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Enterprise Training Cloud ML Engineer Pathways

Operationalize Machine Learning

Applied AI training for teams moving from experiments to production, covering model development, generative AI and MLOps.

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Program overview

Take AI from prototype to production

Applied training that equips engineering and data teams to build, evaluate and deploy machine learning and generative AI solutions responsibly on managed cloud platforms.

  • Frame problems and build reliable ML models
  • Apply generative AI patterns such as retrieval-augmented generation
  • Deploy, monitor and govern models with MLOps practices
AI & Machine Learning program overview

Who this is for

Data scientists and ML engineers
Software engineers adding AI features
Analytics teams moving into ML
Technology leaders scoping AI programs
Capability outcomes

What your team will be able to do

Skills that transfer straight into day-to-day project work.

6core capabilities
OUTCOME 01

Applied machine learning

Frame business problems and build models that generalise.

OUTCOME 02

Feature engineering

Prepare data and features that improve model quality.

OUTCOME 03

Deep learning

Train neural networks with TensorFlow and PyTorch.

OUTCOME 04

Generative AI

Build with large language models and retrieval-augmented generation.

OUTCOME 05

MLOps

Track, version, deploy and monitor models in production.

OUTCOME 06

Responsible AI

Apply guardrails for fairness, privacy and safety.

Curriculum

How the program is structured

Module 01

Machine learning foundations

  • Problem framing and success metrics
  • Data preparation and features
  • Supervised and unsupervised methods
  • Evaluation and validation
Module 02

Model development

  • scikit-learn workflows
  • Deep learning with TensorFlow or PyTorch
  • Hyperparameter tuning
  • Hands-on lab
    train and evaluate a model
Module 03

Generative AI and LLMs

  • LLM concepts and limits
  • Prompt engineering patterns
  • Retrieval-augmented generation
  • Responsible AI guardrails
  • Hands-on lab
    build a RAG assistant
Module 04

MLOps and deployment

  • Experiment tracking with MLflow
  • Model registry and versioning
  • Serving models through APIs
  • CI/CD for machine learning
Module 05

Cloud AI platforms

  • Vertex AI and SageMaker overview
  • Managed training and pipelines
  • Monitoring and drift detection
  • Cost and governance

Swipe to switch modules

Hands-on practice

Learn by building

Concepts are applied through guided exercises, so your team practises on the same platforms and tools used on real projects.

Platforms and tools

Pythonscikit-learnTensorFlowPyTorchMLflowVertex AISageMakerHugging Face
2guided labs in this program
01
Module 02 · Model developmenttrain and evaluate a model
02
Module 03 · Generative AI and LLMsbuild a RAG assistant
Engagement model

From skills assessment to lasting capability

  1. Before

    Assess

    We review your team skills and the outcomes the business needs.

  2. Planning

    Tailor

    Scope, labs and schedule are aligned to your roles and stack.

  3. Delivery

    Deliver

    Instructor-led sessions with hands-on labs and live Q&A.

  4. After

    Sustain

    Progress reporting and follow-up support to embed the skills.

Delivered your way

Virtual

Live instructor-led sessions delivered online.

Best for: Teams across locations or time zones

On-site

Delivered at your office or a training venue.

Best for: Teams that want focus away from daily work

Hybrid

A mix of in-person and virtual sessions.

Best for: Larger groups split across locations

Private batches

Cohorts built around your team and schedule.

Best for: Teams that need a custom timeline
Request a proposal
Certification alignment

Cloud ML Engineer Pathways

Prepares teams for cloud machine learning certifications from AWS, Google Cloud and Microsoft.

Practice assessments

Mock exam

Readiness review

FAQ

Common questions

Need something specific for your team?

Talk to an advisor
What prior experience is needed?

Working knowledge of Python and basic statistics is recommended. Machine learning concepts are covered from the foundations.

Can the program be tailored to our team?

Yes. We align scope, depth and lab scenarios to your roles, stack and goals before delivery.

How is the program delivered?

Instructor-led, virtually or on-site, for a single team or multiple cohorts, with hands-on labs in dedicated environments.

Is support available after the program?

Yes. Participants receive follow-up support after the sessions to help apply the skills on real projects.

Related programs

Explore more training

Ecosystem

Validated by Industry Leaders.

Google
Amazon AWS
Microsoft
Salesforce
Cisco
Intel
TCS
Infosys
Wipro
Deloitte
Sony
Mercedes
Google
Amazon AWS
Microsoft
Salesforce
Cisco
Intel
TCS
Infosys
Wipro
Deloitte
Sony
Mercedes
Market Validation

What Engineering Leaders Say.

We don't deal in theoretical certifications. Our success is measured entirely by the production readiness and multi-cloud capabilities of the teams we deploy.

"

The production-grade sandbox environments completely changed our upskilling trajectory. Our teams didn't just learn AWS; they built failure-resistant architectures they deployed the very next week.

SJ

Sarah Jenkins

VP of Engineering, CloudOps
"

Moving our entire data pipeline to Databricks seemed impossible. The custom architecture playbooks and telemetry tracking provided by the training team gave us absolute confidence to scale.

MR

Marcus Rodriguez

Lead Data Architect
"

It's rare to find an execution model that skips the high-level fluff. We identified critical skill deficits in week one, and by month three, our internal GenAI integrations were live in production.

AK

Aisha Kapoor

Director of AI Infrastructure

Plan your AI & Machine Learning program

Share your team goals and we will scope a program around them.

Request a proposal
Enterprise trainingAI & Machine Learning
Request a proposal