2025 edition, PDF)
2. ACCJC Adopts 2024 Accreditation Standards
3. ACCJC Substantive Change Manual, May 2026 Edition (PDF)
4. ACCJC Policy on Substantive Change, January 2026 (PDF)
5. ACCJC Policy on Distance Education and on Correspondence Education, June 2025 (PDF)
6. ACCJC Eligibility Requirements for Accreditation, January 2024 (PDF)

C. EDD / LMI / BLS / Projections / COE

1. California OEWS wage data CSV (EDD)
2. California Long-Term Occupational Employment Projections 2024-2034 (EDD CSV)
3. 2025 California Jobs Market Report (EDD, DE 864 Rev. 3 8-25)
4. EDD Employment Projections page
5. EDD OEWS Employment and Wage Statistics page
6. Employment Projections - Labor Market Information - CA.gov
7. EDD Labor Market Information Division - Home Page
8. Centers of Excellence for Labor Market Research -- Collections

D. Industry Tools & Techniques

1. Lightcast: The Generative AI Job Market: 2025 Data Insights
2. Lightcast: Beyond the Buzz -- AI Skills Command 28% Salary Premium
3. The best AI agent frameworks (LangChain)
4. Best AI Agent Frameworks 2025: LangGraph, CrewAI, OpenAI, LlamaIndex, AutoGen (Maxim AI)
5. Best Multi-Agent Frameworks 2026: LangGraph, CrewAI, OpenAI SDK
6. Donating the Model Context Protocol and establishing the Agentic AI Foundation (Anthropic)
7. Linux Foundation Announces the Formation of the Agentic AI Foundation
8. Best LLM Observability Tools (Firecrawl)
9. Top 5 LLM and Agent Observability Tools (MLflow)
10. Best AI Agent Observability Tools 2026 (Latitude)
11. Comparing the Top 6 Inference Runtimes for LLM Serving in 2025 (MarkTechPost)
12. vLLM vs TensorRT-LLM vs HF TGI vs LMDeploy — Technical Comparison (MarkTechPost)
13. Introducing NVIDIA Dynamo, a Low-Latency Distributed Inference Framework (NVIDIA)
14. Kubernetes GPU Scheduling: DRA, KAI, MIG (TechPlained)
15. Running Large-Scale GPU Workloads on Kubernetes with Slurm (NVIDIA)
16. SkyPilot (GitHub)
17. LLM Fine-Tuning Guide: LoRA, QLoRA, DPO, GRPO, RLHF (Future AGI)
18. The ML Practitioner's Guide to Fine-Tuning Language Models (MachineLearningMastery)
19. Quantization Techniques for AI Inference 2026: GGUF, AWQ, GPTQ, FP8 (Sesame Disk)
20. LLM Quantization: GPTQ, AWQ, and GGUF for Efficient Deployment (Calmops)
21. RAG in Production 2026: GraphRAG, Hybrid Retrieval, and Evals
22. Top 5 Reranking Models to Improve RAG Results (MachineLearningMastery)
23. RAG Techniques Compared: Best Practices Guide (Starmorph)
24. OWASP Top 10 for LLM Applications 2025 (official)
25. LLM Guardrails: Production Safety Layers Reference 2026 (Digital Applied)
26. LLM Guardrails: Best Practices (Datadog)
27. EU AI Act Implementation Timeline (official EU AI Act portal)
28. EU AI Act vs NIST AI RMF vs ISO/IEC 42001: A Plain English Comparison (EC-Council)
29. ISO/IEC 42001 and EU AI Act: A Practical Pairing (ISACA)
30. Best Vector Databases 2026: A Complete Comparison Guide (Firecrawl)
31. pgvector vs Pinecone vs Qdrant vs Weaviate (2026): Which We Use in Production (Kalvium)
32. AI Inference Cost Economics in 2026: GPU FinOps Playbook (Spheron)
33. FinOps for AI: Governing the Unique Economics of Intelligent Workloads (Flexera)

E. Peer Programs & Job Market

1. De Anza College Launches Silicon Valley's First Associate Degree in Applied Artificial Intelligence
2. Silicon Valley to get its first two-year degree focused on AI – The Press Democrat
3. De Anza College Launches Associate Degree in Applied AI
4. Applied Artificial Intelligence Associate in Science and Certificate of Achievement | Grossmont-Cuyamaca CCD Catalog
5. Grossmont College launches first of its kind, Applied Artificial Intelligence Associate Science degree
6. Artificial Intelligence - Academic Catalog - Foothill College
7. AI for Cybersecurity, Associate of Science Degree < Coastline College
8. Press Release: Coastline College's AI for Cybersecurity Program Earns NSA Validation Through 2030
9. Chaffey College to Launch Four New Computer Technology Programs
10. BC Offering Free Media Literacy Certificate Program
11. Associate in Science in Artificial Intelligence - Academic Map | MiraCosta
12. Artificial Intelligence and Machine Learning Suite | MiraCosta College in partnership with ed2go
13. Introduction to Artificial Intelligence | Saddleback College
14. AI - Bay Area Community College Consortium
15. AIDA - Bay Area Community College Consortium
16. AI Infrastructure Engineer, Model Serving Platform | Scale AI
17. AI Infrastructure Engineer at Hashlist (San Francisco, CA)
18. State of the Tech Workforce 2026 | CompTIA Research
19. Key employment metrics, market insights and the impact of AI revealed in CompTIA State of Tech Workforce 2026 report
20. LinkedIn Jobs on the Rise 2026: The 25 fastest-growing roles in the U.S.
21. AWS Certified Machine Learning Engineer – Associate
22. Professional ML Engineer Certification | Google Cloud
23. Databricks Certified Machine Learning Associate