TetraScience, the Scientific Data Cloud company, announced today that Elucidata, whose data-centric platform Polly powers biological discovery with ML-ready biomedical data, has joined the Tetra Partner Network to accelerate the use of Machine Learning (ML) in life sciences R&D, with the Tetra Scientific Data Cloud . "Where machine learning has encountered large-scale, organized scientific data sets, fundamental breakthroughs have been derived," said Alan Millar, Ph.D., V.P., Tetra Partner Network. "By combining over 1.5 million ML-ready datasets curated by Polly with contextualized experimental data from the Tetra Scientific Data Cloud, our partnership accelerates data-driven discoveries. Successful ML initiatives are built on high-quality data. Elucidata's Polly provides access to clean and curated biomedical datasets fit for any tool, pipeline, or ML model.

Addressing roadblocks in machine learning adoption can help enterprises industrialize and scale AI, and ultimately embed it into businesses processes and new products and services. We are entering the golden age of machine learning (ML), with adoption increasing across all customer segments. Once considered peripheral, ML technology is becoming a core part of many business strategies around the world. From health care to manufacturing, fintech to media and entertainment, ML holds great promise for many industries. Driven by the wide availability of cloud-based computing power, storage capacity, and easy-to-use AI toolsets, the normalization of AI and ML continues at a rapid pace.

I have spent most of my professional life in the age of AI and ML. During earlier times at Uber, I worked with models that estimated ETAs, calculated dynamic pricing and even matched riders with drivers. My co-founder Jason previously led video ad company TubeMogul (acquired by Adobe), which relied on ML to ensure that its advertisers didn't waste their media spend on ads that nobody saw, or ads that only bots saw. Although ride-sharing and video advertising aren't often used in the same sentence, both Jason and I faced similar challenges in ensuring that the models our companies deployed worked effectively and without bias. When models don't work as planned and machines, trained by data, make bad decisions, there is a direct impact on business results.

Aleksandar Kovačević, Sales Engineer at InterSystems, shares how companies use MLOps combined with a central multi-model database to get the most out of their machine learning initiatives. Artificial Intelligence (AI) and Machine Learning (ML) are hot topics at the moment. But when it comes to producing quantifiable results, there is still a lot of work to be done. How can MLOps, which merges machine learning with operations (procedures and processes), help to make ML projects more successful? There is no doubt that Machine Learning and Deep Learning offer a lot of potential.

This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. Enthusiasm for artificial intelligence (AI) and machine learning (ML) remains steady for 2019. However, tech leaders admit some trepidation in terms of AI/ML project management and support. How companies manage their AI/ML projects was the topic of a recent survey by ZDNet's premium sister site, Tech Pro Research. Overall, survey respondents said that their AI/ML projects will be more difficult than previous IT projects.

Building high-quality artificial intelligence (AI) is hard work. It's a specialized discipline that historically has required highly skilled specialists, aka data scientists. Any time you require some highly skilled, highly paid practitioner to accomplish something of value, you've introduced a bottleneck into that process. That explains why there's been such a huge push for machine learning (ML) automation. It also explains why many organizations are seeking to democratize these functions to less skilled personnel.

Bangalore: An in-depth study on talent in the Machine Learning (ML) space by Zinnov, a global management consulting firm, revealed that while a few startups have a had success stories in their AI (artificial intelligence)/ML journeys, there still exists a deep chasm, and most startups and global enterprises haven't been able to succeed and/or scale, their ML initiatives. The AI/ML spend is predicted to touch $400 billion by 2020, according to industry estimates. Given this, it is more important for organizations to invest in the talent that will capitalize on this niche technology. However, acquiring and retaining, the right kind of ML talent continues to remain a significant challenge for organizations. Zinnov's study explained that a large contributor to this challenge is the skewed concentration of the niche ML talent.

A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply machine learning, others are hard at work developing strategies for the technology -- and are already realizing genuine ROI. A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply machine learning, others are hard at work developing strategies for the technology -- and are already realizing genuine ROI. The business world's focus on machine learning (ML) may seem like an overnight development, but the buzz around this technology has been steadily growing since the early days of big data. ML is beginning to deliver on the potential created by big data and analytics by turning raw data into useful, predictive tools for business. Innovation-minded business leaders are embracing ML as "the next big thing" and have already crafted ML strategies and initiatives that promise real benefits and return on investment (ROI).

A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply machine learning, others are hard at work developing strategies for the technology -- and are already realizing genuine ROI. A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply machine learning, others are hard at work developing strategies for the technology -- and are already realizing genuine ROI. The business world's focus on machine learning (ML) may seem like an overnight development, but the buzz around this technology has been steadily growing since the early days of big data. ML is beginning to deliver on the potential created by big data and analytics by turning raw data into useful, predictive tools for business. Innovation-minded business leaders are embracing ML as "the next big thing" and have already crafted ML strategies and initiatives that promise real benefits and return on investment (ROI).