Data Science: Dataiku rapidly deploys on AWS with CloudStack Computerworld

Using a no-code accelerator, Dataiku’s data and ML model management platform can be deployed in hours on the AWS public cloud. Business users and data scientists then access it from their browser and can also connect to AWS managed cloud services.

A few weeks ago, the French publisher Dataiku delivered its no-code CloudStack accelerator to facilitate the deployment of its data management and artificial intelligence platform in the AWS public cloud. Once deployed, the various users, business teams and data scientists will be able to access it from their web browser to work on the data. Some to create reports and dashboards, others to build machine learning models. This CloudStack accelerator is not charged extra.

The deployment of the platform on AWS is done in three steps that Dataiku describes in a post for cloud architects. The first is to create permissions on the existing AWS virtual private cloud topology, for the creation of Dataiku instances. Next comes deployment, using four out-of-the-box templates with what it takes to start and grow analytics and AI projects and put them into production in the cloud. The third step concerns the maintenance, evolution and updating of the Dataiku platform on AWS by the company’s IT team. With CloudStack, the deployment time is reduced from a few days or weeks to a few hours, according to its publisher.

(enlarge image) The four deployment models offer different architecture plans: single-node design environment for building pipelines and data models, or environments for data science teams that need elastic resources (Kubernetes clusters). Credit: Dataiku

Access to AWS services like Athena, Glue, Rekognition…

Once the data management and AI platform is deployed on AWS, business users will prepare their datasets using Dataiku’s no-code visual functions or using custom SQL code. Data scientists, for their part, will have access to the latest AutoML version of the platform for their machine learning models. They will be able to rely on the AWS EKS service (Elastic Kubernetes Service). Other features available to manage ML models include data pipeline and model monitoring, data rift alerting, A/B testing, and model reformation, Dataiku said. Governance functions are also offered to reduce the risks associated with the models. In addition, platform users will also be able to connect to various AWS services, including managed services such as Athena and Glue, Comprehend or Rekognition.

Men and women of data science

At the end of last year, to raise awareness of the great figures of data science, who have advanced the discipline and continue to do so, Dataiku launched the site History of Data Science. It is the result of several months of collaborative work. Alongside historical figures like Charles Babbage (1791-1871), who described the principle of the calculating machine, or in the 1950s, the Dartmouth Summer Research Project team who laid the foundations of artificial intelligence, we discover the engineers and researchers who are at the heart of innovation in data science today.

Along the portraits, scroll for example the profiles of Fei-Fei Li, professor of computer science at Stanford University, Yoshua Bengio, one of the pioneers of deep learning, winner of the AM Turing Prize 2018 alongside the French Yann LeCun and Geoffrey Hinton, or Judea Pearl who received the prize in 2011, “known for having established a probabilistic approach to AI and developed Bayesian networks, as well as the main algorithms used for inference in these models”, recounts in particular History of Data Science. On the site, you can also access the game Beat the linear Algorithm Regressionand we can get a copy of the album Innovators of Data Science: From Bayesian to Bayesian Neural Networks.

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Data Science: Dataiku rapidly deploys on AWS with CloudStack Computerworld


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