Machine Learning Engineering

Move your machine learning models from PowerPoint to production

What is Machine Learning Engineering?

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.

What can you do with help from phData’s experienced Machine Learning engineers?

What does a typical Machine Learning engagement look like?

What are some typical ML engineering project deliverables?

What is typically considered out of scope?

phData's Machine Learning Engineering is trusted by

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Questions about Machine Learning Engineering? We've got answers.

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.

How phData engineering accelerates ML adoption and operationalization

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. 

  • Quicker time to value

    phData’s philosophy is to “go deep” instead of to “go wide”.  Larger ML service providers go wide, using large teams composed of lower-cost, less-specialized personnel.  Rates are low, but total costs are high. By executing our projects with focused, expert teams, we can accomplish in weeks what might take months for others. Our hourly rates may be higher, but with smaller team sizes and shorter durations, projects have lower total costs and return value faster.

  • Foundation for future innovation

    Your first ML engineering project might focus on one task, but selecting the right tools will establish a foundation that accelerates your future model deployment cycles and grows with your team. By using an approach that includes tools for tracking models, templates for rapid deployment, and automated pipelines, we can ensure that you’re establishing a solid roadmap for the future instead of executing a series of isolated projects.

  • Robust applications powered by next-generation MLOps 

    Monitoring and observability are key to resiliency, but ML applications require an extra degree of care.  Real-world data and conditions can evolve over time, causing model performance to degrade without an apparent cause.  We incorporate statistical metrics and logging to make sure models continue to deliver in production just as they were designed in the lab.

  • Accountability and auditability

    Even effective ML models will occasionally produce unexpected output or require auditing for compliance. Our tools and frameworks will ensure that model predictions can always be traced back to the source (including the training data used and the model development process) and will enable effective governance.

  • Proven best practices

    Even experienced software engineers will encounter unwelcome surprises when building their first ML applications.  Our ML engineers have been around the block many times and know how to overcome hurdles and plan for the future.  We’ll make sure to leverage automation and DevOps to minimize technical debt and deploy maintainable ML applications the first time. 

Want to learn more about Machine Learning Engineering? We’ve got you covered.

The Ultimate Guide to Deploying ML Models

Our complete overview of all details teams should consider when deploying ML models.

A Beginner’s Guide to MLOps: Deploying ML to Production

A comprehensive introduction to the emerging field of Machine Learning Operations.

What is MLOps and Why Do I Need It?

A quick rundown of reasons why ML engineering could help your organization.

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