SB
Spandana Bala
AI Product Manager
Hyderabad · India
Now at · Microsoft
Class 014 · AI PRODUCT OWNER · DIGITAL LYNC EXCLUSIVE
Now
Product Owner
+ AI
Agents
Lead, build, and ship AI-powered products in a world reshaped by autonomous agents. Master LLM-feature PRDs, RAG requirements, agentic workflow design, AI evaluations, and Responsible AI governance.
45days
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our UI/UX Design alumni work
MicrosoftAmazonSalesforceAI
EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
MicrosoftAmazonSalesforceAI
EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
WHAT YOU LEAVE WITH
Four things every AI Product Owner grad walks away with.
01
Agent-Ready PO skills
Write PRDs for non-deterministic AI features, spec RAG requirements, and design agentic workflows with MCP and LangGraph — not generic Scrum.
02
10 shipped artefacts
A 6-month AI roadmap, AI PRD, agentic feature spec, prioritised backlog, prompt library, Figma prototype, ethics checklist, and eval framework.
03
Verifiable credential
Digital Lync Certificate + PSPO-AI Essentials Prep on the 2026 Agent-Ready rubric — graded 1–5 with a public verification URL.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, AI-PO-tuned resume rebuild, and warm intros into 14,000+ open AI PO and AI PM roles globally.
45 DAYS · FOUR PHASES · 10 PORTFOLIO DELIVERABLES
From “writes user stories” to ships autonomous AI agents..
WEEKS 1–2 · Foundations & Discovery
AI Product Ownership + AI-Powered Discovery
- PO vs PM vs BA in an AI product team, Scrum for AI cycles
- GenAI tools as daily PO companions — ChatGPT, Claude, Gemini
- Stakeholder mapping for AI (ML Engineers, Data Scientists, AI Ethicists)
- LLM-assisted personas, AI-analysed empathy mapping, Spinach.io workshops
YOU SHIPA complete Product Backlog, Sprint Board, AI User Personas, and Product Vision Board anchored to two real AI case studies.
Modules 3–4 · Strategy & Backlog
AI Roadmaps, OKRs & Agentic Backlog Automation
- Hypothesis-driven AI roadmaps that evolve as models learn
- OKR Framework for AI products, Productboard AI and Zeda.io
- Make-vs-Buy-vs-Partner decision framework for AI capabilities
- Prioritisation — MoSCoW, WSJF, RICE, Value vs Effort
- Agentic backlog automation — auto-triage feedback → Jira
YOU SHIPA 6-month AI roadmap with OKRs and executive deck, plus a WSJF/RICE-prioritised backlog with an n8n automation workflow.
Modules 5–7 · Building AI Products
PRDs, Prompt Engineering & Agentic AI
- PRDs for LLM features — prompt behaviour, output constraints, fallbacks
- RAG system requirements — vector DBs, retrieval logic, chunking
- Prompt Engineering — zero-shot, few-shot, CoT, ReAct patterns
- Automated PO workflows with n8n and Zapier
- Agent architecture, MCP, multi-agent orchestration (CrewAI, LangGraph, AutoGen), HITL
YOU SHIPAn AI PRD, Agentic Feature Spec, 20+ prompt template library, and n8n workflow anchored to a RAG knowledge bot case study.
Modules 8–10 · Launch, Ethics & Evals
Ship AI products people actually trust — with the UX patterns, governance frameworks, and evaluation pipelines that separate launches from disasters.
Master AI UX design with Figma AI, v0.dev, and Lovable — confidence indicators, progressive disclosure, graceful failure, human override. Apply the four pillars of Responsible AI (Fairness / Accountability / Transparency / Explainability) and navigate GDPR, India’s DPDP Act, and the EU AI Act. Close with the most senior skill in the discipline — using AI to grade AI through eval frameworks for RAG and agentic workflows, MLOps handoff, shadow mode, gradual rollout, and the data flywheel that makes your product smarter post-launch.
AI PO job
posting growth (YoY)10x
Open AI PO/PM
roles globally14,000+
→ Top US
salary band$200K+
Course curriculum
Seven sections. 65+ modules. The AI-native UI/UX Design stack.
01
Foundations & Discovery
Establish the conceptual bedrock and discovery practice every AI Product Owner
needs — the AI-aware Scrum framework and the uniquely complex stakeholder landscape of AI
initiatives, which extends beyond traditional product roles to include ML Engineers, Data
Scientists, and AI Ethicists.
PO vs PM vs BA — distinct roles in a modern AI product team
Scrum Framework for AI products — Sprint Planning, Daily Scrum, Review, Retro
adapted for non-deterministic systems and model iteration cycles
Writing User Stories & Acceptance Criteria for AI features
GenAI intro — ChatGPT, Claude, Gemini as daily PO companions
Jira, Linear, and Azure Boards for AI backlogs
Hands-on — build Product Backlog, Role Matrix (PO / PM / BA / ML Engineer / Data
Scientist / AI Ethicist) and Sprint Board
Case Study — Kdigital Technologies AI CRM Feature Launch
Stakeholder mapping for AI initiatives — includes ML Engineers, Data Scientists
and AI Ethicists alongside traditional product stakeholders
AI-aware User Personas with LLM assistance
Empathy Mapping with AI-analysed feedback
Product Vision Board & Product Goal definition
Spinach.io for AI-facilitated discovery workshops
Hands-on — build Stakeholder Report, AI User Personas, Product Vision Board
Case Study — Healthcare AI Patient Portal Discovery (bias, privacy, clinical
safety)
02
Strategy & Backlog
Transition from discovery into the strategic core of AI product ownership —
hypothesis-driven roadmaps, prioritisation frameworks that work in an AI context, and agentic
backlog automation that does the mechanical work in the background.
AI roadmap planning — sequencing model iteration & data acquisition
Hypothesis-driven roadmaps — why AI roadmaps evolve as models learn, drift, and
improve
OKR Framework for AI Products — defining outcomes that account for the
probabilistic nature of model performance
Productboard AI & Zeda.io for roadmap intelligence
The critical Make vs Buy vs Partner decision framework for AI capabilities
Communicating AI strategy clearly to both technical and executive stakeholders
Hands-on — build a 6-Month AI Roadmap, OKR Document, and Executive Deck
Case Study — E-commerce Personalization Engine Roadmap (foundation models, vector
DBs, analytics)
AI-assisted backlog grooming with Jira Assistant AI & ClickUp Brain
Prioritisation frameworks — MoSCoW, WSJF (Weighted Shortest Job First), RICE,
Value vs Effort
When to apply each prioritisation framework in the context of AI product
development
Context Engineering for backlog — PRDs, personas & constraints feeding the
LLM
Agentic backlog automation — agents that auto-triage incoming feedback, classify
it, and create Jira tickets without manual overhead
Delibr & ProdPad CoPilot for AI-powered refinement
Hands-on — build Prioritised Backlog, WSJF/RICE Spreadsheet, and an n8n/Zapier
Automation Workflow
Case Study — SaaS Platform AI Feature Backlog across multi-stakeholder teams
03
Building AI Products
The frontier of the programme — write requirements for non-deterministic
systems, master prompt engineering as a product skill, and design autonomous agent workflows with
MCP, CrewAI, and LangGraph.
PRDs for LLM Features — prompt behaviour, output constraints, fallback logic
RAG System Requirements — vector databases, retrieval logic, chunking strategies,
re-ranking
Agentic Feature Requirements — observable behaviour, error handling, human
handoff, escalation paths
NFRs for AI — latency, cost vs quality trade-offs, explainability requirements
Acceptance Criteria for non-deterministic AI outputs — testable, meaningful
definition of done without deterministic responses
Hands-on — build an AI PRD, Agentic Feature Spec, NFR Document, and Data
Requirements Document
Case Study — Enterprise Internal Knowledge Bot PRD (RAG for a Fortune 500)
Prompt Engineering core patterns — zero-shot, few-shot, chain-of-thought (CoT),
ReAct
When each technique is appropriate (and when it isn't)
Context Engineering — designing the full information environment for LLMs (the
2026 frontier discipline)
Prompt templates for daily PO work — user stories, backlogs, PRDs, competitive
research
Synthetic User Research — AI focus groups with LLM-powered personas
Building automated PO workflows using n8n and Zapier — background workflows for
the mechanical work
Hands-on — build a 20+ Prompt Template Library, n8n Automation Workflow, and
Competitive Analysis Report
Case Study — AI-Powered Weekly Backlog Refinement Workflow
The Three Tiers of AI Systems — Chatbots, Copilots, Autonomous Agents
Agent architecture — reasoning engines, memory systems, tool interfaces, goal
management
Model Context Protocol (MCP) — how agents connect to external systems
Multi-agent orchestration concepts using CrewAI, LangGraph, and AutoGen
Human-in-the-Loop (HITL) workflows — designing the checkpoints where human
judgement is mandatory
Writing acceptance criteria for agents — observable behaviour, error recovery,
escalation paths
The 2026 production agent stack — LangGraph (#1 production default) vs Claude
Agent SDK (#2 MCP-native) vs CrewAI (#3 multi-agent crews)
Hands-on — build an Agentic Workflow Design Document (architecture, tools, HITL,
errors, acceptance criteria)
Case Study — Customer Onboarding Automation Agent (replacing a 12-step manual
process)
04
Launch, Ethics & Evals
The launch and operations track — AI UX patterns, responsible-AI governance, and
the measurement frameworks that turn shipping day into the starting gun rather than the finish line.
AI UX Design Principles — transparency, trust, user control, graceful failure
AI UX Patterns — confidence indicators, progressive disclosure, human override
mechanisms
Wireframing AI flows — chatbots, agent dashboards, feedback loops
Prototyping with Figma AI, v0.dev, and Lovable
Usability Testing for non-deterministic AI features
Designing for the moment an agent gets it wrong — the most important UX moment in
any AI product
Hands-on — build AI Wireframes (5 screens), an Interactive Figma Prototype, and a
Usability Test Report
Case Study — GenAI Chatbot Interface Design with confidence indicators and
graceful error states
The Four Pillars of Responsible AI — Fairness, Accountability, Transparency,
Explainability
AI risks — hallucinations, algorithmic bias, data privacy vulnerabilities, model
drift
Regulatory landscape — GDPR, India's DPDP Act, the EU AI Act
AI safety for agentic systems — containment strategies, sandboxing, kill switches
in autonomous systems
Compliance-by-design for high-stakes AI products
The AI Risk Assessment as a standing artefact (not a one-time exercise)
Hands-on — build an AI Risk Assessment + Ethics Checklist mapped to your
regulatory landscape
Case Study — AI Ethics Review for Fintech Credit Scoring (bias, fairness,
regulatory compliance)
AI Metrics — hallucination rate, model drift, user override rate — beyond
traditional KPIs (DAU, conversion, retention)
Evals Framework — using AI to grade AI — designing evaluation pipelines for RAG
systems and agentic workflows
Detecting hallucinations, measuring retrieval quality, assessing agent task
completion rates
MLOps Handoff — deployment readiness, shadow mode, gradual rollout — launching AI
safely
The Data Flywheel — using product usage to continuously improve models over time
Post-launch reporting templates and continuous improvement cadence
Hands-on — build an AI Eval Framework + Go-Live Checklist with eval pipelines and
a structured rollout plan
Case Study — AI Product Go-Live and Continuous Improvement Cycle (first 90 days
post-launch)
Tools you'll master
32+ AI PM & agentic-era tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
Ship a real AI agent product end-to-end
Validate, prototype, and ship a working agentic product — PRD, v0 build, eval harness, and a GTM plan that survives a real exec review.
01Validated PRD with Jobs-to-be-Done framing, agentic UX wireframes, eval criteria, and a discovery deck signed off by 5 real prospect users.
02Working v0/Cursor prototype of the agent product — wired to OpenAI/Claude, with telemetry events instrumented in PostHog/Mixpanel.
03GTM strategy + pricing model — usage-based vs seat-based analysis, 3 pricing tiers, ICP definition, 90-day launch plan.
04Eval + safety harness — golden dataset, regression tests, hallucination & cost guardrails, a public dashboard reviewers can verify.
Outcome: 5 user interviews completed
Build: v0 prototype shipped
Reviewer: Pragmatic-certified PM
PRDv0OpenAIMixpanelGTMEvals
Enterprise · weeks 6–11
Internal AI copilot roadmap
Build a 6-quarter roadmap for an internal AI copilot serving sales/support/eng — feature prioritization with RICE+AI, capability gating, build-vs-buy framework, and an executive narrative deck.
RoadmapRICECapability mappingExec narrative
Real-time · weeks 8–12
Agent metrics + experiment program
Stand up the experimentation framework for an agentic feature: north-star metric, leading indicators, A/B test design, eval harness, and a dashboard that tells the story to GTM and engineering.
A/B testingLaunchDarklyMixpanelEval harness
Project · weeks 11–12
Ship your AI product into a real partner org.
Pick a real partner problem. Validate, prototype, and ship a working AI agent product — PRD, v0 build, eval harness, GTM plan — into a partner team that's actually using it.
Download the real world project
Full scope, sample partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ shipped76% → placement offers
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Lync · AI Product Strategist
AI PRDs · Agentic Roadmaps · GTM · Pricing · Discovery
"AI product management is where strategy meets non-determinism. The PMs who win in the agentic era write PRDs models can ship against, design evals as tight as their roadmap, and price for outcomes — not seats. That's the bar I teach to, every cohort."
15 yrs
PRODUCT
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of product leadership across AT&T, Salesforce, Cox Communications, and Broadcom — where he led B2B SaaS, internal platform, and consumer product launches for Fortune-500 banks, telcos, and insurers. Most recently he architected agentic-product practices that took AI copilots and autonomous agents from PRD to production.
His classes get you two things other programs don't give you: a founder-PM who has actually shipped AI products inside Fortune 500s, and a curriculum rewritten every quarter — so when hiring managers ask about agent evals, RICE+AI prioritization, or usage-based pricing, you've already done it. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · AI Product & Eval Lead
AI PRDs · Agent UX · Evals · Pricing & GTM · Roadmaps · Discovery
"An AI product is only as good as the eval harness behind it. Golden datasets, regression tests, hallucination and cost guardrails — that's the unglamorous PM craft that turns an LLM demo into a product enterprises actually buy. That's what I teach."
10 yrs
PRODUCT
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the AI product and evaluation practice. After 8 years shipping AI products inside Fortune 500 enterprises — copilots, retrieval systems, and autonomous agents — he stepped into the Chief Technologist seat to wire eval frameworks, agent UX patterns, and usage-based pricing into the way product teams actually ship: PRDs that account for non-determinism, golden datasets that catch regressions before users do, and dashboards that tell GTM the truth about agent quality.
His AI PM modules are built from real production post-mortems, not slide decks. Expect to leave with working PRDs for agentic features, eval harnesses, pricing models, and a discovery + GTM playbook you can run on day one. Ten years at Digital Lync, eight of them shipping AI products in production — Hyderabad-based, hands-on, and known for the rigor of agent evals other programs skip.
HIRING PARTNERS · INDUSTRY VOICES
What AI product employers say about Digital Lync grads.
Real feedback from product leaders at AI-first companies and the firms hiring our AI Product Owner graduates.
Digital Lync grads ramp 40% faster on AI product launches than typical PM hires. Best AI product management pipeline in India.
Aakash Mehta, Director of Product, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for AI product roadmaps and agent eval practices.
Anita Sharma, Senior Manager, Deloitte
The AI Product Owner programme is comprehensive — discovery, PRDs, evals, GTM. Grads come pre-trained for agentic-era enterprise work.
Rahul Bhatt, Solutions Lead, Mphasis
Their AI PRD + eval harness track produces PMs who write production-grade specs on day one. Rare combination of strategy and rigor.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the agentic product layer baked into the AI PM track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their AIPMM + Pragmatic AI Product Owner prep is rigorous, and the shipped project — PRD, v0 build, eval harness — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's AI PMs ship validated PRDs twice as fast in the first 90 days. Our internal product metrics back this up clearly.
Lakshmi Nair, VP Product, Wipro
Best AI product management pipeline we've sourced from in India. Their projects are real shipped products, not pitch decks.
Karthik Subramanian, Engineering Director, Cognizant
Strong B2B SaaS and internal platform PM foundation. Their AI PO grads need almost zero ramp time on enterprise product engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our AI product and watsonx PM teams. Strong fundamentals, sharp on eval and GTM.
Sanjay Verma, Talent Director, IBM
AI roadmaps + agent evals is exactly the talent gap we've been struggling to close. Digital Lync is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their AI PM track delivers product owners who navigate discovery, PRDs, and GTM on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our AI product practice. Strong on PRDs, sharp on agent UX, fluent in eval frameworks.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend AI roadmaps with Azure OpenAI evals land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready AI Product Owner
Presented to
Spandana Bala
For the successful discovery, build, and shipping of an AI agent product —
validated PRD, working v0 prototype, and an eval harness — evaluated against the AIPMM AI Product
Manager track and Pragmatic AI Product Owner credential rubrics.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the AI product community and mapped to AIPMM AI Product Manager
and Pragmatic AI Product Owner credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — PRD, v0 prototype, and eval harness — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: discovery, PRDs, agent UX, eval harnesses,
pricing & GTM. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
04Job placement support
Your first AI Product offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into B2B SaaS and AI-first companies. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your shipped PRDs, v0 prototype, eval dashboard, GTM plan, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the AI products you shipped (agent products, AI copilots, eval harnesses), the partner-org project, and the business outcome. Reviewed by PMs who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into B2B SaaS and AI-first companies — Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, Postman. You leave with recruiter contacts, not a generic "good luck."
AI Product alumni
Hundreds of AI product careers launched — here are eight.
NV
Naveen Vedala
Senior PM (Agentic Products)
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Principal PM (Platform AI)
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
AI Product Owner
Seattle · United States
Now at · Anthropic
MM
Mujahed Mohammed
Product Lead (Agent Eval)
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
Group PM (AI Copilots)
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
Staff PM (LLM Products)
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of Product (AI)
Hyderabad · India
Now at · Notion
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online AI Product Owner cohorts running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 90003 29956
US desk
+1 858 666 6719
Hours
Mon–Sat · 9am–9pm
Online class
Global
Weekend and evening AI PM cohorts running on IST and PST. Every online cohort ships
the same shipped project — PRD, v0 prototype, eval harness, GTM plan — as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Next class
25 May 2026
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the AI PM stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a tech background or prior PM experience?
No on both counts. Roughly 40% of every class comes from non-tech and non-PM streams — engineering, BCom, BBA, business analysts, designers, and first-time PMs. Weeks 1–2 cover the AI product model, agentic UX, and PRD craft from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually build, or is this just frameworks and slides?
You actually build. Every learner ships a working v0/Cursor prototype wired to OpenAI/Claude, an instrumented telemetry dashboard, and a real eval harness. The project is a deployed AI agent product inside a partner org — not a Figma deck.
Which tools and AI models will I actually use?
Discovery & PRDs: Notion, Linear, Jira, Confluence, Figma, Miro. Build: v0, Cursor AI, OpenAI, Claude, ChatGPT, LangChain, LangGraph, MCP. Telemetry & evals: Mixpanel, Amplitude, Segment, GA4, Hex. Experimentation & ops: LaunchDarkly, Optimizely, n8n, Zapier, Make.
Will I prep for AIPMM AI Product Manager and Pragmatic AI Product Owner certs?
Yes. The curriculum is mapped to the AIPMM AI Product Manager track and the Pragmatic AI Product Owner credential. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours building your prototype, and ~5 hours of project work (PRDs, evals, GTM). Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire AI PMs?
Yes — a dedicated placement advisor from week 8, not a helpdesk. AI product hiring partners include Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, and Postman. Resume, LinkedIn, mock interviews, and warm intros are individual.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST cohorts), and a weekend track for working professionals. Every format ships the same shipped project — PRD, v0 prototype, eval harness, GTM plan — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for anyone more than a week behind, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
Class APO-019 starts 25 May 2026.
40 seats. 12 already claimed.
Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real projects from class 022.
