Machine Learning Operations (MLOps)

Leverage a proven system for implementing end-to-end lifecycle management for Machine Learning solutions

Support MLOps with a proven methodology

MLOps is a set of practices that brings together data science and IT teams to quickly develop, deploy, and maintain machine learning models at scale. It is closely related to DevOps in its approach to collaboration and automation.

Whether you are just starting to explore machine learning or looking to leverage it in your digital transformation efforts, we’re here to help. We have the right skills and experience to assist organizations at any stage of their machine learning journey, including those looking to start implementing MLOps. Our team can help you overcome the challenges you will face during your data science journey. Additionally, our flexible engagement model allows us to provide fast support to ensure that your team can quickly overcome any blockers.

Through consulting and managed service offerings, our MLOps practice can help organizations train and scale machine learning models and solutions to production using the same proven methodology that we use in-house. Our team has a track record of successfully driving numerous machine-learning projects to completion, and we can do the same for your organization. This proven model leverages our proven process to:

  • Train models via model testing, model management, and model maintenance. Models are trained to enable reproducible models, supporting quicker model training cycles for similar Machine Learning implementations.
  • Package models to prepare them for execution in their desired runtime environment. This includes preparation for application integrations, accounting for scalability requirements, and ensuring the model is flexible enough to run on various hardware platforms.
  • Validate models by measuring candidate models against predefined KPIs, deployment testing, and testing application integrations.
  • Deploy models by identifying the deployment target, planning the deployment, and then deploying the models to their production environment. During deployment, services are implemented to support scalability, data pipelines are automated, and a model selection strategy is implemented.
  • Monitor models to track behavior, continuously validate KPIs, measure accuracy and response times, watch for drift in model performance, and more. Logging this information enables model training (or retraining) based on experiment results.
Enable MLOps with a proven methodology


Proven Methodology

Model lifecycle management

Flexible Engagement Models


Proven Methodology

While it’s typical for only 50% of machine learning models to make it to production, we have successfully delivered dozens of successful in-house ML projects for their clients. The secret? Leveraging a proven methodology that eliminates guesswork, supports consistency and rapid time to value, and enables continuous packaging, validation, and deployment of models to production.

Working with us to implement MLOps means gaining access to this same methodology. Designed to enable the benefits listed above, this methodology also supports:

  • Record keeping and versioning practices that support consistent, reproducible ML models.
  • Accelerated time-to-value via more efficient model training.
  • Continuously deploying and integrating models, plus gaining insights into model evolution.
Model Lifecycle Management

Model Lifecycle Management

A logical, easy-follow policy for Model Lifecycle Management (MLM) is essential to ensure machine learning models are consistent and all business requirements are met at scale. Our MLOps methodology includes a process for streamlining model training, packaging, validation, deployment, and monitoring to help ensure ML projects run smoothly from start to finish.

By setting a clear, consistent methodology for MLM, organizations can:

  • Proactively address common business concerns (such as regulatory compliance).
  • Enable reproducible models by tracking data, models, code, and model versioning.
  • Package and deliver models in repeatable configurations to support reusability.

Flexible Engagement Opportunities

Gartner reports that 40% of top-performing companies view AI/ML as a game-changer for their business but that 56% of organizations also cite staff skill level as a major challenge in AI/ML adoption.

We offer flexible engagement models that allow quick support for any machine learning challenge, whether through deploying skilled ML experts or implementing an organization wide MLOps strategy.

Our team of MLOps and business consulting experts can immediately help overcome obstacles to MLOps adoption. A few common areas where we have helped include:

  • Conducting a maturity assessment with clients and identifying areas MLOps would provide the greatest benefit.
  • Building MLOps Proof-of-Concepts.
  • Roadmapping for organization wide MLOps rollouts.

Example Engagements with Neal Analytics


Maturity assessment

During the initial phase of new MLOps projects, our ML engineers and business consulting experts will work with the client’s internal IT and analytics teams to coordinate a working session.

Throughout the session, the team will go through a checklist assessment designed to determine specific areas of MLOps that need to be addressed within the organization. As part of this assessment, we will work to:

  • Gain an understanding of the organization’s current MLOps capabilities and maturity.
  • Catalog existing processes and tooling for AI/ML workloads.
  • Identify areas where MLOps is most needed.

After completing the assessment checklist, the team creates a presentation containing a summary of the findings, including identifying a sample use case to leverage proof of concepts.

Proof of Concept

After the MLOps maturity assessment, projects usually move to a proof-of-concept stage, using the sample case identified in the assessment.

This is achieved by collaboration with the client organization’s data science resources to implement MLOps for the use case. This involves the creation of MLOps pipelines and:

  • Incorporation of MLOps governance.
  • Identification and mitigation of gaps in best practices.
  • Implementation of best practices for core ML lifecycle delivery (including CI/CD).
  • Evaluation and validation of MLOps practices by the organization’s corporate culture and policies.

The successful conclusion of a proof-of-concept engagement will result in a viable solution to demonstrate value and drive approvals for expanding MLOps into other use cases.

MLOps lifecycle
MLops Roadmap

Roadmap planning

We can also support organizations as they begin to scale out MLOps practices across an organization’s AI/ML workflows. During a roadmap planning engagement, we will work with the organization to:

  • Identify and prioritize mission-critical AI/ML workflows that can benefit from retroactive MLOps implementation.
  • Create a roadmap for applying MLOps to the backlog of workflows.
  • Collaborate on the organization’s MLOps governance and policy enforcement strategy.

After identifying the workflows and use cases where MLOps can be implemented, we can assist the organization in scaling out the use of MLOps and provide support for implementing it in future use cases as they arise.

Getting started

We have experience delivering unique solutions for unique business challenges across virtually all industries. 

Our MLOps practice is ready to help organizations leverage the same proven methodology used in house to quickly assist organizations in reproducible model training and CI/CD for Machine Learning. 

Ready to begin leveraging MLOps in your organization? Contact us below to get started. 

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