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[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”] Raises $14 Million Series A To Revolutionize Simplicity, Performance And Scale For Data Analytics And Machine Learning, the extreme-performance parallel compute platform for data workloads, announced it has raised $14 million in Series A funding led by Dell Technologies Capital, with participation from Uncorrelated Ventures, Fusion Fund and Candou Ventures.

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Founded in 2019 to revolutionize complex data analytics and machine learning applications, Bodo’s goal is to make Python a first-class, high-performance and production-ready platform. The company’s innovative compiler technology enables customers to solve challenging, large-scale data and machine learning problems at extreme performance and low cost with the simplicity and flexibility of native Python. Validated at 10,000+ cores and petabytes of data, Bodo delivers a previously unattainable supercomputing-like performance with linear parallel scalability. By eliminating the need to use new libraries or APIs or rewrite Python into Scala, C++, Java, or GPU code to achieve scalability, Bodo users may achieve a new level of performance and economic efficiency for large-scale ETL, Data Prep, Feature Engineering, and AI/ML Model training.

“Big data is getting bigger, and in today’s data-driven economy, enterprise customers need speed and scale for their data analytics needs,” said Behzad Nasre, co-founder and CEO of “Existing workarounds for large scale data processing like extra libraries and frameworks fail to address the underlying scale and performance issues. Bodo not only addresses this, but does so with an approach that requires no rewriting of the original application code.”

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Python is the second most popular programming language in existence largely due to its popularity among AI and ML developers and data scientists. However, most developers and data engineers who rely on Python for AI and ML algorithms are hampered by its sub-optimal performance when handling large-scale data. And those who use extensions and frameworks still find their performance falls orders of magnitude short of Bodo’s. For example, a large retailer recently achieved more than 100x real time performance improvement for their mission-critical program metric analysis workloads and saved over 90% on cloud infrastructure costs by using Bodo as opposed to a leading cloud data platform.

“Customers know that parallel computing is the only way to keep up with computational demands for artificial intelligence and machine learning and extend Moore’s Law. But such high-performance computing has only been accessible to select experts at large tech companies and government laboratories,” added Ehsan Totoni, co-founder and CTO of “Our inferential compiler technology automates the parallelization formerly done by performance experts, democratizing compute power for all developers and enterprises. This will have a profound impact on large-scale AI, ML and analytics communities.”

Bodo bridges the simplicity-vs-performance gap by delivering compute performance and runtime efficiency with no application rewriting. This will enable hundreds of thousands of Python developers and data scientists to perform near-real-time analytics and unlock new revenue opportunities for customers.

“We see enterprises using more ML and data analytics to drive business insight and growth. There is a nearly constant need for more and better insights at near-real-time,” said Daniel Docter, Managing Director, Dell Technologies Capital. “But the exploding growth in data and analytics comes with huge hidden costs – massive infrastructure spend, code rewriting, complexity, and time. We see Bodo attacking these problems head-on, with an elegant approach that works for native Python for scale-out parallel processing. It will change the face of analytics.”

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