Machine Learning Services
Data is changing, are you?
We're used to plotting data and 2D trends to deliver business insight, but the shape of our data is changing.
As our data grows we use more advanced methods for analysis, but more importantly we can start to use Machine Learning to augment that advanced analysis with predictive outcomes.
If you’re struggling to know where to start with Machine Learning, DSP-Explorer can help plan and execute a strategy which can lead to real business outcomes.
How canyour Business?
We build bespoke AI solutions to help your company automate, optimise and extract insights to improve your products and services
Here are some examples.
- Clinical Analytics
- Early Diagnosis
- Automated Administration
- Demand Forecasting
- Price Optimisation
- Fault & Anomaly Detection
- Fleet Management Maintenance
- Revenue Analytics
- Volume Predictions
- Sales Forecasting
- Customer Sentiment Analysis
- Dynamic Pricing
- Fault Detection
- Estimating Remaining Useful Life
- Improve Operational Efficiency
Finance & Insurance
- Fraud Detection
- Cross Sell Predictions
- Underwriting & Credit Scores
Machine Learning is revolutionising the world of business intelligence. As your data gets more complicated and the number of dimensions you need to model become more than just sales and geography Machine Learning is available to help solve these challenges.
Supercharge your business intelligence to go from forecasting to predictive insights.
Prior to billing you can provide us with a problem brief and sample data, our team of in-house data scientists can then go to work investigating whether machine learning can help you.
This process involves data exploration, cleaning, and visualisation. The findings of this investigation will then be provided to you in a follow up meeting, any problems can be addressed, and a project statement will be made - it's your choice if you would like to continue from this point onwards.
This is where the magic happens. Model building commences, our team will proceed to create, train, and test machine learning models tailored to your data and your problem.
This is an iterative process during which we use our expertise to evaluate our selection of models, find the one with the best performance, and demonstrate to you how the model performs.
When you're happy with the value that machine learning can provide, we can either hand off deployment to you, or we can deploy the model for you. This involves containerising the model and deploying it to a location where its availability suits you. Whether or not we handle the deployment we can always provide maintenance, in order to fix problems or improve upon the model in light of new data.
Stages of Machine Learning Adoption
So you have data. Lots of data, probably - but how mature is that data? Where is it stored, how useful is it, and how much of it is even accessible?
Before any Machine Learning magic can begin, we need to make sure that your data is mature and consolidated.
DSP-Explorer will provide a Free Assessment to ascertain the data maturity within your company and pinpoint your next obvious steps towards unlocking your data potential.
The next logical step in this series is to develop your Use Case. What do you want to get out of your data in order for it to be useful to your business goals?
We'll help determine the best practice Machine Learning methods, but first we need a clear idea of the best way we can put your data to use.
Pilot and POV
Now we can get onto the exciting stuff. The Pilot and PoV are designed to give you a sneak peak into how the expanding capabilities of Machine Learning can improve your business intelligence.
It will also give us a chance to work out the best way of implementing our methods on a wider scale, so you know your data is in safe hands.
Machine Learning Deployment
The final step moves into the deployment of fully managed Machine Learning mechanisms that make your data really work for you.
Utilising Machine Learning can increase efficiency, save on resources, and speed up innovation within your company.
Bringing Machine Learning to you
Machine learning is an industry agnostic tool, what we're offering to do is bridge the gap between your use case and our skillset. Below you can read about the various technologies and techniques we use to apply machine learning to various problems.
Oracle Data Science
Azure Machine Learning
Providing Access to the Cutting Edge
The myriad of platforms, technologies, and algorithms can make machine learning look inaccessible to small or medium sized enterprises.
You don't need a team of 100 data scientists or a budget of millions to get started, we have the cloud platforms and the data scientists available, all you need to bring is the data.
We utilise the following ML methods:
- Linear Regression
- Polynomial Regression
- Ridge/Lasso Regression
- K Nearest Neighbours
- Bayesian Statistics
- Support Vector Machines
- Decision Trees
- Logistic Regression
- Random Forests
- Extreme Gradient Boost
- Principal Component Analysis
- Linear Descriminant Analysis
- Singular Value Decomposition
- Latent Semantic Analysis
- K Means Clustering
- Fuzz C Means Clustering
- Artificial Neural Networks
- Convolutional Neural Networks
- Generative Adversarial Networks
- Recurrent Neural Networks
"DSP-Explorer has helped the National Institute of Health Research to apply Machine Learning to enable the predictive programming of Cochlear implants; DSP-Explorer understands Machine Learning and data so regardless of how industry-specific your ML problem is DSP-Explorer can help unlock insights into that data and help build new ways of interpreting existing data using Machine Learning models."
Hearing Team Lead | NIHR Nottingham Biomedical Research Centre
"Working closely in partnership with DSP-Explorer and leveraging the power of Oracle Cloud Infrastructure Data Science has enabled our team to gain new insights into how we can apply cutting edge machine learning approaches to help solve a challenging clinical need.”
“This partnership is helping us to develop mathematical models of how implants are programmed in the weeks and months following surgery, and once refined will have direct and immediate applications in the clinical environment for patient benefit.”
Hearing Team Lead | NIHR Nottingham Biomedical Research Centre