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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
Deploying AI at scale: Today’s key obstacles
Lack of necessary data
Non-existent integrated development environments
Inconsistent model execution
Top 4 improvements Data Scientists expect from MLOps:
Simplify ML tools integration
Stronger security & Governance
Reduce time to deployment
Lower infrastructure cost
Source: Survey - State of Machine Learning and Data Science, October 2021
Why is MLOps important?
MLOps is a game changer when you need to automate and scale the deployment of AI models
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.
Traditionally, machine learning has been approached from a perspective of individual experiments, predominantly carried out in isolation by data scientists.
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.
The main benefits delivered by our solution
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.
Components assessed with customers
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.
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.
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.
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
Use the AI Orchestration framework to integrate diverse data sources, development tools and other enterprise applications.
The AI Orchestration framework will be used to interconnect your existing hybrid compute infrastructure and enable auto-scaling and parallel computing.
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.
There is no wrong answer, automate the process where it is failing you the most. Gain benefits quickly, by formalizing and automating the processes.
The ActiveEon team is happy to advise you on the best way forward.
Alternatively, we can help you to organize a workshop with partners of our ecosystem who advise the largest companies in the world.
Integrating MLOps principles and making it easy to implement
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.
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.
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