Machine learning consulting in the UK
Building machine learning models is one thing — making them actually work for your business is another. From unreliable predictions to models that can’t scale, companies often hit roadblocks that stall progress and drain resources.
That’s where Vention’s machine learning consulting stands apart. Our UK-based experts cut through the complexity, fine-tuning your AI to be faster, smarter, and built for real-world impact.
Machine learning in the UK: key market insights
As a subset of artificial intelligence, machine learning is a driving force behind AI’s rapid advancement. With the UK market size projected to reach £12.82B by 2030 and the British AI sector ranking third globally, the technology is on track for nationwide adoption.
Its true strength lies in versatility: machine learning can seamlessly integrate into virtually any application. From web clients and mobile apps to backend-only solutions, it enhances efficiency, decision-making, and automation wherever it’s applied.
Our machine learning consulting services
From infrastructure assessment to post-launch support, Vention is a machine learning consulting company that gets it right the first time. We tailor every solution to your needs and aspirations, delivering peace of mind at every stage.
Strategy development
Not sure how to approach ML? Our machine learning consultancy will help you identify the best starting point. We’ll also draft a step-by-step implementation plan, ensuring everything is mapped out clearly.
Technology advisory and selection
One of the most crucial steps in developing ML solutions is selecting a tech stack that does the job without unnecessary complexity or costs. Our experts will guide you through the vast landscape of ML frameworks and tools, helping you choose the best fit.
Process analysis
Could your processes benefit from automation? Our machine learning consultants will thoroughly assess your business operations, pinpointing where and how ML solutions can make them faster, smarter, and more efficient.
ML development and implementation
Vention is not just a machine learning consulting: we're also skilled developers who can turn your ideas into fully functional solutions, quickly and with full transparency. With a large pool of ML experts, we’ll kick off your development project without delays.
ML model evaluation and review
An expert’s job doesn’t end with implementation, especially when it comes to innovative models. Upon request, our specialists monitor and fine-tune your ML models to prevent degradation or drift. We proactively identify anomalies, tackling their root causes before they turn into costly problems.
Machine learning solutions you can benefit from right now
Every business requires a delicate touch, and machine learning consulting services are no exception. We consider one’s specific domain, peculiarities, and aspirations — not just picking a suitable framework or platform to deploy ML solutions but also tailoring the functionality for optimal impact.
Natural language processing (NLP)
From chatbots to advanced document analysis tools, NLP plays a key role in both internal and customer-facing processes. Its precision and adaptability allow it to efficiently gather customer feedback, extract key points from documents, and even generate personalised content tailored to each customer.
Computer vision (CV)
Interpreting visual data automatically is a game-changer for many businesses. Applications range from crop and livestock monitoring in agriculture to advanced quality control in manufacturing and even smart security systems with sensors that capture information beyond human vision.
Speech recognition
Speech recognition can enhance efficiency across multiple business functions. Meetings and customer support can be upgraded with automatic transcription, which also distinguishes between speakers. Chatbots can gain their own voice and hearing, making customer interactions smoother and more natural. And by adding voice control to existing solutions, businesses empower employees and users with a truly hands-free experience.
Neural networks
Autonomous decision-making demands precision. We develop neural networks that process vast amounts of data to deliver fast and accurate results. Whether tailored TV recommendations or optimised delivery route planning, these models serve as invaluable assistants to real-time operations.
Unstructured data analysis
Need to analyse large volumes of online text, multimedia files, and internal data tables simultaneously? Say no more. Unstructured data analysis tools handle multiple data formats at once, making them more versatile than standard ML models. And the more data they process, the more accurate the results become.
Time series analytics
Data is sequential and time-dependent, so understanding its patterns is key. By analysing how data evolves, you can uncover hidden trends that fuel business growth. From emerging market shifts to seasonal and cyclical patterns, nothing escapes the insights of a well-built analytics platform.
Here from our expert
“Startups frequently choose AI solutions centred around large language models (LLMs) and retrieval-augmented generation (RAG) applications due to their adaptability and rapid deployment in solving diverse business tasks — from customer service automation to content creation. My team has extensive experience working closely with both emerging and well-established startups across various industries, including fintech, medtech, and beyond, which enables us to effectively tailor AI solutions to diverse business needs.
Small and medium-sized businesses typically benefit from AI-driven analytics and process automation solutions that directly enhance efficiency and cost-effectiveness. Enterprises, meanwhile, are increasingly interested in comprehensive AI integration strategies, including custom AI model development, explainable AI (XAI) solutions, and scalable autonomous driving technologies. These solutions not only streamline operational workflows but also provide valuable strategic insights, reduce risks, and significantly enhance competitive advantage.”
Makhmudjon Sodikov
Machine Learning Engineering Manager at Vention
Wondering about machine learning consulting costs?
Every project is unique, but you don’t have to start blind. Use our project calculator for a rough estimate, or reach out — our consultants will get back to you within hours.
How we help businesses adopt machine learning
No matter the size or stage of your business, it’s never too early — or too late — to integrate machine learning into your operations.
Startups
Small and medium-sized businesses (SMBs)
Enterprises
How to get started
Startups
- Use open-source ML tools like TensorFlow, PyTorch, or scikit-learn.
- Leverage AI-as-a-service platforms like Google Cloud AI, AWS AI, or Open AI APIs (you also can save big on AWS with Vention’s partner benefits).
- Start small, then grow bigger, adding new features and complex processes as your business develops.
- Find a trusted vendor to help you on the way.
Small and medium-sized businesses (SMBs)
- Start small, and grow your product as your business develops.
- Leverage AI tools and SaaS platforms.
- Take a look at AI-as-a-service platforms like AWS, GCP, or Azure.
- Invest in your employees’ AI skills.
- Partner with a reliable vendor to develop your machine-learning product.
Enterprises
- Develop a strategy for your machine learning advancements.
- Invest in infrastructure for your ML tools, leverage platforms like GCP, AWS, or Azure.
- Use enterprise AI platforms like Salesforce Einstein, IBM Watson, or SAP AI.
- Put emphasis on expanding your AI talent pool through hiring, partnering with skilled vendors, or upskilling your employees.
- Make sure your data is ready for machine learning.
- Adopt compliance and ethics practices.
Vention’s workshop helps you avoid the costly mistake of investing in the wrong ML initiative. Our specialists will guide you through:
- Identifying the best areas in your business for machine learning;
- Assessing your data readiness for ML implementation;
- Mapping out the key steps to ensure a successful ML product.
Make one informed decision — so that every decision after it is the right one.
Our approach — your results
Implementing machine learning, when done right, is like having the odds stacked in your favour. You make decisions with confidence, knowing the outcome before it unfolds.
Data-driven decisions
- ML models transform complex datasets into clear, informed decisions.
- Live data analytics enable swift, precise responses to market shifts.
- Predictive analytics help you anticipate trends and make proactive moves.
Enhanced efficiency
- Machine learning streamlines workflows and reduces human error.
- Intelligent systems efficiently prioritise tasks and automate their execution.
- Process automation frees up your employees to be more strategic and creative in their work.
Unparalleled customer satisfaction
- Chatbots and virtual assistants provide 24/7 customer support, even on weekends and holidays.
- Smart recommender systems personalise the customer experience, ensuring the most relevant options.
- ML-powered systems proactively engage customers, reminding them about items in their shopping cart, upcoming events, or subscription renewals.
Reduced costs
- Predictive maintenance systems analyse how your equipment is used and schedule maintenance before it breaks down and causes delays.
- Inventory management and demand forecasting tools help keep you well- but not overstocked.
- Smart logistics platforms reduce the time and costs of transporting your inventory between warehouses and customers. As a bonus, your environmental impact can also be reduced.
We transform your industry
With expertise across 30+ domains, we make machine learning work for you — because we’ve seen it all and know exactly what turns AI from hype into ROI.
Retail and ecommerce
Healthcare
Finance
Manufacturing
Martech & adtech
Media and entertainment
Transportation & logistics
Security
Agriculture
Our clients say it best
Vention has been very cost-effective, providing high-calibre engineering staff at a competitive cost.
I was impressed with their creativity and desire to remain competitive when our business looked towards an offshoring strategy for permanent staff.”
Graeme Lindsay
Our clients say it best
Team members provided have integrated seamlessly with our team and worked well.
We've been able to add extra resource where it's needed within a week or so, and ramp them down when done. Selecting new team members is easy, with a very high hit rate of staff proposed to those selected to join our team. Any issues have been discussed proactively and resolved well."
Gordon Griffin
Our clients say it best
We were able to put our MVP into production within 3 months of inception and continue to issue new releases on a regular basis.
This has allowed us to capture new clients on an ongoing basis.”
Jon WadJon Wadee
Our clients say it best
Not only they were highly skilled professionals in their own domain, but also they have good communication skills.
We learnt from each other on technical knowledge and also on project/ product management side. They were very engaging and proactively listening for feedbacks and improve ways of working.
Jun Seki
Common machine learning challenges and how we resolve them
Venturing into machine learning comes with its own set of industry-specific challenges. With our team by your side, you can cut through the complexity and move straight to a solution that works.
Description
Solution
Data quality and availability
Machine learning models require large, high-quality datasets, which can be a challenge if your data is incomplete, biased, or inconsistent.
- Perform data cleaning and preprocessing.
- Use synthetic data generation when real-world data is scarce, or augment your dataset with external sources where available.
ML model overfitting/underfitting
Overfitting occurs when a model performs well on training data but fails on new data. Underfitting means the model is too simplistic and doesn’t capture patterns effectively.
- Apply regularisation techniques like Lasso (L1) or Ridge (L2) to prevent overfitting.
- Use cross-validation to improve generalisation.
- Increase training data variety and fine-tune models through hyperparameter optimisation.
Interpretability and explainability
Many ML models function as “black boxes,” making them difficult to explain — especially in regulated industries like finance, healthcare, and legal sectors that require explainable AI (XAI).
- Implement explainability techniques like SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-Agnostic Explanations).
- Where possible, opt for simpler models that provide clearer reasoning.
Scalability
As data volume grows, ML models can become slower and more expensive to train.
- Leverage cloud-based ML services like Google Cloud AI, AWS AI, or Azure ML.
- Leverage frameworks like Apache Spark or TensorFlow, which enable training across multiple GPUs or servers.
- Apply model optimisation techniques such as quantisation, pruning, and knowledge distillation.
Bias
ML models inherit biases present in their training data, potentially leading to unfair or discriminatory outcomes.
- Introduce bias audit and fairness metrics with tools like IBM AI Fairness 360 or Google’s What-If
- Ensure data diversity and representativeness.
- Perform adversarial debiasing where needed.
Data privacy & security
Collecting and processing personal data raises privacy concerns, requires regulation compliance like HIPAA or GDPR, and makes ML models a target for cyberattacks
- Store and process data in a secure environment with end-to-end encryption enabled
- Follow regulations compliance
- Make your ML privacy-preserving with techniques like federated learning, differential privacy, and homomorphic encryption
Deployment and integration
Deploying ML models can be a complex task on its own. What’s more, the solution must integrate seamlessly with existing business infrastructure
- Refer to MLOps (machine learning operations) for automated model deployment and monitoring with the help of tools like Kubeflow or MLflow
- Implement containerisation for better scalability and portability
- Set up real-time monitoring for your ML models
- Make sure your existing infrastructure is capable of efficiently working with ML models. Modernise it if needed
Costs
Training models requires significant GPU/TPU resources, and running ML-based applications at scale increases infrastructure costs
- Leverage cloud AI services that offer pay-as-you-go pricing
- Optimise your ML models
- Use edge AI for real-time data processing where applicable
Concept drift
ML models become less efficient over time as data patterns change.
- Introduce continuous model retraining.
- Use drift detection tools like Evidently AI or NannyML.
- Continuously update training data with relevant entries.
Why Vention?
Years of experience developing custom solutions
Engineers with AI-specific skill sets
AI projects successfully finished
Assistance in choosing stacks that reduce both upfront and ongoing maintenance costs
An ISO 27001-certified company
Delivery on time, on budget, and on scope
Tailored apps for every platform, device, and user
Solutions compliant with all major healthcare regulations, including HIPAA, GDPR, and HITECH
How we approach machine learning projects
From planning to deployment, we execute each step carefully, ensuring a fully functional product from the start.
Assessment
Before developing your machine learning model, our consultants dive into your business processes and infrastructure to ensure everything is ready for transformation.
We assess data sources, software nodes, and workflows, crafting a clear project plan and implementation strategy. This way, you see exactly how ML fits into your existing setup and make informed decisions before execution.
Modelling
Once the assessment is complete and the plan is set, our machine learning engineers step in to build models tailored to your needs.
This is also where we choose the right tools and algorithms, processing data, and training models to ensure they’re ready for real-world applications.
Integration
When your ML models are production-ready, we seamlessly integrate them into your infrastructure, making the solution fully operational.
To minimise disruptions, we first deploy models in a controlled environment that mirrors your real infrastructure, which allows us to identify and resolve any inconsistencies, bugs, or errors before the final rollout. Once the model meets performance expectations, it’s fully deployed for real-world use.
Support
Our commitment doesn’t stop at deployment. Upon request, we monitor your models to ensure they perform as expected and make adjustments as needed.
As your business scales, we’re ready to expand your ML models’ capabilities, whether by enhancing functionality or increasing bandwidth to keep up with growing demands.
Our work
All cases
Technologies & tools we use
At Vention, we work with the latest and greatest in machine learning technology. Want a peek under the hood? Here’s just a snapshot of the powerful tools we use to bring ML solutions to life.
Cloud tools and applications
AWS ML tools
Azure Machine Learning
Google AI Platform
Google Cloud AutoML
Jupyter Notebooks
Tableau
Frameworks
CNTK
DVC
H2O
Horovod
LightGBM
MLFlow
PyTorch
Libraries
BigDL
Fastai
Gensim
Keras
NLTK
OpenCV
pandas
scikit-learn
Seaborn
TensorFlow
TPOT
Transformers
XGBoost
Need something extra?
Machine learning is just the beginning. Whether you need big data expertise, AI-powered solutions, or a strategic tech leader, we’ve got you covered.
Located in London?
Let’s talk in person.
Drop by our London office — our experts are always up for a talk. Or you can just give us a call.
+44 207 117 284230
Churchill Place
London E14 5RE, UK
hello@ventionteams.com
Let’s talk ML