Keep up to date with the latest developments in trusted AI, MLOps and their applications in your sector.
News in the spotlight
Generative AI: Craft AI raises €5.5 million
Paris, July 18, 2023 - Craft AI receives 5.5 million euros in funding to accelerate the development of its generative AI specialized in dataset creation. This funding, in addition to the 9 million already raised, is made up of 1.5 million euros from Bpifrance's i-Démo call for projects, and 4 million euros contributed by the investment holding company Talis.
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.
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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.
Keeping people in the loop with eXplainable AI (XAI)
What role does explainability (XAI) play in Machine Learning and Data Science today? The challenge of the last ten years in data science has been to find the right "algorithmic recipe" to create ML models that are more and more powerful, more and more complex and therefore less and less understandable.
The industrialisation of AI & the concept of MLOps
MLOps appears to be a necessity to overcome the difficulties in scaling up AI within companies: reproducibility, versioning, continuous integration... This was the subject of one of the conferences on the industrialisation of artificial intelligence at Enjeu Day Industrie & Services 2022. You couldn't attend? Watch the replay.
A guide of the most promising XAI libraries
Using Machine Learning to solve a problem is good, understanding how it does is better. Indeed, many AI-based systems are characterized by their obscure nature. When seeking an explanation, looking to understand how a given result was produced, exposing the system's architecture or its parameters alone is rarely enough. Explaining that a CNN recognized a dog by detailing the Neural Network weights is, to say the least, obscure. Even for models deemed as glass-box such as decision trees, a proper explanation is never obvious.
The difficulties of industrialising AI
For a long time, the main objective of a Data Scientist has been to find the best algorithmic recipe to answer a given business problem. To facilitate this prototyping phase, many tools have emerged such as open-source libraries and Data Science platforms; the latter even offer a no-code experience.
Implement PostgreSQL Pool connection in Rust
At Craft AI, we build a new product so data scientists can code and push, quickly and easily, in production, their machine learning algorithms. Our purpose is to make life easier for data scientists, for example, we handle data storage in a nice way so data scientists do not have to bother with saving and loading data from a database.
Enter the predictive age with AI!
In this article we will try to define what Artificial Intelligence is, what role data plays in it, understand how Machine Learning is the future of AI and discover the use cases that are already revolutionizing the daily life of our schools, hospitals, communities and companies.