Career Paths Catalog

Explore every course, individual track and bundled program in one place. Public overviews are open to everyone; detailed lessons and project execution remain inside enrolled classrooms.

Prepare for intermediate and senior opportunities.

Completing your enrolled Neriya Tech Academy course is a step toward demonstrating the practical skills employers seek. Through assessed projects, troubleshooting, a capstone and feedback, build evidence you can explain and apply. Learners who demonstrate the required capability can pursue intermediate and senior roles aligned with their skills, relevant professional experience and the employer’s expectations. Role readiness is judged by the work you can perform and the outcomes you can deliver; years in technology alone do not establish seniority.

Ready to enroll, or would you like to talk first?

Explore a course and enroll when you are ready. If you have questions about the curriculum, prerequisites, schedule or course fit, book a consultation with the Neriya Tech Academy team before enrolling.

If tuition feels out of reach or you are unsure how to pay, talk with us about affordability and available payment options.

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Browse every career direction below.

AI, Machine Learning & Agentic Systems

AI, Machine Learning & Agentic Systems

Kanban + AI Career Path

Duration: 14 weeks · one module per week

Explore the curriculum, enterprise projects, capstone and enrollment details.

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

AI, Machine Learning & Agentic Systems

SAFe + AI Career Path

Duration: 14 weeks · one module per week

Explore the curriculum, enterprise projects, capstone and enrollment details.

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

AI, Machine Learning & Agentic Systems

Applied Data Scientist

Duration: 14 weeks · one module per week

Explore the curriculum, enterprise projects, capstone and enrollment details.

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

AI, Machine Learning & Agentic Systems

UX/UI & AI Product Design

Duration: 14 weeks

Turn user needs into tested digital experiences, including AI interfaces that communicate uncertainty and preserve user control.

Five enterprise projects · one capstone · private live classroom

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

AI, Machine Learning & Agentic Systems · Skill-to-Proof Accelerator

Azure AI Fundamentals — AI-901 Preparation

Duration: 4 weeks · 6–8 hours weekly

Explore Azure AI concepts and Microsoft Foundry through a controlled prototype, evaluation and exam-readiness review.

One assessed proof project · instructor review · private classroom

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Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

AI, Machine Learning & Agentic Systems · Skill-to-Proof Accelerator

AI Governance Foundations & AIGP Preparation

Duration: 4 weeks · 6–8 hours weekly

Assess an AI use case, document risk and ownership, and prepare against the current IAPP AIGP body of knowledge.

One assessed proof project · instructor review · private classroom

Explore program

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

AI, Machine Learning & Agentic Systems

AI Literacy & Responsible Adoption for Business Professionals

Enrollment by consultation

Understand AI capabilities, assess workplace use cases and make informed adoption decisions.

Level: Beginner · no coding required

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Business professionals and team leads who want a practical, nontechnical route into responsible AI adoption.

Prerequisites and entry review

No programming or mathematics prerequisite. Bring a workplace process you want to improve.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Evaluate an AI opportunity against business value and risk.
  • Design a measured pilot with human accountability.
  • Present a justified adoption recommendation.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Business opportunity framing

    Applied work: Map a workplace process and define an improvement measure.

  2. Module 2: Understanding AI approaches

    Applied work: Compare predictive, generative and retrieval-based solutions.

  3. Module 3: Data and source readiness

    Applied work: Create a data-quality and permitted-use checklist.

  4. Module 4: Working with AI outputs

    Applied work: Test instructions and evaluate responses against a rubric.

  5. Module 5: Responsible adoption

    Applied work: Document privacy boundaries, human review and escalation.

  6. Module 6: Pilot measurement

    Applied work: Compare quality, effort and cost with a baseline.

  7. Module 7: Team enablement

    Applied work: Prepare a stakeholder and operational ownership plan.

  8. Module 8: Capstone decision review

    Applied work: Present an AI pilot recommendation with evidence.

Tools and practice environment

Spreadsheet tools, process mapping, an approved AI assistant and evaluation templates.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Develop an AI opportunity brief and a small pilot evaluation for a business workflow, including risks, review points and measurable acceptance criteria.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

Computer Science Foundations for AI Engineering

Enrollment by consultation

Build the programming, algorithms and software foundations needed for practical AI work.

Level: Beginner to intermediate

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Comfort using a computer and a willingness to practice programming; introductory Python knowledge is helpful.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Build and test a maintainable Python application.
  • Explain algorithm, data and API design tradeoffs.
  • Integrate a tested AI component with clear failure handling.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Problem decomposition

    Applied work: Translate a business task into pseudocode and acceptance cases.

  2. Module 2: Python application building

    Applied work: Implement functions, validation and error handling.

  3. Module 3: Algorithms and efficiency

    Applied work: Benchmark alternative search and processing methods.

  4. Module 4: Data structures and state

    Applied work: Build an indexed record store and test edge cases.

  5. Module 5: Files and relational data

    Applied work: Create a SQL-backed import with rejected-record reporting.

  6. Module 6: APIs and service boundaries

    Applied work: Build an API with clear failure responses.

  7. Module 7: AI integration and testing

    Applied work: Add a small evaluated AI component behind an interface.

  8. Module 8: Capstone engineering handover

    Applied work: Deliver code, tests, documentation and a technical walkthrough.

Tools and practice environment

Python, Git, pytest, SQLite, an API framework and a small machine-learning library.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Build a tested document-processing application, compare alternative data structures and expose a small API with clear error handling and a reproducible setup.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

Classical AI: Search, Reasoning & Planning

Enrollment by consultation

Solve constrained decision problems with search, logical reasoning and planning algorithms.

Level: Intermediate

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Working Python knowledge, basic algorithms and introductory probability.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Represent constrained decisions as computational problems.
  • Implement and compare search, reasoning and planning methods.
  • Explain feasibility, uncertainty and solution quality.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Decision-problem modeling

    Applied work: Define states, actions, objectives and constraints.

  2. Module 2: Graph search

    Applied work: Compare baseline and heuristic search methods.

  3. Module 3: Constraint satisfaction

    Applied work: Diagnose an impossible resource allocation.

  4. Module 4: Rules and inference

    Applied work: Trace conclusions through a documented rule set.

  5. Module 5: Uncertain decisions

    Applied work: Compare outcomes under different probability assumptions.

  6. Module 6: Planning and competing agents

    Applied work: Evaluate action sequences under changing conditions.

  7. Module 7: Failure analysis

    Applied work: Build regression cases for fragile assumptions.

  8. Module 8: Capstone planner defense

    Applied work: Demonstrate a resource planner against a baseline.

Tools and practice environment

Python, notebooks, graph and optimization libraries, Git and automated tests.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Create a resource-allocation planner for a simulated service operation. Compare search methods, explain infeasible cases and evaluate plan quality against a documented baseline.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

LangChain & LangGraph Application Engineering

Enrollment by consultation

Develop a focused LLM application with retrieval, controlled tools and observable workflows.

Level: Intermediate

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Python, APIs and introductory LLM concepts; related Neriya agentic AI and RAG courses provide a broader route.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Build a grounded LLM application with stateful workflows.
  • Constrain tool access and incorporate human decisions.
  • Evaluate, trace and hand over an agent application.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Application contracts

    Applied work: Validate structured inputs, outputs and error behavior.

  2. Module 2: Knowledge preparation

    Applied work: Ingest permitted documents with source provenance.

  3. Module 3: Retrieval and grounding

    Applied work: Test retrieval quality and answer support.

  4. Module 4: LangChain composition

    Applied work: Build a grounded workflow with citation checks.

  5. Module 5: LangGraph orchestration

    Applied work: Implement state transitions and resumable execution.

  6. Module 6: Tools and human decisions

    Applied work: Add scoped tools and an approval gate.

  7. Module 7: Evaluation and operations

    Applied work: Create regression tests, tracing and latency checks.

  8. Module 8: Capstone application handover

    Applied work: Deliver a support assistant and operating runbook.

Tools and practice environment

Python, LangChain, LangGraph, an approved model API, a vector index and tracing tools.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Build an internal support assistant over a permitted knowledge set, with a human escalation step, tool restrictions, evaluation cases and an operational handover.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

LLM Architecture, Adaptation & Fine-Tuning

Enrollment by consultation

Connect transformer concepts to practical model adaptation and evidence-based model selection.

Level: Intermediate to advanced

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Python, introductory machine learning and basic linear algebra; familiarity with a deep-learning framework is helpful.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Explain transformer architecture and model-selection tradeoffs.
  • Run a reproducible, permitted-data adaptation experiment.
  • Compare baseline quality, efficiency and release risks.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Model selection and baselines

    Applied work: Define evaluation criteria before choosing a model.

  2. Module 2: Tokens and attention

    Applied work: Inspect tokenization and verify a small attention computation.

  3. Module 3: Transformer architecture

    Applied work: Trace encoder and decoder components and tensor shapes.

  4. Module 4: Data and experiment integrity

    Applied work: Create permitted train, validation and test partitions.

  5. Module 5: Model adaptation

    Applied work: Compare prompting, retrieval and a small parameter-efficient adaptation.

  6. Module 6: Serving efficiency

    Applied work: Measure memory, latency and throughput tradeoffs.

  7. Module 7: Evaluation and release

    Applied work: Assess robustness and document model limitations.

  8. Module 8: Capstone model review

    Applied work: Defend a reproducible adaptation and release recommendation.

Tools and practice environment

Python, PyTorch, Hugging Face tooling, parameter-efficient adaptation and experiment tracking.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Compare an unadapted model, a retrieval-based approach and a small adaptation experiment on a permitted dataset. Document quality, cost, limitations and a release recommendation.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

Deep Learning & Neural Network Engineering

Enrollment by consultation

Train, debug and evaluate neural networks using disciplined experiments and clear evidence.

Level: Intermediate

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Python, introductory machine learning, basic linear algebra and probability.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Train and debug neural-network models.
  • Evaluate generalization and investigate failure slices.
  • Package a tested model service and explain its limitations.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Experimental foundations

    Applied work: Create a reproducible dataset and baseline.

  2. Module 2: Neural computation

    Applied work: Implement and verify forward and gradient calculations.

  3. Module 3: Training optimization

    Applied work: Diagnose unstable training using recorded learning curves.

  4. Module 4: Generalization

    Applied work: Test regularization and investigate leakage risks.

  5. Module 5: Vision models

    Applied work: Evaluate a convolutional model and transfer-learning approach.

  6. Module 6: Sequence modeling

    Applied work: Compare a text baseline with a sequence or attention model.

  7. Module 7: Model serving

    Applied work: Package a service with validation and regression checks.

  8. Module 8: Capstone model defense

    Applied work: Deliver a tested model service and failure analysis.

Tools and practice environment

Python, PyTorch, notebooks, experiment tracking, API and container tooling.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Build an image or text classification service on a permitted dataset. Compare a simple baseline with a neural model, investigate failure slices and document the model’s operating limits.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

Reinforcement Learning & Decision Systems Engineering

Enrollment by consultation

Design and evaluate learning agents in controlled decision environments with measurable rewards and clear safety boundaries.

Level: Intermediate to advanced

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Python, machine learning, probability and basic linear algebra. Deep-learning familiarity is recommended.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Formulate decision environments and measurable rewards.
  • Train and compare policies with reproducible experiments.
  • Evaluate generalization and reward-related failure cases.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Environment design

    Applied work: Build a resource-dispatch simulator with explicit episode boundaries.

  2. Module 2: Policy baselines

    Applied work: Compare a fixed policy with tabular value learning.

  3. Module 3: Function approximation

    Applied work: Train and debug a small deep value-learning experiment.

  4. Module 4: Policy optimization

    Applied work: Compare policy-gradient and actor-critic behavior.

  5. Module 5: Reward quality

    Applied work: Detect and mitigate reward exploitation in a controlled task.

  6. Module 6: Generalization

    Applied work: Evaluate a policy on unseen operating conditions.

  7. Module 7: Verification and feedback

    Applied work: Build a bounded task with deterministic success checks.

  8. Module 8: Capstone policy defense

    Applied work: Deliver the agent, experiments and limitations review.

Tools and practice environment

Python, simulation environments, PyTorch and experiment tracking.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Create a simulated resource-dispatch agent. Compare it with a rule-based baseline, investigate reward exploitation and defend the policy on held-out scenarios.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

Recommendation, Ranking & Personalization Engineering

Enrollment by consultation

Build recommendation and ranking systems with measurable relevance, coverage and operational quality.

Level: Intermediate

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Python, SQL, basic statistics and supervised machine learning.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Design candidate-retrieval and ranking stages.
  • Measure relevance, coverage and cold-start behavior.
  • Plan a monitored experiment with appropriate guardrails.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Product objectives

    Applied work: Define relevance measures and an interaction-data contract.

  2. Module 2: Recommendation baselines

    Applied work: Implement content-based and collaborative candidates.

  3. Module 3: Candidate retrieval

    Applied work: Evaluate an embedding index and retrieval coverage.

  4. Module 4: Ranking models

    Applied work: Compare a learned ranker with a simple baseline.

  5. Module 5: Cold start and bias

    Applied work: Test new-user behavior and uneven item exposure.

  6. Module 6: Experiment design

    Applied work: Prepare an offline evaluation and guarded online-test plan.

  7. Module 7: Serving and monitoring

    Applied work: Build a recommendation API and freshness checks.

  8. Module 8: Capstone system review

    Applied work: Deliver the service and an evidence-based rollout recommendation.

Tools and practice environment

Python, SQL, scikit-learn, a deep-learning framework, vector search and experiment tracking.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Build a learning-resource recommendation service with permitted or synthetic interaction data. Compare popularity, collaborative and feature-based methods and document cold-start and exposure risks.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

AI, Machine Learning & Agentic Systems

Databricks Data & AI Platform Engineering with MLflow

Enrollment by consultation

Connect governed lakehouse pipelines with reproducible model experiments, evaluation and operational handover.

Level: Intermediate

Structure: 8 modules, including capstone

Explore curriculum, projects & enrollment

Who this course is for

Learners and working professionals developing the technical capabilities described below. Course selection begins with a prerequisite and goals review.

Prerequisites and entry review

Python, SQL, data-pipeline fundamentals and introductory machine learning. Workspace access and costs are confirmed during consultation.

A short readiness discussion checks prerequisite knowledge, practice time and access to the required tools before enrollment.

Learning outcomes

  • Build governed, testable lakehouse pipelines.
  • Track experiments and model or agent evaluation evidence.
  • Operate a monitored release with recovery controls.

Neriya curriculum

Eight teaching modules, including the capstone. Calendar pacing and live-session arrangements are confirmed for the selected cohort.

  1. Module 1: Platform and access design

    Applied work: Map ownership and design least-privilege workspace access.

  2. Module 2: Reliable ingestion

    Applied work: Create a pipeline with schema and quality checks.

  3. Module 3: Trustworthy transformations

    Applied work: Build layered data products with traceable lineage.

  4. Module 4: Workflow operations

    Applied work: Test failed jobs and controlled pipeline recovery.

  5. Module 5: MLflow experiments

    Applied work: Compare tracked models with a reproducible baseline.

  6. Module 6: Model and agent evaluation

    Applied work: Record evaluation datasets, traces and failure cases.

  7. Module 7: Release and serving

    Applied work: Prepare deployment checks, monitoring and rollback.

  8. Module 8: Capstone platform handover

    Applied work: Deliver the pipeline, evaluated service and runbook.

Tools and practice environment

Databricks, Apache Spark, Delta Lake, Unity Catalog, MLflow, Git and workflow orchestration.

Use permitted or synthetic datasets. Any model API, cloud compute, workspace or third-party license costs are confirmed before payment.

Enterprise capstone and portfolio evidence

Deliver a permitted-data lakehouse pipeline and an evaluated prediction or retrieval service, including access controls, lineage, release checks and recovery documentation.

Deliverables include a problem brief, implementation or business artifact, test or evaluation evidence, a limitations report and a reviewer walkthrough. Technical courses also include reproducible setup and an operating handover.

Assessment and completion requirements

Complete the required module exercises and practical submissions, address reviewer feedback, and pass the capstone review. Assessment considers correctness, evidence quality, reproducibility where applicable, responsible use and the ability to explain decisions. An assessment rubric and submission schedule are supplied before teaching begins.

Learning experience and support

Instructor-guided workshops, independent practice, troubleshooting exercises and project feedback form the planned delivery. Confirm the instructor, meeting schedule, feedback arrangements and accessibility needs during enrollment consultation.

Enrollment, tuition and payment

Enrollment is by consultation. Confirm prerequisite fit, delivery format, duration, cohort dates, tuition and any separate lab costs in writing before payment. Credit or debit card payment and any available Neriya installment option are reviewed with the course quotation.

External certification exams and third-party credentials are separate from Neriya course completion. Career opportunities depend on demonstrated capability, relevant experience and employer requirements.

Discuss enrollment and course fit

Agile & delivery

Facilitate delivery, shape products and lead enterprise change through practical Agile work. Explore this collection

Data & analytics

Build trustworthy data, analytical decisions and reliable reporting through practical enterprise work. Explore this collection

Data & analytics

Data Analytics

Duration: 14 weeks · one module per week

Explore the curriculum, enterprise projects, capstone and enrollment details.

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

Security & governance

Investigate threats, implement controls and produce evidence that supports accountable security decisions. Explore this collection

Security & governance

Cybersecurity

Duration: 14 weeks · one module per week

Explore the curriculum, enterprise projects, capstone and enrollment details.

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

Security & governance

Cyber Security Bundle

Duration: 28 weeks · four complete courses

Next cohort: November 2, 2026. Additional 15% off Neriya tuition. Explore the learning overview and enrollment details.

Explore track

Questions or cost concerns? Speak with our team before enrolling.

Book a consultation

Security & governance

Enterprise Cybersecurity Architecture

Duration: 14 weeks

Design defensible enterprise security across identity, networks, applications, cloud and AI. Turn business risks into tested controls, architecture decisions and an operational handover.

Five enterprise projects · capstone · private classroom

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Security & governance

Cyber Threat Intelligence & Attribution

Duration: 14 weeks

Turn fragmented threat evidence into clear defensive decisions. Practice intelligence requirements, behavior mapping, confidence-based analysis and actionable reporting without overstating attribution.

Five enterprise projects · capstone · private classroom

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Systems, applications & business

Operate enterprise systems, automate business platforms and connect technology delivery to business needs. Explore this collection

Systems, applications & business

Systems Engineering

Duration: 14 weeks · one module per week

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Networking

Design, automate and troubleshoot enterprise connectivity through realistic network scenarios. Explore this collection

Networking

Cisco Networking

Duration: 14 weeks · one module per week

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Cloud, DevOps & platforms

Build, deploy and operate resilient platforms with practical infrastructure, delivery and recovery work. Explore this collection

Cloud, DevOps & platforms

Cloud Engineer

Duration: 14 weeks · one module per week

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Cloud, DevOps & platforms

DevOps Engineer

Duration: 14 weeks · one module per week

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Cloud, DevOps & platforms · Skill-to-Proof Accelerator

AWS Cloud Practitioner — CLF-C02 Preparation

Duration: 2 weeks · 6–8 hours weekly

Understand AWS services, shared responsibility, security and billing, then explain a cost-aware cloud design.

One assessed proof project · instructor review · private classroom

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Cloud, DevOps & platforms · Skill-to-Proof Accelerator

Google Cloud Digital Leader Preparation

Duration: 2 weeks · 6–8 hours weekly

Connect cloud, data and AI capabilities to business value through a measured transformation proposal.

One assessed proof project · instructor review · private classroom

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Cloud, DevOps & platforms · Skill-to-Proof Accelerator

FinOps & Cloud Cost Foundations

Duration: 4 weeks · 6–8 hours weekly

Investigate a synthetic cloud bill, allocate costs, prioritize savings and explain business value with evidence.

One assessed proof project · instructor review · private classroom

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Cloud, DevOps & platforms · Skill-to-Proof Accelerator

Git, GitHub Actions & CI/CD Accelerator

Duration: 4 weeks · 6–8 hours weekly

Build a tested delivery pipeline, investigate failures, and demonstrate a controlled deployment and rollback.

One assessed proof project · instructor review · private classroom

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Cloud, DevOps & platforms · Skill-to-Proof Accelerator

Docker & Container Foundations

Duration: 2 weeks · 6–8 hours weekly

Containerize a small application and prove it starts reliably, persists data and handles failure.

One assessed proof project · instructor review · private classroom

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SAP & Enterprise Business Systems

Explore SAP business processes, functional configuration, development, integration, operations and delivery. Explore this collection

Enterprise Software & Digital Experience School

Build digital products that work for their users. Practice implementation, research, accessibility, validation and professional handover.

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Software & Digital Experience

Frontend & UI Engineering

Duration: 14 weeks

Build responsive, accessible enterprise interfaces, integrate APIs and prove quality through automated tests and performance evidence.

Five enterprise projects · one capstone · private live classroom

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Software & Digital Experience

Mobile Application Engineering

Duration: 14 weeks

Build a native mobile application that handles real-world connectivity, permissions, privacy, accessibility and release quality.

Five enterprise projects · one capstone · private live classroom

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Software & Digital Experience

Digital Accessibility Engineering

Duration: 12 weeks

Evaluate and remediate digital barriers through keyboard, screen-reader and usability testing, with evidence that goes beyond automated scans.

Five enterprise projects · one capstone · private live classroom

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Software & Digital Experience

Embedded Systems & Firmware Engineering

Duration: 18 weeks

Build and debug reliable firmware, connect hardware and software, and prove timing, recovery and resource behavior in controlled labs.

Five enterprise projects · one capstone · private live classroom

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PRACTICAL GUIDANCE FROM NERIYA

See what practical learning means at Neriya.

Meet the people behind the learning, inspect example project standards and ask clear questions before committing.

Tuition & payment options

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Where available, spread tuition across automatic payments. On your chosen course page, select Review installment plan to see the exact amounts and payment dates before agreeing to checkout.

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