MLOps Consulting

Your ML and RL models are at the heart of your business. Give them the love and support that they deserve.

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Benefits of Implementing MLOps Solutions

How is MLOps useful? Why would you use it? Learn why, below.

Empower Data Scientists With Our Proven MLOps Solutions

MLOps helps your organization operationalize machine learning. It’s a close relative of DevOps, the infamous software engineering buzz-word that aims to encourage developers to be responsible for their products, but it’s different in that the downstream customers of your ML models are usually other teams within your business, not the public. Nevertheless, operating machine learning models is a tricky business, and MLOps attempts to enforce or enable practices that promote stable, reliable AI solutions.

Model Management

Model Management

Model management tools and processes make it easier to govern your AI inventory. They provide the ability to:

  • Catalog what models you own
  • Track when, where, how, and why models were updated
  • Provide lineage, in the form of training artifacts and parameters
  • Promote reproducibility, by being a centralized, trusted artifact store
Experiment Tracking

Experiment Tracking

Experiment tracking is an essential tool to help data scientists be more productive. You’ll be able to:

  • Submit multiple jobs at the same time, increasing research throughput
  • Compare performance between runs
  • Time travel back to previous experiments
  • Unify how your data scientists instantiate pipelines
Scalable Pipelines

Scalable Pipelines

Scale your data ingestion or training pipelines to meet your demanding needs. With pipelines you can:

  • Train massive models, using pipelines that scale up to meet demand, and down to save costs
  • Make model training more repeatable and resilient
  • Take advantage of modern computing hardware like GPUs and high memory instances
  • Run your pipelines on-premise, in the cloud, or both!
Model Deployment

Model Deployment

Packaging models can be tricky. But we’ve worked with range of solutions that are able to simplify the deployment process. In this phase you can:

  • Automatically deploy models into production, with minimal human intervention
  • Bake models into containers for ultimate provenance and scalability
  • Serve models in a variety of protocols, like REST or GRPC
  • Automatically scale up deployments to meet demand, or scale down to save costs
Monitoring & Alerting

Monitoring & Alerting

Monitoring models and pipelines in production is essential to maintain availability of your services. With MLOps you can:

  • Watch for concept or data drift, which could invalidate your models
  • Provide analytics into the health of your AI system, with data healthchecks, deployment monitoring, user satisfaction and more
  • Implement continuous learning, to automatically retrain your models via your pipelines to deploy new suitable models into production, automagically
  • Alert before catastrophe, upon events like broken pipelines, endpoints, and data quality checks

Why Winder.AI?

Winder.ai are the leading MLOps consultancy. Learn more about us here.

Your MLOps Partner

By choosing Winder.ai as your trusted MLOps partner, rest assured that you talking to the most experienced MLOps agencies in the world. Together with our friends at the MLOps community, we helped define what MLOps is today. If you search around you can probably find our CEO Phil Winder talking about “DataDevOps”; this was our moniker for MLOps before MLOps was a thing!

Since then we’ve helped companies as large as Shell, with hundreds of data scientists, to start-ups like Living Optics, with only a few. Our flexibility allows us to collaborate closely with our clients. And our experience delivering ML and RL solutions demonstrates that we eat our own dog food.

About Us

Winder.AI is a data science consulting firm, and is based in the UK, although we operate globally. Our data science consultants deliver incredible value by evaluating and recommending strategic business decisions to further your organizational ambitions.

Collaborative MLOps team

The World's Best AI Companies

From startups to the world’s largest enterprises, companies trust Winder.AI.

MLOps Consulting Services

Winder.AI helps companies build production-quality reinforcement learning products and platforms.

MLOps Consulting for Living Optics - Courtesy of Living Optics

World Leading MLOps Company

Investing early to make the best MLOps decisions potentially saves millions in costs.

One of our clients, Neste, invested in designing and building an operational MLOps handbook for their organization, which paired with their bespoke GCP-focussed ML tool-suite. They are now leveraging this to great effect and have rapidly deployed over 20 ML projects since then.

We’re also helping companies like Living Optics, a start-up which is building an MLOps platform for the first time. They appreciate our guidance and lack of bias to deliver a design that is best suited to their unique needs. In this case their unique need is a massive amount of one-time data that is required during training, and very long training times. This required a focus on training observability and optimizing the placement of data.

Winder.AI provides expert evaluation and guidance to improve your ML development and de-risk implementation details. We advise organizations both large and small and operate across the world including Europe, UK, and USA.

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Our Approach to MLOps Consulting

Successful consulting arises from decades of experience. Take a look at our tips for a successful MLOps consulting project.

A flowchart depicting our MLOps consulting process.

MLOps Consulting Process

1. Business Context

Any problem demands context from the business. A solution for one industry may not be applicable to another, nor is every business the same. Establishing shared context helps get the project off to the right start.

2. Stakeholder Inclusion

More than just identification, we find projects are optimal when key stakeholders are including in the project. With “skin in the game”, stakeholders are far more likely to collaborate to produce a better overall solution.

3. Problem Definition

A key phase where business problems are defined and prioritized. It is worth spending time to get this right, as subsequent effort could be ineffective and wasted.

4. Domain Knowledge Transfer

Businesses are often experts in their own domain. This domain expertise is valuable to help direct future solutions.

5. Process Gap Analysis

In this phase our expert consultants speak to all the stakeholders and users to find the largest efficiency losses and pain points in the ML productionization process.

6. Regulatory/Audit Analysis

Not always applicable, but some industries use MLOps as a way of solving regulatory requirements. In this phase we collaborate with you to establish what those needs are.

7. Research and Validation

Finally we get to the phase where we begin to design a solution, based upon the requirements for the MLOps system gathered earlier in the process. In some cases we also provide POCs to validate these solutions.

8. Strategic Solution

In the final phase we present the solution back to you and your stakeholders and then move on to the next phase in the project.

Optimizing for Value Generation

Businesses have three core operational functions. Processes define how businesses run. Decisions decide when businesses are run. Strategies define why businesses are run.

Software has successfully automated many business processes. Data science automates decisions and strategies via machine learning and reinforcement learning, respectively.

By leveraging our data science consulting services we can help you automate the top two most valuable tiers in the pyramid, to make your organization more efficient and profitable.

The value of reinforcement learning, courtesy of our Reinforcement Learning book.
The OODA loop for continuous innovation.
Winder.AI’s data science consulting strives for continuous innovation. Courtesy of our Reinforcement Learning book.

Continuous Innovation

The infamous OODA loop, originally developed by the US military, is of particular use during our work because it helps promote innovation.

At every phase we look for opportunities to add value and make your products and services better. Our clients find that our work greatly exceeds their expectations due to the extra value presented by our solutions.

Start Your Consulting Project Now

The team at Winder.AI are ready to collaborate with you on your consulting project. We will design and execute a solution specific to your needs, so you can focus on your own goals. Fill out the form below to get started, or contact us in another way.

Selected Case Studies

Some of our most recent work. You can find more case studies in our portfolio.

Using Reinforcement Learning to Attack Web Application Firewalls

Introduction Ideally, the best way to improve the security of any system is to detect all vulnerabilities and patch them. Unfortunately this is rarely possible due to the extreme complexity of modern systems. One primary threat are payloads arriving from the public internet, with the attacker using them to discover and exploit vulnerabilities. For this reason, web application firewalls (WAF) are introduced to detect suspicious behaviour. These are often rules based and when they detect nefarious activities they significantly reduce the overall damage.

Helping Modzy Build an ML Platform

Winder.AI collaborated with the Modzy development team and MLOps Consulting to deliver a variety of solutions that make up the Modzy product, a ModelOps and MLOps platform. A summary of this work includes: Developing the Open Model Interface Open-sourcing chassis, the missing link that allows data scientists to build robust ML containers Model monitoring and observability product features MLOps and model management product features The Problem: How to Build An ML Platform Modzy’s goal is to help large organizations orchestrate and manage their machine learning (ML) models.

How To Build a Robust ML Workflow With Pachyderm and Seldon

This article outlines the technical design behind the Pachyderm-Seldon Deploy integration available on GitHub and is intended to highlight the salient features of the demo. For an in depth overview watch the accompanying video on YouTube. Introduction Pachyderm and Seldon run on top of Kubernetes, a scalable orchestration system; here I explain their installation process, then I use an example use case to illustrate how to operate a release, rollback, fix, re-release cycle in a live ML deployment.

Start Your MLOps Consulting Project Now

The team at Winder.AI are ready to collaborate with you on your mlops consulting project. We will design and execute a solution specific to your needs, so you can focus on your own goals. Fill out the form below to get started, or contact us in another way.