Nearly 10 years later, Dataiku is helping to operationalize AI across a range of business use cases, from fraud detection and customer churn prevention to predictive maintenance and supply chain optimization. If the final destination is weaving AI capabilities so thoroughly into the fabric of day-to-day work that people forget it's there, enterprises are typically somewhere in the middle of the journey, Douetteau says. To get there, they should look inward. In this "AI From the Front Lines" interview, Douetteau and Romain Fouache, Dataiku's chief revenue officer, speak with Beena Ammanath, executive director of the Deloitte AI Institute, about their vision of AI in the enterprise, the importance of building systemization and trust for AI, and how execution will be more important than innovation in democratizing the technologies. "It's not a technology issue--we can build platforms able to continually process and enhance data and build new AI on top to optimize business processes," Douetteau says.

Dataiku is going downstream with a new product today called Dataiku Online. As the name suggests, Dataiku Online is a fully managed version of Dataiku. It lets you take advantage of the data science platform without going through a complicated setup process that involves a system administrator and your own infrastructure. If you're not familiar with Dataiku, the platform lets you turn raw data into advanced analytics, run some data visualization tasks, create data-backed dashboards and train machine learning models. In particular, Dataiku can be used by data scientists, but also business analysts and less technical people.

Dataiku today expanded its effort to make AI accessible to the average business user with an update that makes it possible to run what-if simulations of AI models to determine how changes to the data they are based on will impact them. The goal is to make it easier for business analysts to experiment with AI models based on machine learning algorithms they can create with the help of a data scientist team, Dataiku CEO Florian Douetteau said. As part of that effort, Dataiku 9 adds a Model Assertions tool that enables a subject matter expert to inject a known condition or sanity-checks into a model to prevent a certain outcome or conclusion from ever being reached. There is also now a Visual ML Diagnostics tool that will generate error messages if the platform determines a model will fail and a Model Fairness Report tool that provides access to a dashboard through which companies can assess the bias or fairness of an AI model. Finally, there is also now a Smart Pattern Builder and Fuzzy Joins capability that makes it easier to work with more complex or even incomplete datasets without having to write code or manually clean or prep data.

Artificial Intelligence (AI) and Covid-19 are the two remarkable buzzwords in recent times. AI has seen unprecedented developed by availing technology to fight the Covid-19 crisis. AI has contributed to the virus detection and tracking, and mainly to vaccine production. With 2020 coming to an end, Florian Douetteau, CEO and co-founder of Dataiku talked to Analytics Insight on his predictions of AI policies for 2021. Dataiku provides enterprise AI tools for companies like Pfizer, GE and Unilever.

CEO Florian Douetteau's leadership has helped make Dataiku a billion-dollar startup with customers ... [ ] in North America, Europe and Asia. At a time when tech giants such as Google and Amazon are weaving artificial intelligence throughout their company fabrics, data remains siloed at many companies outside of tech, left to the work of employees with the word "data" in their job titles. Data science startup Dataiku thinks it has the tools for companies in retail, finance and the like to utilize data like their tech peers. "I suspect in ten years we won't be using spreadsheets," says CEO Florian Douetteau. Instead, he hopes enterprises will turn to Dataiku's web-based software to take themselves on the path to artificial intelligence.

Dataiku has announced raising $28 million in a Series B round led by Battery Ventures, with participation from FirstMark, Serena Capital, and Alven. The Series B round brings the total amount raised by the New York City-headquartered data science software company to approximately $45 million. Dataiku said the funding will be allocated across three areas: Development, marketing, and recruitment. It plans to double its headcount to 200 employees across its offices in London, New York City, and Paris over the coming months, and add connectors to deep learning frameworks to its platform. Founded in France in 2014, Dataiku offers a "collaborative" platform, called Data Science Studio (DSS), with connectors to data sources, visual data preparation, and prepackaged machine-learning algorithms.

Machine learning is exploding, with smart algorithms being used everywhere from email to smartphone apps to marketing campaigns. Translation: if you're looking for an in-demand career, setting yourself up with the skills to work with smart machines/artificial intelligence is a good move. With input from Florian Douetteau, CEO of Dataiku, here are some things you can start doing today to position yourself for a future career in machine learning. This may sound obvious, says Douetteau, but it's important. "Having experience and understanding of what machine learning is, understanding the basic maths behind it, understanding the alternative technology, and having experience -- hands-on experience -- with the technology is key."