Machine learning (and data science) can help a business leapfrog to the front of the pack in their industry. With the advent of unified data platforms and almost limitless compute and storage, companies of all sizes are realising the value of incorporating machine learning and data science into their business practices.
These incredible technologies don’t come without their challenges, of course. Data scientists can often spend much of their time wrangling data, struggling with pipelines and generally not doing enough of what they are highly skilled at - data science. We believe data scientists should be enabled to focus their time on the models and developing features to get the best results, not hammering away at the orchestration of their pipelines.
How can we help?
Cloud Fundis have many years experience in building complex pipelines used for datalakes, as well as pipelines to analyse, train, test and deploy machine learning models into your environment. We understand that it takes a thorough understanding of the software development life cycle (SDLC) and the cloud platform tools and services.
Machine learning pipeline and MLOps
Build a machine learning pipeline to completely automate the training, testing and deployment cycle of the machine learning pipeline - whether to provide a real-time inference of the data (perhaps an API that website customers are using to get a prediction) or a batch-processing pipeline. We can engineer a pipeline that is not only fault-tolerant and resilient, but also dynamically scales with your environment, while balancing performance and cost per cycle.
We have seasoned engineers on our team who can help your data scientists work with the highest efficiency, ensuring they can deliver on the science part of your data with ease.
Feature store development
Many organisations suffer from a lack of features required for machine learning models. While data is collected and refined in datalakes, data warehouses or databases, these are often discreet data points that bear no correlation to one another in their siloed areas. Certainly data warehouses and datalakes have improved that, but a feature (like whether someone is employed or not and who their employer is) may well be contained in many different silos of the lakes or warehouses. Having that data readily available for use in a machine learning model is part of a feature store.
As a forward-thinking business, having machine learning pipelines without features to feed the models, won’t achieve the desired effect. We help businesses build a central feature store that ensures minimal duplication of compute and resources in ongoing feature creation and new feature development. A central feature store ensures adequate governance and oversight of new features, as well as maintaining the quality of features for the whole organisation.
Providing data scientists on demand
What if you don’t have data scientists? No problem, we’ve got you covered! We have scientists who work with data every day of their lives. One could say they live data; it’s processing and analysis. Our internationally qualified scientists have a thorough understanding of using data to gain insights, understand statistics and correlations and are well versed in helping you use your data in the most valuable ways, to make informed decisions for your business.
If you have a Data Science or Machine Learning project you want to talk to us about, then please contact us.