With rapid advancements in machine learning (ML) techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. A study conducted by PwC Global concludes that by the end of this decade, the total positive impact of artificial intelligence (AI) on the global economy could be above $15 trillion, driven mostly by enhancements in consumer products. To make that happen, however, businesses must make strategic investments in the type of technology that moves AI projects into production (productionizing) and helps customers deploy them. Unfortunately, PwC's survey reveals the percentage of executives planning to deploy AI has gone down from 20 percent a year ago to only 4 percent at the beginning of 2020. The primary reason for this decrease is the gap between the growing volume of data and data-driven modeling capabilities, and the necessary skills and toolsets.

We will provide a dataset, jump-start workflows, and final solutions for the proposed tasks, and of course data visualization and ML experts. Before the event, we will share the link to download the workshop material (jump-start workflows and instructions). This is a free event, open to everybody who is interested. Food and drinks will be provided.

If you work with data in any capacity, go ahead and do yourself a favor: download KNIME Analytics Platform right here. KNIME Analytics Platform is the strongest and most comprehensive free platform for drag-and-drop analytics, machine learning, statistics, and ETL that I've found to date. The fact that there's neither a paywall nor locked features means the barrier to entry is nonexistent. Connectors to data sources (both on-premise and on the cloud) are available for all major providers, making it easy to move data between environments. It's also worth mentioning that the community is particularly robust.

As with previous Summits, there'll be leading data scientists there, highlighting how they use KNIME Software for solving data problems across industries such as telecommunications, retail, life sciences, manufacturing, finance, and more. We've also got our KNIME courses on offer, giving you the chance to extend your KNIME knowledge, plus an exciting social program, providing plenty of networking opportunities. On March 18 and 19 we are offering several one day KNIME Courses that cover a variety of topics. We'll also be running a KNIME Server half-day workshop on Friday, March 22. Watch this space for more details. We'll take a step back in time and look at the last four versions of KNIME Analytics Platform and all the neat features that have been released.

Knime (the K is silent, so it's pronounced nīm) is a highly rated data analytics platform with wide applicability and many integrations with other products, such as with databases, languages, machine learning frameworks, and deep learning frameworks. The philosophy of Knime is to be inclusive and "blend" whatever software and data sources you want to use. The exploration, model building, visualization, reporting, and development portions of the platform are open source, as are the community extensions. Knime Server, which provides collaboration, automation, management, and deployment capabilities, is commercial, as are the partner extensions. Knime Analytics Platform and Knime Server are available for on-prem installation and for the AWS and Azure clouds.

Systems that automate the data science cycle have been gaining a lot of attention recently. Similar to smart home assistant systems however, automating data science for business users only works for well-defined tasks. We do not expect home assistants to have truly deep conversations about changing topics. In fact, the most successful systems restrict the types of possible interactions heavily and cannot deal with vaguely defined topics. Real data science problems are similarly vaguely defined: only an interactive exchange between the business analysts and the data analysts can guide the analysis in a new, useful direction, potentially sparking interesting new insights and further sharpening the analysis. Therefore, as soon as we leave the realm of completely automated data science sandboxes, the challenge lies in allowing data scientists to build interactive systems, interactively assisting the business analyst in her quest to find new insights in data and predict future outcomes.