One of the most critical components in machine learning projects is the quality of an organization's database management system. And as artificial intelligence (AI) continues to grow more complex, access to adequate data is an increasingly important component of a company's success. For deep learning, forward-thinking companies must choose to upgrade to more robust and efficient databases. As reported by the World Economic Forum, the "deep" in deep learning refers to the depth of layers in a neural network. A neural network consisting of more than three layers--which would be inclusive of the inputs and the output--can be considered a deep learning algorithm.

There are plenty of tools and point solutions that address bits and pieces of the challenge of delivering artificial intelligence (AI) and Internet of things (IoT) applications. C3.ai's focus is on delivering an end-to-end platform for developing, deploying and running these applications in production at scale. Whether customers use every aspect of the C3.ai platform or not, big enterprise-scale companies seem to be attracted by that promise of quickly developing and running innovative, data-driven applications at scale. There was plenty of evidence of that fact at C3.ai's February 25-27 Transform conference in San Francisco, where customers including Bank of America, Shell, 3M and Engie detailed their deployments. C3.ai's cloud-first platform is comprehensive, addressing the needs of developers, data engineers and data scientists, and the operational teams challenged with bringing applications into production at scale.

Data is the new strategic asset, the biggest business asset today. Data is to today's digital economy what electricity was to the industrial economy. Organizations that understand the value of their data have been excited about the prospects of leveraging artificial intelligence (AI) and machine learning (ML) for smarter insights. They have invested in AI and ML tools and technologies, but have yet to see quantifiable benefits from their investments. Others are reluctant to even start, with a combination of skepticism, lack of expertise, and lack of confidence in the reliability of their datasets holding them back.

The amount of new technologies in 2017 has been overwhelming: The cloud was adopted faster than analysts projected and brought several new tools with it; AI was introduced into just about all areas of our lives; IoT and edge computing emerged; and a slew of cloud-native technologies came into fruition, such as Kubernetes, serverless, and cloud databases, to name a few. I covered some of these a year ago in my 2017 predictions and it's now time to analyze the trends and anticipate what will likely happen in the tech arena next year. While we love new tech, the average business owner, IT buyer, and software developer glaze over this massive innovation and don't know how to start turning it into business value. We will see several trends emerge in 2018, and their key focus will be on making new technology easy and consumable. Amazon and the other cloud providers are in a race to gain and maintain market share, so they keep on raising the level of abstraction and cross-service integration to improve developer productivity and strengthen customer lockins.

We sat down with Maxim Lukichev, Lead Data Scientist, Reltio, to go over Reltio Insights and how it seamlessly leverages reliable data as part of Reltio Cloud Modern Data Management Platform as a Service to power advanced analytics and machine learning, and feed data-driven applications. "A major benefit you get with Reltio Insights is time to value. You can develop analytic algorithms in a matter of weeks, instead of months, because you have a prebuilt reliable MDM foundation that is continuously synchronized and kept up to date. Once the foundation is in place you can add new attributes and sources at the speed of business. All of this can be accomplished at a fraction of the cost of building data lakes that rapidly turn into swamps due to poor quality data."