Both DataStax and Kaskada have a track record of contributing to open source communities. Datastax will open source the core Kaskada technology initially, and it plans to offer a new machine learning cloud service later this year. Most machine learning initiatives don't deliver the results that businesses need because the process is manual, complex and frustrating. Compounding this problem, many models underperform because they lack the relevance and context of real-time data. The addition of Kaskada to DataStax's portfolio of cloud services--which today includes the massively scalable Astra DB database-as-a-service built on Apache Cassandra and event streaming with Astra Streaming-- will give organizations a single environment to easily and cost-effectively deliver applications infused with real-time AI, using an advanced ML/AI model proven by industry leaders such as Netflix and Uber.

Katie Dee is a graduate of SUNY Oneonta where she studied english with a heavy focus on writing and editing. Her passion for writing and interest in tech led her to become an Online and Social Media Editor for SD Times. She is also a lifelong dancer and was a member of the Oneonta State Kickline Team while in school.

Number one, I'm looking at improved productivity of users, the technical experts, citizen developers, or business users. Secondly, if complexities are solved, it opens up for business users to carry out integration tasks almost without any support from a central IT team, or your integration specialist, such as a data engineer,

Granade predicts that those companies that find themselves behind the curve on data science and machine learning practices will work quickly to correct their mistakes. Organizations that have tried and failed to implement this kind of technology will keep their eye on the others that have succeeded and take tips where they can get them. Not adapting to this practice isn't an option in most industries as it will inevitably lead to certain companies falling behind as a business. Granade goes back to a comprehensive approach as the key to remedy the mistakes he has seen. "I think you can say'we're going to invest as a company to build out this capability holistically. 'We're going to hire the right people, we're going to put a data science process in place, and the right tooling to support that process and those people,' and I think if you do that you can see great results," he said.

"The opportunity to get in on the ground floor of a transformative set of technologies doesn't come along often. When one does, it is usually inaccessible to all but a select group of specialists. For now, AI 2.0 has leveled the playing field by eliminating many barriers to entry built on years of expertise in AI domains like natural language processing, computer vision, and data advantages painstakingly built over years. Newcomers are outperforming veterans, and startups are building new applications that used to take years or were infeasible. Could you wait and take advantage of AI 2.0 solutions once they are mature? Yes, but you would forgo the opportunity to outperform your industry," Forrester wrote in the report.

The ethics of AI and its potential biases were also more heavily talked about this year, with the Black Lives Matter movement bringing more attention to an issue that has been talked about in the industry for the past few years. Anaconda's 2020 State of Data Science report revealed that social impact that stems from bias in data and models was the top issue that needs to be addressed in AI and machine learning, with 27% of respondents citing it as their top concern.

Deployment can be incredibly challenging, but like any skill, having a good guide can help show you the right path and give you the real-world experience so you can maximize your efficiencies. At Six Nines, we've developed a guide to help practitioners who are just starting out to understand the decision-making processes needed to get their data models from concept to a working ML training deployment, and then scale those deployments into clusters. The guide, titled "Getting started with a ML training model using AWS & PyTorch," helps walk practitioners through decisions around which AWS instances are right for the ML model they're trying to train, and what steps to take to get started. Beginners just starting, up to skilled practitioners who are looking for a shortcut to getting their models into the right cloud environment can benefit from this tutorial.

Ridesharing is a coordination problem in its core. Traditionally it has been solved in a centralized manner by ridesharing platforms. Yet, to truly allow for scalable solutions, we needs to shift from traditional approaches, to multi-agent systems, ideally run on-device. In this paper, we show that a recently proposed heuristic (ALMA), which exhibits such properties, offers an efficient, end-to-end solution for the ridesharing problem. Moreover, by utilizing simple relocation schemes we significantly improve QoS metrics, by up to 50%. To demonstrate the latter, we perform a systematic evaluation of a diverse set of algorithms for the ridesharing problem, which is, to the best of our knowledge, one of the largest and most comprehensive to date. Our evaluation setting is specifically designed to resemble reality as closely as possible. In particular, we evaluate 12 different algorithms over 12 metrics related to global efficiency, complexity, passenger, driver, and platform incentives.

"Whether businesses are using machine learning to perform predictive maintenance or create better retail shopping experiences, ML has the power to unlock value across a myriad of use cases. We're constantly inspired by all the ways our customers use Google Cloud AI for image and video understanding--everything from eBay's use of image search to improve their shopping experience, to AES leveraging AutoML Vision to accelerate a greener energy future and help make their employees safer. Today, we're introducing a number of enhancements to our Vision AI portfolio to help even more customers take advantage of AI," Google product managers Vishy Tirumalashetty and Andrew Schwartz wrote in a post.

The combination of Aible Advanced for data scientists and Aible for business people allows experts to scale themselves by enforcing best practices while easily soliciting business user input to maximize business impact. Aible Advanced users can solicit feedback from Aible users or even delegate specific steps of the end-to-end process to specific business people using Aible,