Your business may have an in-house analytics team, which is used to building dashboards in Tableau or PowerBI to provide valuable business insights. Machine learning can take this to another level by generating predictions, or finding patterns and trends in data. For example, a machine learning model can predict how much a customer is likely to spend, how much a clinical trial may cost, or who is likely to make an insurance claim.
In particular, as the hype around Large Language Models (LLMs) shows, natural language processing (NLP) can delivery a huge amount of value from your unstructured text data. We have over 15 years’ experience in NLP and are well-placed to help you extract value from your structured or unstructured data, whatever the format (text or numeric).
The Fast Data Science machine learning consulting flow.
Below you can see a few of the applications of machine learning in industry that we’ve seen since we’ve been in business as a ML consulting company.
|Example of data discovery
|Example of predictive modelling
|Identify common drug interactions mentioned in literature
|Predict the cost or risk of a clinical trial
|Segment customer base by demographic and behaviour
|Predict the likelihood of a given customer making a claim
|Identify trends in prosecution rates by county and offence
|Predict the likelihood of a case going to appeal
|Identify trends in international students coming to the UK by country of origin and field of study
|Predict student attrition based on exam performance
|Segment junior doctors by speciality and study outcome
|Identify junior doctors in NHS who are likely to drop out before qualifying as a consultant or GP
|Identify which facilities have higher than usual breakdown rates
|Identify wind turbines which are likely to have a catastrophic failure using computer vision
|Find common causes of factory equipment breakdowns from error logs in plain text
|Predict likelihood of machine breaking down and needing maintenance
|Use natural language processing to harmonise multilingual survey data on anxiety and depression
|Predictive model which takes the SMILES representation of a molecule and predicts its solubility in oil and water
Fast Data Science - London
We offer end to end machine learning consulting services and development.
We will start with an on-site meeting to discuss your requirements, and to brainstorm about the projects and objectives with the highest ROI for your company.
This is followed by a data exploration phase where we establish what is achievable for your use case using AI.
We begin an intensive phase of model development, testing thousands of competing models against a validation dataset, and finally we move on to model deployment. Most machine learning consulting firms will assist you with parts of the cycle - we do it all.
A typical case is where a company has a large amount of unstructured information about several million customers, users or products.
For example, you may have a large database of unstructured and messy text data such as user profile texts, or product descriptions, or past purchasing histories, and you may want to use this data to
In these cases we can provide ML consulting services or work together with your company to fully deploy a machine learning solution that extracts value from these large datasets.
The value to your business of fully using your data can be huge.
If you would like consulting for your machine learning strategy please get in touch.
You may be wondering how long a machine learning consulting engagement is likely to ask, or, more importantly, about the cost.
Our ML consulting company offers a number of free and open resources on our website for planning ML engagements.
Fast Data Science stands out among other machine learning consulting firms by the sheer amount of reference and educational materials we provide for free.
This is an in-browser tool which you can use for planning natural language processing projects. It generates a Gantt chart based on the inputs which you give it.
A handy document for planning a machine learning consulting project by categorising tasks into high, medium or low impact.
A tool for assessing the risk of a NLP/machine learning consulting project failing to complete.
This tool was developed for NLP projects, which tend to have a greater degree of complexity and risk than machine learning projects that operate on structured data or relational databases, but is still informative for any kind of machine learning consulting service engagement.