AI & Machine Learning Fellowship

Master neural networks, NLP, computer vision, and production ML systems. The most technical AI programme for data scientists and ML engineers.

Months (Delivery + EPA)

Industry Qualifications

£27k

Fully Funded

Level 6

Degree-Equivalent

Deep Learning Excellence

Advanced neural networks, TensorFlow, PyTorch, and production ML systems on Azure and AWS.

Industry-Recognised

Microsoft Certified BCS Advanced NVIDIA

🔬

Advanced Technical Modules

🚀

85%

Learners in ML Roles Post-Programme

💻

100%

Production-Ready Projects

🏆

BCS

Professional Recognition

Why This Programme?

🎯

Most Technical AI Offering

Build state-of-the-art ML systems using the same frameworks as Google, Meta, and OpenAI.

📊

Real-World ML Engineering

Neural networks, NLP, computer vision, MLOps pipelines, model deployment, responsible AI — not theory.

💼

For Data Scientists & Engineers

Built for professionals with existing technical foundations specialising in ML at scale.

ML Engineering Skills Coverage

Python & Deep Learning

85%

MLOps & Deployment

72%

Production ML Systems

65%

Built For These Roles

Perfect for professionals specialising in machine learning engineering and advanced AI systems.

🔬

Data Scientists

Level up to production ML models. Master neural networks and MLOps.

⚙️

ML Engineers

Deepen expertise in scalable ML systems, containerisation, cloud deployment.

💡

Software Engineers

Add ML specialisation to your toolkit. Build AI-powered features at scale.

📈

Analytics Engineers

Move into predictive ML. Automate business intelligence systems.

🤖

AI/ML Specialists

Formalise your knowledge with industry-recognised qualifications.

🚀

Career Changers (Technical)

Coming from maths, physics, or engineering? Transition into ML professionally.

Qualifications & Certifications

Industry-recognised credentials from leading technology partners.

📜

ST1398 Level 6 Apprenticeship

Machine Learning Engineer Standard

Regulated apprenticeship for ML engineering. Neural networks, deep learning, MLOps, and production systems.

Degree-Equivalent

☁️

Microsoft Azure AI Engineer

AI-900 & AI-102

Expertise in implementing AI solutions on Microsoft Azure. Embedded in the curriculum.

Industry-Recognised

🏅

BCS Advanced RITTech

Professional Recognition

BCS accreditation recognising professional ML engineering competence.

Professional

🎓

NVIDIA Deep Learning Certification

CUDA & Deep Learning Frameworks

Expertise with NVIDIA GPU computing and production deep learning systems.

Technical Specialist

Your Learning Journey

24 months of progressive, hands-on ML engineering training in six intensive phases.

Foundations & Python Mastery

Months 1-4

Core

1.1

Advanced Python

OOP design, functional programming, NumPy, Pandas, Matplotlib.

Core

1.2

Data Processing at Scale

SQL, big data, cleaning, feature engineering, exploratory analysis.

Core

1.3

Statistics & Probability

Hypothesis testing, distributions, Bayesian inference for ML.

Traditional ML to Neural Networks

Months 5-8

Core

2.1

ML Fundamentals

Classification, regression, decision trees, ensemble methods, tuning.

Core

2.2

Deep Learning Foundations

Neural networks, backprop, activation functions, TensorFlow, PyTorch.

Specialist

2.3

Computer Vision Basics

CNNs, image preprocessing, transfer learning, OpenCV.

Advanced Neural Networks & NLP

Months 9-12

Specialist

3.1

NLP & Language Models

Transformers, BERT, GPT, embeddings, fine-tuning, huggingface.

Specialist

3.2

Advanced Computer Vision

Detection, segmentation, YOLO, pose estimation, vision transformers.

Core

3.3

Responsible AI & Ethics

Bias detection, fairness, governance, regulatory compliance.

MLOps & Production Systems

Months 13-16

Core

4.1

Model Serving & Deployment

Docker, Kubernetes, FastAPI, serving, A/B testing, monitoring.

Core

4.2

MLOps Pipelines

CI/CD, automated training, tracking, versioning, DVC, Airflow.

Core

4.3

Cloud ML Platforms

Azure ML, AWS SageMaker, GCP Vertex AI, registries.

Advanced Topics & Specialisation

Months 17-20

Specialist

5.1

Reinforcement Learning

Q-learning, policy gradients, DQN, multi-agent systems.

Specialist

5.2

Large Language Models

Fine-tuning, RAG systems, prompt engineering, evaluation.

Core

5.3

Production Capstone

Build and deploy production ML systems from scratch.

End-Point Assessment (EPA)

Months 21-24

Assessment

6.1

Professional Portfolio

BCS assessment of your production ML projects and evidence.

Assessment

6.2

Technical Interview

ML engineering knowledge, system design, problem solving.

Assessment

6.3

Professional Recognition

Achieve BCS Advanced RITTech accreditation.

Employer Value & ROI

Measurable business outcomes from your ML engineering investment.

📈

340%

Average ROI within 18 months from productivity and ML deployment.

⚡

Production ML systems deployed per learner year one.

💰

£127k

Average cost savings from ML automation.

🎯

92%

Retention rate post-programme.

ML Engineering Skills Impact on Business Value

Before: Manual processes

Baseline

After 6 months: First models deployed

+60% efficiency

After 12 months: Full ML pipeline

+90% automation

How We Compare

Feature	Our L6 ML Fellowship	University MSc	Online Bootcamp
Duration	✓ 24 mo (part-time, earn)	2 years full-time	3-6 months
Recognised Standard	✓ ST1398 Level 6	Academic degree	Certificate
Industry Certs	✓ 4 (Microsoft, BCS, NVIDIA)	None	1-2
Fully Funded	✓ Yes (Levy)	Student loans	£8-20k
Earn While Learning	✓ Yes	No	Varies
Production Projects	✓ 8+ real systems	2-3 coursework	Simulated
MLOps & Deployment	✓ Deep	Theory	Intro
1:1 Coach Support	✓ Throughout	Office hours	Forum-based
Professional Accreditation	✓ BCS RITTech	None	N/A
Employer Recognition	✓ BCS + Cloud	University	Variable

Frequently Asked Questions

Everything you need to know about the ML Fellowship Level 6.

Data scientists, ML engineers, software engineers, and technical professionals specialising in advanced machine learning systems. Strong programming foundations and comfort with mathematics required. No prior ML experience needed, but technical skills accelerate learning.

Solid programming experience (Python preferred), basic statistics, and mathematics comfort. No prior ML required. We teach from neural network foundations through production systems. Level 3 qualifications (GCSEs) minimum and motivation to specialise in ML engineering.

Key differences: earn while learning (stay employed), fully funded via Levy, 4 industry certifications, hands-on with 8+ production projects. Universities focus on theory; we focus on practical, deployable ML systems. Both Level 6 (degree-equivalent), but our pathway is faster, cheaper, and more immediately applicable.

Yes. The entire £27,000 cost is covered by the Apprenticeship Levy for eligible UK employers (payroll over £3 million). If your organisation doesn't pay the levy, we explore alternative funding. You're paid to learn — stay in your current role throughout.

You achieve: (1) ST1398 Level 6 ML Engineer regulated apprenticeship, (2) Microsoft Azure AI Engineer certifications, (3) BCS Advanced RITTech professional accreditation, (4) NVIDIA Deep Learning specialisation. All industry-recognised and immediately valuable in the job market.

Blended: live workshops (2-3 days/month), self-paced online modules, minimum 6 hours/week on-the-job training (OTJ). You remain in your role, so time integrates into your work. Total time is roughly equivalent to 1 university module whilst working full-time.