Machine Learning Operations (MLOps)

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

Enable MLOps with a proven methodology

MLOps, a close relative to DevOps, is a combination of philosophies and practices designed to enable data science and IT teams to rapidly develop, deploy, maintain, and scale-out Machine Learning models.

Regardless of whether an organization is just beginning to explore Machine Learning or has already begun to leverage it in their digital transformation and wants to incorporate MLOps, Neal Analytics has the team and proven methodology that can help. In fact, the flexible engagement model offered by Neal enables virtually immediate support, allowing it to rapidly overcome obstacles, blockers, and other challenges facing data science teams.

Offered as a managed service, the MLOps practice at Neal Analytics supports organizations by leveraging the same methodology used in-house to drive dozens of successful Machine Learning models and solutions to completion. 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 to execute 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.


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, Neal Analytics has 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 Neal Analytics 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.
Neal Analytics can help train, package, validate, deploy, and monitor ML Models

Model Lifecycle Management

To ensure Machine Learning models are consistent and all business requirements are met at scale, a logical, easy follow policy for Model Lifecycle Management is essential. The Neal Analytics MLOps methodology includes a process for streamlining model training, packaging, validation, deployment, and monitoring to help ensure ML projects run consistently from end-to-end.

By setting a clear, consistent methodology for Model Lifecycle Management, 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

According to Gartner, while 40% of top-performing companies see AI/ML as a gamechanger for their business, 56% of organizations also report staff skill level as a top challenge in AI/ML adoption.

Neal Analytics offers flexible engagement models that enable immediate support for virtually any Machine Learning challenge, whether it be providing skilled ML experts or driving an organization-wide MLOps implementation.

The team of MLOps and business consulting experts at Neal Analytics can immediately help overcome obstacles to MLOps adoption. A few common example engagements include:

  • Working with clients to determine MLOps maturity and identify areas MLOps would provide the greatest benefit
  • Building MLOps Proof-of-Concepts based on sample use cases
  • Create and implement a roadmap for an organization-wide MLOps rollout

Example Engagements with Neal Analytics

Maturity assessment

During the initial phase of new MLOps projects, Neal’s team of 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, Neal Analytics goes through a checklist assessment designed to determine specific areas of MLOps that need to be addressed within the organization. As part of this assessment, Neal 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 Neal team creates a presentation containing a summary of the findings, including identifying a sample use case to leverage proof of concepts.

Proof of Concept

Projects typically move on to a proof of concept engagement, usually leveraging the sample use case identified in the MLOps maturity 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 cycle
Growth model for MLOps roadmap planning

Roadmap planning

Neal Analytics can also support organizations as they begin to scale out MLOps practices across an organization’s AI/ML workflows. During a roadmap planning engagement, Neal Analytics 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

From there, Neal Analytics can support the organization in scaling out MLOps to the identified workflows and use cases, plus can provide support implementing it in future use cases as they arise.

Getting started

Neal Analytics has experience delivering unique solutions for unique business challenges across virtually all industries.

The MLOps practice at Neal Analytics 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.

Neal Analytics is also an award-winning Microsoft Gold Partner, reflecting our deep expertise in migrating data and apps to Azure and other cloud platforms.

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

Interested in MLOps, but not ready to make a full commitment? Neal Analytics offers flexible engagement models designed to support organizations; however, they need to create an exploratory proof of concepts.

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