At the heart of any IT solution is its infrastructure, which includes the hardware and software necessary to run it.
For example, machine learning model training requires a dedicated machine, and to speed up the process, multiple machines and management software are needed to distribute the workload. Without infrastructure, there’s no solution.
Infrastructure is a core component of any IT solution and is therefore part of every use case. Let's highlight some of the types of infrastructure we have experience with:
This is our bread and butter. We use this infrastructure to train, verify, deploy and serve new machine learning models.
This type of infrastructure is built around the concept of real-time events. This means that every change in a database is captured and published to other systems, which can then react to it.
Not every solution can be run entirely in the cloud. We have, however, set up infrastructures that can use the cloud's scalability for training ML models while serving those models on-premise.
We often use cloud services like GCP, AWS, and Azure to make our infrastructure work. They take care of the hardware side of things, so we can focus on the actual solution.
We start with a basic setup using Nimbus (our own tool), and use Terraform to manage everything. Terraform lets us describe the infrastructure in code, so we can easily keep track of what's going on and make sure it stays consistent.
Since infrastructure depends on the specific use case, we typically build our own infrastructure in the cloud.
However, because Paperbox is an expert in this area, we occasionally use their infrastructure for document processing.
For edge deployment, we collaborate with other parties to make sure everything runs smoothly and meets uptime requirements.
A practical guide to efficient neural networks: model compression techniques including pruning, quantization, knowledge distillation, and optimization tricks like gradient checkpointing and accumulation.
November 24, 2021
We explore how the connexion library facilitates API-first design with Python andtake a look at what the advantages are of API-first and compare with the opposite approach of “code-first”. Based on a benchmarking exercise and our values, we will dive deeper into why we are helping to maintain connexion.
January 20, 2022
Data is the “fuel” for ML. Yet, in many projects, it is still challenging to get the data right due to a lack of documentation, data quality issues, missing historical data, scalability issues with data platforms and overall lack of ownership.
February 16, 2021
Let us help you build a solid foundation for your AI solutions with our expertise in infrastructure, including hardware, software, and management tools.