Services
Machine Learning as a Service
INTRODUCTION
Machine Learning is becoming imperative for businesses looking to transform data into their most strategic asset.
The increasing prevalence and impact of Machine Learning (ML) means that business leaders everywhere are realising its transformative power. Reducing costs, managing risks, streamlining operations, accelerating growth and fuelling innovation – the potential value to your business is limitless.
Our Machine Learning as a Service framework is the culmination of more than two decades of combined experience across our team of data scientists. Having applied their skillset across multiple industries and use cases, our experts have developed highly effective, proven strategies, across different sectors.
How it works
Our highly skilled team work with your business and data experts to comprehensively define the problem, before outlining the recommended roadmap to achieve your goals.
Build the Models. We provide specialist resources to build the models, create the documentation and then deploy. Development and deployment take place on Calligo’s ALPHI infrastructure, ensuring quality and excellence.
Models are deployed and managed on Calligo’s infrastructure, to ensure reliability of results. Our ongoing monitoring then ensures continued quality and return on investment.
BENEFITS
Your ambitions depend on better knowledge, deeper understanding and more actionable decisions.
How Calligo has helped businesses
USE CASE EXAMPLE
Recommendations
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
USE CASE EXAMPLE
Recommendations
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
USE CASE EXAMPLE
Recommendations
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
USE CASE EXAMPLE
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
USE CASE EXAMPLE
Recommendation
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
USE CASE EXAMPLE
Recommendations
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
USE CASE EXAMPLE
Recommendations
Companies can leverage customer purchase history, consumer behaviour data, and personal data to target marketing efforts to existing customers.
Calligo’s machine learning solutions customize a set of product offerings for each customer that not only predicts a customer’s next product purchase, but also maximizes their total value and satisfaction.
Data for potential new customers can be compared to your existing customer base to understand the most efficient way to expand a customer base.
REVIEWS
See what our clients say about us
The difference working with Calligo was the team’s mindset. They looked at the problem entirely as a commercial question, not as a data science challenge applied in a business context. And it worked.”
Our Solve My Bycatch Problem tool is a resource for fisheries stakeholders to understand how to reduce risk to vulnerable ocean wildlife. But it’s only really valuable if people use it. The eye-catching design and easy user interface that Calligo created will help ensure that the right people get the information they need.
FAQs
People Also Asked Us…
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.