Head of Data Engineering – Adatis
“We now have the flexibility to build and deploy models without worrying about the engineering behind it.”
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The use of Machine Learning and AI was once a niche area but has rapidly shown value in more traditional businesses. Advanced clustering produces new ways of viewing your customer base that leads to smarter product recommendation engines. Preventative maintenance can massively reduce costs and advances in image, video and audio recognition can automate tasks that previously required human intervention.
Businesses are investing in Data Science teams with deep domain knowledge and statistics expertise to devise increasingly sophisticated models. The skillsets required by these teams differs vastly from traditional development and many data science projects stall when it comes to the final hurdle – deploying the models into a reliable, productionised state. Many teams spend more time on data preparation and managing semi-production models rather than working on adding additional value. This stifles continuous improvement and innovation.
Adatis specialise in Data Science Enablement, working with your Data Scientists to overhaul and automate their development practices by putting in the right processes so they can focus on what they do best – delivering value. A key element of this is what we call Data Science DevOps; we can help transform your team to take advantage of these processes. We have the deep data engineering background to build out automated data processing so you don’t have to. We have developed frameworks for managing the Data Science Lifecycle – from initial experimentation, through automated deployment pipelines as models are tweaked and retrained, to robust managed production architectures that can scale with demand and gather telemetry as they go.
Here the Adatis team share their latest perspectives on all things advanced data analytics