UK

United Kingdom · Technology and data · Director/VP (16-20 years)

Director of AI/ML

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandDirector/VP (16-20 years)
  • Direct reports3-5 reports
  • Reports toVP of Technical_roles
  • UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as Head of Artificial Intelligence · VP of Machine Learning Engineering · AI Strategy Lead

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 2 of 6 standards
ATHE Level 7 Extended Diploma in Computing ATHE Ltd · Level 7 · covers 1 of 6 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 2 of 6 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 2 of 6 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 6 standards

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Data Science Foundations OTHM Level 7 Diploma in Data Science
Applications of Machine Learning and Artificial Intelligence ATHE Level 7 Extended Diploma in Computing
Artificial Intelligence NCC Education Level 5 Extended Diploma in Computing with Data Science
Data Analytics Pearson BTEC Level 5 Higher National Diploma in Computing for England
Data-led Decision Making Level 6 Award in Data-led Decision Making
Apply the Concepts of Data Science to Computer Engineering NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science)

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Director of AI/ML

6 units that map to this job, from the qualifications that cover it.

  1. Data Science Foundations OTHM Qualifications · covers 2 of 6 standards Level 7
  2. Applications of Machine Learning and Artificial Intelligence ATHE Ltd · covers 1 of 6 standards Level 7
  3. Artificial Intelligence NCC Education Limited · covers 2 of 6 standards Level 5
  4. Data Analytics Pearson Education Ltd · covers 2 of 6 standards Level 5
  5. Data-led Decision Making Institute of Sales Professionals · covers 1 of 6 standards Level 6
  6. Apply the Concepts of Data Science to Computer Engineering NOCN · covers 1 of 6 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Director of AI/ML?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Generative AI & LLM Strategy
  • AI Governance & Auditability at Scale
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Director of AI/ML plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

This job sets the long-term vision for how AI drives a major business unit. It involves building and supporting the teams who write the code, making sure AI work moves the business forward. It means managing managers, shaping strategy, and taking accountability for a significant chunk of the P&L, making sure AI investments pay off.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

AWS/GCP/Azure (Multi-Cloud Strategy) Expert

Making strategic platform decisions, managing multi-cloud strategy, negotiating enterprise discounts, and setting organisation-wide security and cost policies (FinOps) for AI workloads.

Python (Framework Agnostic) Advanced

Evaluating emerging ML frameworks and hardware (e.g., TPUs vs. GPUs) for strategic fit, setting coding standards, and defining best practices for the entire organisation. You won't be coding, but you'll understand the implications.

MLflow/Kubeflow/Vertex AI (Enterprise MLOps) Expert

Selecting, owning, and governing the enterprise MLOps platform. Focus on governance, auditability, and scalability of the entire ML ecosystem across multiple teams.

Kubernetes (K8s) (Cluster Architecture & GitOps) Advanced

Architecting cluster configurations, setting namespace strategies, managing Ingress controllers, and implementing GitOps principles (e.g., with ArgoCD) for the department's AI infrastructure.

Databricks/Snowflake (Data Platform Governance) Expert

Governing the entire data platform for AI. Making decisions on data warehousing vs. lakehouse architecture, setting data access policies, and managing platform budget and cost optimisation.

Jira Align/Anaplan (Portfolio Management) Advanced

Managing portfolio-level roadmaps, aligning engineering capacity with business objectives, and reporting on program status to executive leadership. This is about strategic oversight, not sprint planning.

GitHub/GitLab Enterprise (Security & CI/CD Infrastructure) Advanced

Setting repository permissions, defining security policies (e.g., using Dependabot, branch protection rules), and managing the CI/CD runner infrastructure for the entire AI engineering department.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Strategic AI Roadmap Definition N/A N/A Contributes technical insights and feasibility assessments to the roadmap discussions.
Budget Allocation (AI Spend) N/A N/A Estimates cloud costs for specific model training runs or deployments.
Hiring & Organisational Design N/A N/A Participates in technical interviews for junior and mid-level roles.
AI Ethics & Governance Policy N/A N/A Follows established ethical guidelines in model development and data handling.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

AI-Driven Revenue Growth
The incremental revenue generated or influenced by AI-powered products or features within your business unit.
Target · Contribute to >£5M in new revenue or 10% uplift in existing revenue streams annually.

Your team launches an AI-driven personalisation engine that increases average customer spend by 12%, directly contributing £7M in additional revenue for the year.

Operational Cost Reduction via AI
The measurable cost savings achieved through AI-driven automation or optimisation within the business unit's operations.
Target · Reduce operational costs by £2M-£5M annually through AI initiatives.

An AI-powered fraud detection system, led by your team, reduces false positives by 30% and manual review time by 50%, saving the business unit £3.5M in operational expenses.

AI Talent Retention Rate
The percentage of AI/ML engineering talent retained within your business unit year-over-year.
Target · Maintain an annual attrition rate below 10% for AI/ML roles.

Out of 50 AI/ML engineers in your department, only 4 leave in a given year, demonstrating strong team health and leadership.

Strategic Roadmap Execution
The percentage of key AI initiatives on the annual strategic roadmap that are delivered on time and within budget.
Target · Successfully execute 90% of the annual AI strategic roadmap initiatives.

You planned 10 major AI projects for the year, and 9 were delivered as planned, with the remaining one being strategically deprioritised rather than simply failing.

Strategic Influence & Thought Leadership
Your ability to shape the broader company's AI narrative, influence executive decisions, and represent our organisation as an AI leader externally.
  • Regularly invited to present to the Executive Board on AI strategy
  • frequently consulted by other VPs on AI opportunities
  • invited to speak at industry conferences
  • active participation in industry forums
  • successful internal advocacy for significant AI investments.
Organisational Design & Team Development
How effectively you've structured your teams, developed your managers, and fostered a culture of innovation and continuous learning.
  • High internal promotion rate within your teams
  • positive feedback from skip-level reports on manager effectiveness
  • clear career pathways defined and communicated
  • successful onboarding of senior hires
  • demonstrable improvements in team collaboration and knowledge sharing.
Cross-Functional Collaboration Effectiveness
The strength of your relationships with other department heads (Product, Engineering, Sales, Marketing) and your ability to drive joint initiatives.
  • Regularly co-leading initiatives with other department VPs
  • documented examples of successful cross-functional AI projects
  • positive feedback from peer leaders on your team's partnership
  • proactive identification and resolution of inter-departmental dependencies.
Risk Management & Governance
Your oversight of AI ethics, compliance, and operational risks, ensuring our AI systems are responsible, secure, and robust.
  • No major AI-related compliance breaches or ethical incidents
  • proactive implementation of model governance frameworks (e.g., model cards, data sheets)
  • robust incident response plans for production AI systems
  • regular audits of AI systems for fairness and bias.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Driving Business Transformation through AI

You'll spend your days identifying new areas where AI can create significant business value, defining the strategic roadmap, and securing the resources to make it happen. You'll get a kick out of seeing your vision become a reality that impacts the bottom line.

Successfully launching an AI-powered pricing engine that directly increases gross margin by 5% for the business unit, after years of strategic planning and team execution.

Building and Nurturing High-Performing Teams

A significant part of your role is about hiring exceptional talent, developing your managers, and fostering a culture where AI engineers thrive. You'll find immense satisfaction in seeing your direct reports grow into strong leaders and your overall team deliver outstanding work.

Mentoring two of your Lead Engineers into AI/ML Engineering Managers, and seeing your overall team's engagement scores rise year-on-year.

Solving Complex Organisational & Technical Challenges

You'll be tackling problems that aren't just technical, but involve organisational politics, budget constraints, and strategic trade-offs. You'll enjoy navigating these complexities to create scalable, impactful AI solutions that work across the business.

Successfully integrating a new MLOps platform across multiple teams, overcoming initial resistance and demonstrating clear efficiency gains, leading to a 20% reduction in deployment time.

What frustrates people
  • The constant need to justify significant AI investments (both people and cloud) to non-technical finance leaders.
  • Dealing with shifting strategic priorities from the Executive Board that can derail multi-month AI projects.
  • The challenge of attracting and retaining top-tier AI/ML talent in an incredibly competitive market.
  • Managing the expectations of business stakeholders who often overestimate AI's current capabilities and underestimate the effort required.
  • Navigating organisational politics and securing buy-in from other department heads for cross-functional AI initiatives.
What this role does not give you
  • Daily hands-on coding or model building—your focus is on strategy, leadership, and enablement.
  • A predictable, unchanging environment; expect constant shifts in priorities and technology.
  • An easy ride; this role comes with significant pressure and accountability for large-scale outcomes.
  • The luxury of avoiding difficult conversations about budget, performance, or strategic direction.

8Who you work with

This role directly drives the competitive advantage of a major business unit through AI innovation. It influences product roadmaps, operational efficiency, and revenue growth. Your decisions will shape the talent strategy for AI, impacting our ability to attract, retain, and develop top-tier engineering talent, and ultimately our long-term market position.

Inside the business
  • VP of Product Management
  • Head of Engineering
  • CFO and Finance Leadership
  • Heads of Business Units (e.g., Sales, Marketing, Operations)
  • Legal and Compliance Teams
Outside the business
  • Key Technology Vendors (e.g., Cloud Providers)
  • Industry Research Partners
  • Recruitment Agencies (for senior talent)
  • Potential M&A Targets (for capability acquisition)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • Proven experience (at least 5 years) in a senior leadership role (e.g., AI/ML Engineering Manager, Lead Staff ML Engineer) overseeing multiple teams or large-scale AI programmes.
  • Demonstrable track record of defining and executing a successful AI strategy that delivered significant, measurable business impact.
  • Expertise in building and scaling high-performing AI/ML engineering teams, including hiring, mentoring, and performance management of managers.
  • Deep technical understanding of modern ML architectures, MLOps practices, and cloud platforms, even if you're not coding daily.
  • Exceptional executive presence and communication skills, with a proven ability to influence C-suite stakeholders and external partners.
  • Strong financial acumen, including experience managing large budgets and justifying technology investments to finance leadership.

10What to practise next

Where the job is going, and what to do about it starting this week.

AI Hardware & Infrastructure Optimisation

The cost and performance of AI workloads are heavily dependent on underlying hardware and infrastructure. As models grow larger and more complex, optimising cloud spend and selecting the right compute (e.g., custom accelerators, serverless ML) becomes a strategic imperative. You'll need to make informed decisions that impact both budget and capability.

Cloud Cost Optimisation for ML (FinOps) · Specialised AI Accelerators (e.g., TPUs, custom ASICs) · Edge AI Deployment · Green AI & Sustainable ML

  • This quarter: Review your business unit's current cloud spend for AI, identifying top cost drivers and potential areas for optimisation with your FinOps team.
  • Next quarter: Task a Lead Engineer to research and present a proposal on the strategic adoption of a new AI accelerator or serverless ML platform for specific workloads.
  • Month 6: Implement a 'cost of inference' metric for all production models, driving accountability for efficiency across your teams.
  • Month 9: Engage with cloud vendors to negotiate better pricing models or explore new services that align with your long-term AI infrastructure strategy.

Quick win: Mandate that every new model deployment includes a cost estimate and a plan for ongoing cost monitoring. Encourage teams to use spot instances for non-critical training jobs.

Data Mesh & Feature Store Strategy

As AI systems become more distributed and data sources proliferate, managing data effectively is paramount. A data mesh approach, combined with a robust feature store, can unlock greater agility, consistency, and reusability of data assets across your business unit, preventing data silos and improving model quality.

Data as a Product Principles · Decentralised Data Governance · Feature Store Architecture & Adoption · Data Discovery & Catalogue

  • This quarter: Partner with Data Platform leadership to assess the current state of data management across your business unit and identify key pain points for AI teams.
  • Next quarter: Sponsor a proof-of-concept for a data mesh or feature store initiative, focusing on a critical AI project that struggles with data consistency.
  • Month 6: Develop a strategic roadmap for evolving our data architecture to better support distributed AI development, securing buy-in from relevant VPs.
  • Month 9: Begin allocating resources and defining clear ownership for data domains within your business unit, aligning with data mesh principles.

Quick win: Encourage your teams to document all features used in production models in a shared wiki or simple registry, including their definition, source, and transformation logic.

11Staying current once you are in

What people here do to keep up
  • Regularly attending and speaking at industry conferences (e.g., NeurIPS, ICML, KDD, Re:Invent, Google I/O) to stay abreast of the latest AI research and network with peers.
  • Participating in executive leadership programmes or workshops focused on strategic decision-making, organisational change, and talent management.
  • Engaging with academic institutions or research labs to explore potential partnerships and stay connected to fundamental AI advancements.
  • Mentoring rising talent within the AI/ML community, both internally and externally, to foster a strong talent pipeline and give back to the field.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Generative AI & LLM Strategy

Generative AI, especially Large Language Models (LLMs), is fundamentally changing how businesses interact with data, automate content creation, and build new products. Competitors are already using these to gain significant advantages in productivity and innovation. As a Director, you need to define how we strategically adopt and integrate these capabilities across our business unit.

  • RAG (Retrieval Augmented Generation) Architectures
  • Agentic AI Systems
  • Model Fine-tuning & Customisation
  • Ethical & Safety Guardrails for Generative AI

AI Governance & Auditability at Scale

With increasing regulatory scrutiny (e.g., EU AI Act) and the growing complexity of AI systems, robust governance and auditability are no longer 'nice-to-haves' but critical necessities. As a Director, you'll be accountable for ensuring all AI systems within your domain meet these stringent requirements, protecting the business from significant legal and reputational risks.

  • AI Risk Management Frameworks
  • Automated Model Monitoring & Alerting
  • Digital Product Passports for AI
  • Explainable AI (XAI) Policy & Implementation

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • ML System Design (Organisational Scale)
  • Agile for Research & Development (Portfolio Management)
  • Model Governance & Explainability (Enterprise Frameworks)
  • Cloud FinOps for ML (Strategic Cost Management)
  • Technical Mentorship & Coaching (Leaders)
  • Stakeholder Translation (Board Level)

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    AI/ML Engineering Manager (Large Enterprise)

    5-8 years as Manager

    Skills to master

    • Scaling teams, managing multiple AI programmes, strategic budget management, executive communication, cross-functional leadership.

    You're ready to move on when

    • Successfully managed 3+ AI/ML teams (15+ engineers total) and delivered multiple high-impact projects.
    • Consistently met or exceeded budget targets for their domain.
    • Demonstrated ability to influence product roadmaps and engineering strategy at a departmental level.
    • Mentored and developed other managers or lead engineers to take on greater responsibility.
  2. 2

    Lead/Principal Staff ML Engineer (Large Enterprise)

    8-12 years as Staff/Principal

    Skills to master

    • Deep technical architecture for large-scale ML systems, influencing technical strategy across multiple teams, leading complex technical initiatives, informal mentorship of senior engineers.

    You're ready to move on when

    • Architected and delivered multiple enterprise-level ML systems that are critical to the business.
    • Recognised as a thought leader and technical authority within the organisation and potentially externally.
    • Successfully driven adoption of new technologies or best practices across multiple engineering teams.
    • Consistently provided strategic technical guidance that shaped the direction of major AI programmes.
  3. 3

    Head of AI/ML (Mid-Sized Company/Scale-up)

    3-5 years as Head of AI

    Skills to master

    • Building an AI function from the ground up, full P&L accountability for AI, investor relations (technical aspects), rapid scaling of teams and infrastructure.

    You're ready to move on when

    • Successfully built and scaled an AI team (20+ engineers) in a fast-growing environment.
    • Delivered significant, measurable business impact through AI in a competitive market.
    • Managed the full AI budget and made strategic technology decisions for the entire company's AI efforts.
    • Effectively communicated AI strategy and progress to the CEO and board.

15Where this role leads

The long view: This work can lead to roles such as Chief AI Officer or Chief Technology Officer, shaping technology strategy at the highest level. It can also lead to running a business unit as General Manager, where AI is one part of the wider job.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Director of AI/ML is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • VP of AI/ML
  • Head of Product (AI-focused)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Data Science Foundations, worked against Director of AI/ML.

Data Science Foundations Level 7

Applied to your work in Director of AI/ML

1. To enable the learner to define the scope and landscape of Data Science and differentiate the roles of Data Scientists from other IT professionals. 2. To enable the learner to evaluate key topics within Data Science, including data administration, governance, and big data sources. 3. To enable the learner to describe the architecture and core elements of Apache Hadoop. 4. To enable the learner to analyse the advantages and disadvantages of utilising Artificial Intelligence techniques in a business context. 5. To enable the learner to critically analyse the impact of Big Data on digital transformation within organisations and its effects on users. 6. To enable the learner to review strategies for ensuring data compliance and explain the responsibilities and challenges faced by data specialists.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

Start my Director of AI/ML plan, free
See Your Progress Grow Illustration
Director of AI/ML
  • ML System Design (Organisational Scale)
  • Agile for Research & Development (Portfolio Management)
  • Model Governance & Explainability (Enterprise Frameworks)
  • Cloud FinOps for ML (Strategic Cost Management)
  • Technical Mentorship & Coaching (Leaders)
  • Stakeholder Translation (Board Level)
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Director of AI/ML is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. VP of AI/ML

    3-5 years

    Level 7

    • M&A strategy and integration for AI companies.
    • Public speaking and industry thought leadership at a global scale.
    • Regulatory advocacy and influencing AI policy.
    • Global talent strategy and executive recruitment.

  2. Head of Product (AI-focused)

    3-5 years

    Level 6/7 (depending on scope)

    • Product vision and roadmap ownership for an AI product portfolio.
    • Pricing and monetisation strategies for AI features.
    • Cross-functional leadership of product, engineering, and design teams for AI.
    • Driving product-led growth for AI solutions.
Working with AI on the job

Working with AI

Where AI is starting to help

As a Director of AI/ML, your time is precious. You're not coding every day, but you are constantly reviewing strategies, assessing team performance, and crafting high-level communications. The good news is, AI isn't just for your engineering teams; it's a powerful assistant for you too. Imagine cutting down on tedious tasks and amplifying your strategic impact.

We're embedding AI-powered tools directly into our workflows, not just for engineers, but for leaders like you. This means less time wrestling with reports, more time focusing on what truly matters: setting vision, developing your people, and driving transformational change. Here's a glimpse of how AI can give you back valuable hours every week:

Strategic Document Drafter

Use advanced LLMs to draft initial versions of your quarterly business reviews, board presentations, or strategic AI roadmaps from your bullet points and meeting notes. You'll refine the output, but the heavy lifting of structure and initial content is done for you. This frees you up to focus on the core message and executive-level insights.

Portfolio Performance Synthesizer

Connect AI-powered analytics to your Jira Align, Anaplan, and cloud cost dashboards. Get automated summaries of your entire AI portfolio's performance, highlighting key risks, budget overruns, or projects ahead of schedule. This means less manual data crunching and more time making informed strategic decisions.

Emerging Tech Trend Analyst

Deploy an AI assistant to continuously monitor arXiv, industry news, and competitor announcements for emerging AI technologies relevant to our business unit. It'll summarise key innovations, potential threats, and opportunities into concise briefings, keeping you ahead of the curve without endless reading.

Executive Comms Assistant

Leverage generative AI to craft the first pass of sensitive emails, internal announcements, or external thought leadership pieces. It helps ensure clarity, tone, and conciseness, allowing you to focus on the strategic nuance and personal touch. Imagine having a ghostwriter who understands our brand voice.

Common questions

Common questions

How do you become a Director of AI/ML?

Common routes in include AI/ML Engineering Manager (Large Enterprise) (5-8 years as Manager), Lead/Principal Staff ML Engineer (Large Enterprise) (8-12 years as Staff/Principal) and Head of AI/ML (Mid-Sized Company/Scale-up) (3-5 years as Head of AI). Times vary with prior experience.

Where can a Director of AI/ML progress to?

This role can lead on to VP of AI/ML (3-5 years) and Head of Product (AI-focused) (3-5 years), depending on the skills you build.

What new skills matter most for a Director of AI/ML?

Increasingly, Generative AI & LLM Strategy and AI Governance & Auditability at Scale. These are the skills our record of this role lists as growing.

Free tools

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17Where to go from here

Other roles at Level 7

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built in this role transfer across many industries, including FinTech, HealthTech, e-commerce, automotive and consulting. Strategic leadership, team building and AI expertise are in demand across sectors.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my Director of AI/ML plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.