Notre blog

Pandas vs Polars: Who’s the fastest (library)?

MLOps, short for Operational Machine Learning, is a practice that combines DevOps principles with the development and deployment of Machine Learning models. By automating and streamlining the entire lifecycle, from data preparation to model scaling, MLOps enables organizations to effectively leverage data for real-time, large-scale ML applications. In this context, Python libraries like Pandas and Polars play a crucial role, offering powerful data processing capabilities. Comparing these libraries helps optimize MLOps workflows, ensuring efficient and successful ML deployments.

MLOps: Buy Vs. Build?

MLOps has emerged over the last few years to simplify AI projects by offering a set of practices and tools to manage all stages of development in an automated manner. Its main goal is to reduce development time, speed up and minimize deployment-related risks by improving model reliability and stability. When using an MLOps stack to develop a model, the transition from experimentation to production can be done with just a few clicks, whereas it could take up to 6 months previously.

Why MLOps is every data scientist’s dream? Part 2

We will try to provide some answers to this questions in two parts. This second article focuses on the first deployment and iterations to quickly improve it while the first one focuses on conception, data collection, exploration and application prototyping.

Why MLOps is every data scientist’s dream? Part 1

We will try to provide some answers to this questions in two parts. This first article focuses on conception, data collection, exploration and application prototyping while the second one focuses on the first deployment of the solution and iterations to quickly improve it.

Improve your ML workflows with synthetic data

As a data scientist, you know that high-performance machine learning models cannot exist without a large amount of high-quality data to train and test them. Most of the time, building an appropriate machine learning model is not a problem: there are plenty of architectures available, and since it is part of your job, you know exactly which one will best suit your use case. However, having a large amount of high-quality data can be much more challenging: you need a labeled and cleaned dataset matching exactly your use case. Unfortunately, such a dataset is usually not already available. Maybe you only have a few data matching your requirements, maybe you have data but they are not matching exactly what you want (they can have biases or unbalanced classes for example), or maybe a dataset exists but you cannot access it because it contains private information. Therefore, you need to collect new data, label them and clean them, which can be a time-consuming and costly process, or even not be possible at all.

How MLOps will streamline your AI projects?

When speaking of Artificial Intelligence, the efficiency and profitability of projects depend on the ability of companies to deploy reliable applications quickly and at low cost. To succeed, you need to organize and improve the processes for creating, implementing, and maintaining AI models with a diverse and sizable team.

Don’t just build models, deploy them too!

You don’t know what “model deployment” means? Even when you try to understand what it means, you end up searching for the meaning of too many baffling tech words like “CI/CD”, “REST HTTPS API”, “Kubernetes clusters”, “WSGI servers”… and you feel overwhelmed or discouraged by this pile of new concepts?

Un-risk Model Deployment with Differential Privacy

As a general rule, all data ought to be treated as confidential by default. Machine learning models, if not properly designed, can inadvertently expose elements of the training set, which can have significant privacy implications. Differential privacy, a mathematical framework, enables data scientists to measure the privacy leakage of an algorithm. However, it is important to note that differential privacy necessitates a tradeoff between a model's privacy and its utility. In the context of deep learning there are available algorithms which achieve differential privacy. Various libraries exist, making it possible to attain differential privacy with minimal modifications to a model.

A Beginner's Guide to MLOps

MLOps is the combination of Machine Learning and Operations. Like DevOps for the software world, the concatenation of "ML" with the agile execution methodology "Ops", augurs a coming of age of Machine Learning.