Machine learning requires technologies, dependencies, and artifacts that can be unfamiliar to traditional software and data engineering teams.
Operationalizing machine learning and building repeatable processes requires development of complex software applications and pipelines for both models and data. With specialized skills and training, experienced machine learning engineers are able to:
Without effective MLOps practices, many machine learning models never see production usage and die in PowerPoint presentations. Successful machine learning projects require knowledge of best practices and experience with common pitfalls to scale up from experimental POCs to critical business applications.
With support from experienced machine learning engineers, your data scientists will be able to effectively deploy robust models that create and track measurable business value.
ML engineering is a hybrid skill set that involves elements of software engineering, DevOps, and data science. ML engineers need the skills to develop and deploy robust software applications. On top of that, they need to understand the fundamental elements of data science and machine learning, such as data manipulation, statistics, model training, and model evaluation. For more details, check out our overview of ML engineering skills.
While there are many ways to cut corners and quickly deploy a model, creating a robust production application requires consideration of ML-specific requirements such as version control for both models and data, monitoring models for statistical degradation (like data drift), and ensuring traceability of predictions. For more details, check out our Ultimate Guide to Deploying ML Models.
Each ML engineering project is different, and every organization is at a different level of maturity for machine learning. On the lower end, a smaller effort (e.g. deploy a test version of a model for limited usage) might take a couple weeks and cost around $35k. A larger effort (e.g. migrating several models to the cloud while implementing process improvements) might require over four months of work, upwards of 1,000 hours of labor, and cost around $300k. These are just ballpark estimates, but you can contact us to get a better idea of the scope and requirements of your solution.
We bring technology, experience, and automation to ML engineering projects. With our industry-tested experience and frameworks, we help organizations implement solutions in weeks that could otherwise take months or years.
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