The ultimate Machine Learning Operations in the cloud
A team of top of the line engineers ready to help you integrate the power of AI into your products. Reach out and see how machine learning operations can get you ahead in the competitive world of startups and scale-ups.
End-to-end ML lifecycle
We believe in the MLOps’ best practices. Experiment tracking, model registries, feature stores, automatic retraining, model monitoring - we will let you enjoy all of the benefits of these principles, without having to deal with any of the burdens.
Your ML team will be able to focus on what matters the most.
Customized environments
Local environments can only get you so far when it comes to experimentation. To find an edge over your competition, you need to incorporate the enormous computational power offered by the cloud providers.
We can help you introduce and automate cloud-native experiments, allowing you to capitalize on opportunities as soon as they emerge.
Reliable model hosting in the cloud
ML models require customized hardware and are extremely sensitive to dependencies. In addition, the overwhelming abundance of possibilities offered by different vendors can lead to a continuous feeling of missing out on an opportunity.
As multi-year cloud experts, we can help you cut through the noise, and choose the option that your business needs.
ML scientific code refinement
Writing down a brilliant idea as code is one thing, but having a production dependency to it is another. A new set of challenges arises, such as versioning and reproducibility.
As DevOps practitioners and theoretical computer scientists, we can help your data science teams alleviate those problems, while ensuring their ideas do not get lost in the translation.
Development pipelines customization
The ML tooling landscape is changing rapidly. New tools are invented everyday, and all of them can bring genuine value to your data science team. However, these tools usually suffer from compatibility issues and poor documentation.
We can relieve your scientists of this burden, and let them focus on capitalizing, rather then building their tech-stack.
Machine Learning models MVPs
For most businesses, it is the first model that ends up bringing the most value. However, it is also the most challenging - it requires overcoming the deterrents of costs and complexity for the very first time.
We help bridge this gap by utilizing generally available, pay-as-you-go models to build the first, cost-effective functional PoC.
Zero-MLOps
Recent developments in AI have enabled many new breakthroughs for businesses. However, ML integration is a tricky process, with complexity and costs lurking around every corner.
Zero-MLOps is a set of processes and research strategies, which help businesses reach a definite conclusion whether a custom ML architecture is the right answer for their needs.
Sometimes, an experienced second pair of eyes can help you see your challenge in a completely different light.
There are many different options, which can contribute to the simplification of the MLOps stack:
- publicly available, open-sourced models;
- commercial, pay-as-you-go models;
- pre-built templates for custom models.
However, their market is extremely competitive and they also pose a new set of potential challenges:
- vendor lock-in;
- high maintenance burden;
- poor compatibility and extensibility;
- lackluster data security.
Our CEO, Mateusz Markiewicz, leading Fibertide since 2017, is an engineer too. Feel free to chat with him about your tech stack and engineering challenges!
mateusz.markiewicz@fibertide.com
Mateusz Markiewicz
Working with us
When we start our collaboration, we dedicate a team of our experts (size of which depends on the scale of your project and your budget) almost permanently to working with you.
Our teams are highly self-sufficient. Once we understand your business and current priorities, we organize our work efficiently on our own.
Even though there's a specific team working with you directly, in fact you benefit from experience of all of our experts.
Fibertide prefers working directly with your tech teams, supplementing them with specialized cloud knowledge and experience in highly reliable and scalable systems, and allowing them to focus on what they do best.
We value long-term collaboration
Since 2017, Fibertide has maintained the infrastructure of more than 20 established companies and startups, based in the USA, UK, Denmark, France, Switzerland, Belgium and other European countries and still work with most of our partners today.
Their lines of business include fields of insurance, bioinformatics, translations, finance, hospitality, retail and many others.
Next steps
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Discovery call
Our first step is to schedule a discovery call with you. During this call, we'll ask questions and listen to better understand your business, challenges, goals, and project scope. Our goal is to determine how our company can help you and what we can offer to meet your needs.
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Architect insight
After gathering information during the discovery call, we'd like to setup a more in-depth meeting with one of our architects. During this stage we'll get better understanding of your system and its technical challenges to plan our collaboration accordingly.
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Proposal stage
With all the information we've got, we'll prepare a proposal outlining our view regarding the best fitting team, budget and roadmap. We'll work with you to address any questions or concerns you may have, discussing any changes to the proposal and finalize the terms of the project to ensure we're both on the same page.
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Project kick-off
With the terms of the project agreed upon, it's time to kick-off the project. This involves setting up project timelines, assigning tasks to team members, and establishing communication channels between our team and yours. We'll keep you updated throughout the project and address any issues promptly to ensure the project stays on the right track.