Calling all Data Science Teams using AWS SageMaker

Stop Grappling With

SageMaker’s Complexity

And Say Goodbye to Bottlenecks
With the Codex MLOps Platform.

Simplify Your Path to Success
With Codex’s AWS Native MLOps Platform.

Discover

the secret to seamless model deployment and management.

Learn

how leading organisations are cutting down deployment timelines from months to weeks.

Find Out

how to significantly reduce operational overhead while boosting your team’s productivity to new levels.

Grappling with the complexity of SageMaker?

Is each day a struggle trying to transition models from concept to production?

Suffering from bottlenecks holding back promising models at the conceptual stage?

Are you and your team overwhelmed by the demands of managing current model lifecycles in production?

I get it, that’s why we have built this platform…

Adrian Campbell
AI & ML General Manager

Picture a MLOps Platform That

Removes the Complexity

of Data and Model Pipelines.

Imagine a MLOps platform that made deployments stress-free.

This is the dream state for MLOps, where every step is an effortless stride towards tangible business value.

How would you feel once your concepts were no longer stuck due to bottlenecks, but were in production?

What would you do with the free time created by a MLOps platform that proactively manages the model lifecycle in production?

What impact would this have on your daily workflow?

Codex’s AWS native MLOps Platform has been designed with you, the data scientist, in mind.

Designed to make your day-to-day workflow seamless.

These large companies trust us

We work with small companies too

Avoid No/Low Code Time Wasters

That Are Resulting In Failure

The 6 Pillars of

MLOps Success

Commonly Faced Problems

Codex’s MLOps Solution

1 Flexible

Traditional MLOps platforms often lack the agility needed to keep pace with the rapid evolution of data science tools and processes.

Codex’s platform is inherently modular, enabling seamless customisations to meet the ever-changing demands of data science.

2 Reproducible

Teams frequently struggle to replicate predictions or identify the root causes of errors, leading to excessive troubleshooting efforts.

By systematically storing all metadata and artefacts, Codex ensures predictions and errors can be reproduced with ease and efficiency.

3 Reusable

Custom solutions for ML-driven business cases drain valuable time and resources.

Codex’s platform is designed for repeated use across various projects, significantly reducing time and cost.

4 Scalable

Many solutions falter when faced with the need to scale up due to increased data volume or model complexity.

Codex’s platform adjusts seamlessly to your project’s requirements, regardless of size.

5 Auditable

The inability to thoroughly explain predictions or audit the platform complicates compliance with regulatory standards.

Codex maintains comprehensive logs of every action, input, and output, ensuring full auditability and regulatory compliance.

6 Consistent

Without continuous monitoring, models can degrade over time, leading to suboptimal outcomes and manual intervention for improvement.

By actively monitoring both data integrity and model performance, Codex’s platform ensures sustained optimal performance, extending the productive life of your models.

Don’t Be Another Statistic

Gartner estimates that 75% of organisations will look to operationalise ML/AI initiatives by the end of 20241.

Significantly, 53% of these initiatives

will never make it to production2.

Our MLOps solution will help you defy these odds, allowing you to invest in and deploy more initiatives than ever before on SageMaker.

%

14

Bonuses that come with the
Codex MLOps Platform

Initial Consultations

Complimentary Workshop
We will take the time to understand your needs, and provide a tailored solution for your business.
MLOps Maturity Assessment (Pre)
We will benchmark your current MLOps maturity, which will guide your strategy.
ML Lifecycle Time Loss
Understand your current time loss due to manual activities.

Setup

MLOps Platform Setup
We will set up your MLOps platform.
Initial Model Onboarding
We will assist you in onboarding your first ML model.

Training

Training
Ensure your team is utilising the platform effectively with interactive sessions.
Documentation
Get access to comprehensive guides for ongoing use and troubleshooting.
Videos
Get access to easy-to-understand training videos on MLOps and the platform.

Ongoing Support

Troubleshooting*
We will help diagnose and resolve model deployment issues.
Operational Guidance
We will provide advice on best practices for maintaining model performance and efficiency.
Technical Issue Resolution*
We will assist with platform related technical issues.
Security Update Support
We will provide guidance on applying necessary security updates.
* Unlimited access for 30 days post-deployment.

Post-Review

MLOps Maturity Assessment (Post)
We will re-evaluate your MLOps maturity post implementation.

Companies Are Spending HUNDREDS OF THOUSANDS on MLOps.

 

 

Today, You’re Getting The Complete Package

ALL INCLUSIVE.

STOP

You Need to Read This

Building models is hard
Productionising models is 10x the effort…

Without a production-grade ML pipeline, your models will never see the light of day!

Most Businesses Waste Time & Money On…

ML Governance

1 – 3 months wasted

You will need to hire: Data Scientists, ML Engineers, MLOps Engineers, Business Stakeholders.

Data Management

2 – 4 months wasted

You will need to hire: Data Engineers, Data Scientists, ML Engineers, Data Security Specialist

ML Pipelines

1 – 4 months wasted

You will need to hire: Data Scientists, ML Engineers

ML  Lifecycle Management

1 – 2 months wasted

You will need to hire: ML Engineers, MLOps Engineers, DevOps Engineers

ML Deployment

1 – 2 months wasted

You will need to hire: ML Engineers, MLOps Engineers, DevOps Engineers

Platform Foundation

2 – 6 months wasted

You will need to hire: Cloud Engineers, DevOps Engineers, Security Specialists, ML Enginers, MLOps Engineers, Cloud Engineers

8 months wasted

This is why SMBs are not operationalising AI & ML in SageMaker
So what do you need to do?

Codex MLOps Platform
to the Rescue

Cut months down to WEEKS
with the Codex MLOps Platform

Harness the Full Power
of SageMaker & AWS.

Control your MLOps platform through your favourite GIT provider.
Have all relevant metadata and artefacts stored and easily accessible.
Access industry standard workflow approvals.
Use any popular framework or upload a custom-built framework.
Leverage SOTA model retraining policies, promotion processes, deployment strategies and decommissioning processes.
Deploy your models using Batch, Real-time or serverless.
Proactively monitor for data drift, concept drift and model degradation.

Accessing all of this (and more), not for the hundreds of thousands of dollars you might expect, but for a fraction of that cost.

For the First 20 Customers

who qualify, we’re offering this
groundbreaking opportunity for just

$50,000 AUD.

$15,000 AUD.

This is $35,000 AUD in savings!

We Guarantee Your MLOps Environment Will Significantly Boost Productivity.

Don’t risk deploying models without production grade pipelines. 

Don’t invest into long platform builds, that may not work. 

Don’t invest the time and effort looking for specialist resources who may not be able to get the job done.

For just $15,000 you can:

1

Get your models into production faster than ever before

2

Boost your data science teams productivity

3

Work on new AI & ML initiatives

4

Save time on repetitive tasks

5

Show your business what’s possible

Don’t Wait.

To see if you qualify, book your consultation now and step into the future of machine learning operations.

Let’s unlock the potential of your ideas together.

This offer is limited and the benefits are vast.

Act now to secure your place at the forefront of the industry and don’t become another statistic.

Don’t Waste Any More Time.

This Offer Is Limited.

  1. Onag, G. (2022, February 18). Operationalising AI: Moving from model to production. FutureCIO. https://futurecio.tech/operationalising-ai-moving-from-model-to-production/
  2. Costello, K., Rimol, M.. (2020, October 19). Gartner Identifies the Top Strategic Technology Trends for 2021. https://www.gartner.com/en/newsroom/press-releases/2020-10-19-gartner-identifies-the-top-strategic-technology-trends-for-2021