Machine Learning Operations (MLOps)

How MLOps principles, embedded within ProActive AI Orchestration, help your AI success

The purpose is to deploy reliable code, data, and models into production with a fast time to market

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The era of manual AI will progressively mature with the introduction of MLOps.

Deploying AI at scale: Today’s key obstacles

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Lack of necessary data

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Non-existent integrated development environments

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Inconsistent model execution

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Top 4 improvements Data Scientists expect from MLOps:

57%

Simplify ML tools integration

56%

Stronger security & Governance

67%

Reduce time to deployment

69%

Lower infrastructure cost

Source: Survey - State of Machine Learning and Data Science, October 2021

MLOps is the key to generating business value from AI projects

Why is MLOps important?

MLOps is a game changer when you need to automate and scale the deployment of AI models

Remove friction

The goal of MLOps is to remove the frictions from the experimental stage right through when the model is in production. Making sure to successfully put AI model in production in the shortest possible time with as little risk as possible.

Better collaboration

Traditionally, machine learning has been approached from a perspective of individual experiments, predominantly carried out in isolation by data scientists.

Industrialization

In reality, only models running in production can bring value. Models have zero ROI until they can be used. Therefore, models deployment need to be integrated within the AI pipeline and move from informal to an industrialize process.

ProActive AI Orchestration top benefits

The main benefits delivered by our solution

Better collaboration

MLOps ensures teams share everything that goes into producing AI models visibility – from data extraction to model deployment and monitoring. Turning tacit knowledge into parameters and process makes machine learning collaborative.

Illustration ActiveEon MLOps Collaboration between teams
mlops scalibility elements

Components assessed with customers

Achieve scalability

AI scalability refers to scaling AI applications that can handle any amount of data and perform many computations in a cost-effective and time-saving way to instantly serve millions of users residing at global locations.

Ensure reproducibility

Data scientists should be able to audit and reproduce every production model. Unlike DevOps, version control is not enough, AI requires more than that. Versioning data, model parameters and metadata are essentials. Storing all model training related artifacts ensures that models can always be reproduced.

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Components assessed with customers

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ROI & value for money

Let’s put aside gut feeling and start monitoring performances. Testing and monitoring are part of engineering practices, and AI should be no different. In the AI context, performance is not only focused on technical metrics (such as latency) but, more importantly, predictive performance. MLOps best practices encourage making expected behavior visible and to set standards that models should adhere to.

Do you really need MLOps?

Tell-tale signs you need MLOps

It is rather hard to maintain and integrate so many tools and languages.

Data Scientists require more power, but their IDE can only use local machines or the expensive cloud service.

Despite budget increase, you experience difficulties to deploy new models in production.

You are starting to deploy multiple models, but you are struggling to keep up with monitoring them all.

You would like to use your hybrid environment, but you are locked-in with one cloud provider.

You are running process that require multiple type of workloads - real-time, event based, cron jobs.

Your DevOps team, unlike traditional IT application, has trouble monitoring AI model performances.

How to implement MLOps?

Best practices based on our experience from the deployment of ProActive AI Orchestration

Integrate

Use the AI Orchestration framework to integrate diverse data sources, development tools and other enterprise applications.

unify

Interconnect

The AI Orchestration framework will be used to interconnect your existing hybrid compute infrastructure and enable auto-scaling and parallel computing.

Integration

No lock-in

The AI Orchestration framework should be an open framework allowing you to select the tool you want and not forcing you towards a specific technology – the choice must remain yours.

Results

Fix where it hurts

There is no wrong answer, automate the process where it is failing you the most. Gain benefits quickly, by formalizing and automating the processes.

Results

The ActiveEon team is happy to advise you on the best way forward.

Learn more

Alternatively, we can help you to organize a workshop with partners of our ecosystem who advise the largest companies in the world.

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How ProActive AI Orchestration helps to industrialize the AI pipeline

Integrating MLOps principles and making it easy to implement

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Quick

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Secure

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Cost-effective

We help you to manage all the stages of AI pipeline within your existing operational processes, so you can put models into production quickly, securely and cost-effectively.

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Favorite tools

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Favorite languages

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Any infrastructure

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Security & governance

ProActive AI orchestration's open architecture gives you the freedom to interconnect your favorite tools, favorites languages, your hybrid compute infrastructure CPU, GPU, TPU (on-premise, in the cloud) in containers, Kubernetes or VM.
While you’re welcome to use AI tools provided by public cloud providers, we avoid vendor-lock in by integrating models made in the cloud into the ProActive framework, and share them across the company.

Go Further

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