Tailored Delivery Options

  • Self-paced immersive eLearning
  • Bespoke programmes
    and bootcamps
  • 12 month Fellowship
  • Apprenticeship (England)
The Academy Delivery The Curriculum FAQs Register your interest View the prospectus

The Academy

Scale Machine Learning capability across your organisation

Cambridge Spark's Machine Learning Engineer Academy will equip your employees from a software engineering background with an advanced skill set to develop and deploy machine learning models into production and scale them across an organisation using MLOps practices.

Learner Outcomes:

  • Be able to put into production the models created by data science teams and incorporate MLOps practices across the organisation
  • Feed and manage the data being fed into those models to optimise and automate business processes
  • Provide technical authority, direction and strategic guidance for the business on emerging opportunities and insights for AI that are relevant to business goals

Organisation Outcomes:

  • Scale AI and machine learning capability across the organisation effectively
  • A higher return on investment from Data and AI projects which can now be successfully deployed and maintained into production

Who The ML Engineer Academy Is For:

  • Software engineering teams that work with data science models
  • Any employee with a software engineering background looking to upskill for AI and work with machine learning models.


Experts In Online Delivery

We deliver all of our programmes online, helping our clients offer flexible and inclusive programmes open to all of their staff. We believe that the gold standard for online delivery is to offer a mix of experiential learning, coaching, technical mentorship and peer support.

  • Live instructor-led lectures
  • Technical mentor
  • Experienced coach
  • EDUKATE.AI, our online learning platform for data science
  • Industry Expert Community
  • Work-based Projects

The Curriculum

Our curriculum is developed by our leading Faculty, made up of data scientists in leading industry positions and academics from some of the top universities in the world. Our curriculum is continuously updated and reiterated to incorporate the most cutting-edge skills.

We take a modular approach to how we offer our curriculum. The full Machine Learning Engineer Academy learning pathway includes all of the below modules. We also offer curated shorter tracks and can offer a fully tailored pathway based on a skills gap analysis.

  • The Data Science ToolboxUse of some of the most common, industry standard tools for conducting data analysis and data science in Python.
  • Data Science for BusinessIdentify practical applications and use cases for Data Science & AI to deliver and create business value.
  • Databases SQL & NoSQLLearn how to use SQL and NoSQL to store, query and retrieve structured and unstructured data.
  • Maths for Data ScienceBuild an advanced understanding of key mathematical concepts that underpin much of the AI and Data Science domain.
  • Introduction to Machine LearningBuild familiarity with a range of advanced concepts and tools required to use different types of machine learning models and techniques.
  • Supervised LearningUse an array of discriminative and generative supervised learning models as well as sophisticated techniques to evaluate model suitability and improve model performance.
  • Unsupervised LearningLearn a range of unsupervised learning models and techniques to reveal latent structure within data, including KMeans, hierarchical clustering, DBSCAN, PCA and t-SNE.
  • Time Series AnalysisBuild an advanced understanding of tools and testing techniques for working with time series data with Python, Pandas, Numpy, the Prophet library as well as autoregressive models.
  • Big Data SystemsLearn how to apply and leverage the power of distributed computing to extract value & insight at scale.
  • Software Testing for Data ScienceGain familiarity with advanced approaches and techniques for conducting testing, diagnostics and validation of Machine Learning Systems.
  • Principles of Cloud ComputingBuild familiarity with cloud computing infrastructure covering common cloud services, the differences between virtualisation and containerisation and the fundamentals of working with Docker.
  • Data Privacy, Ethics and RegulationsGet up to speed with the essentials covering ethical, legal and regulatory issues relating to the use of dat.
  • Practical HackathonWork collaboratively in teams on a real-world project to apply newly acquired skills in a realistic simulated environment.
  • Ensemble MethodsGain familiarity with ensembles, covering a range of key concepts including bagging and random forest, boosting & gradient boosting, stacking, advanced SKlearn Techniques & Support Vector Machines.
  • Neural Networks and Deep LearningLearn how neural networks are constructed and trained and how to use them in practice including CNN, RNN, GANs and Graph Neural Networks.
  • Natural Language ProcessingLearn about the main applications and techniques of NLP and how to build models for and evaluate approaches to supervised and unsupervised sentiment analysis.
  • Model Explainability and InterpretabilityUnderstand the different approaches and techniques for interpreting and explaining a range of machine learning models and deep neural networks.
  • Pragmatic Techniques for Model EvaluationGain familiarity with a suite of evaluative techniques to tackle different types of data science problems for different situations and purposes.
  • Product Management for AIDevelop a customer-centric product mindset and focus on understanding users to build products that solve their problems and serve their needs.
  • Machine Learning in ProductionLearn to build, implement, deploy and monitor machine learning projects, taking them from the research phase into full production.


What Delivery Options Do You Offer?

We tailor our delivery to your workforce needs. This ranges from from independent, immersive elearning supported by EDUKATE.AI through to tailored bootcamps to our structured Fellowship and Apprenticeship programmes. The Level 7 AI Data Specialist Apprenticeship is available to learners based in England undertaking the ML Engineer Academy.

Are You Able To Tailor The Programme To The Organisation And Sector?

Yes. We work with our clients to contextualise our programmes to their organisation and sectors they operate in. We do this through tailored hackathons, bespoke assignments and guest lectures from industry experts. We also work with a range of partners to create bespoke programmes for sectors including health, retail and journalism.

What Is An Apprenticeship?

Apprenticeships are a long-term training commitment which seek to support people into the workforce and upskill existing UK-based employees within an organisation, enabling employers to foster a workforce consisting of highly-skilled and highly-engaged talent.

The Cambridge Spark Level 7 AI Data Specialist Apprenticeship is delivered over 15 months plus 3 months End Point Assessment and includes a minimum of 20% off-the-job training, enabling a blended approach between theory and practical-learning.

What Is The Apprenticeship Levy?

The UK government’s Apprenticeship Levy scheme came into effect in April 2017 as a way to drive investment in strengthening the country’s skills base.

All organisations with annual staff costs of over £3m have to pay 0.5% of their salary bill into a ring-fenced apprenticeship levy pot. The money is collected monthly via PAYE and must be spent within 24 months and used for training on approved apprenticeship schemes (such as the Level 7 AI Data Specialist Apprenticeship that we offer).

What Does 20% Off-The-Job Training Mean?

Off-the-job training is defined as learning which is undertaken outside of the day-to-day work duties and it must take place within the apprentice’s normal working hours.

Our off-the-job training is delivered on a flexible basis and can be carried out at the apprentice’s place of work or from home.

The 20% off-the-job training provides learners with the time to focus and develop the required skills, knowledge and behaviours to achieve the apprenticeship.

How Much Do Managers Need To Be Involved?

Managers will need to ensure apprentices achieve the 20% off-the-job training hours and work on their project portfolio.

We also encourage managers to have regular one-to-one meetings with apprentices to catch up on how they are progressing and to join the apprentice and their coach for thirty minutes every 3-4 months for a general catch up about the programme.

Register your interest

Fill out the following form and we’ll email you within the next two business days to arrange a quick call to help with any questions about the programme.

We look forward to speaking with you.

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