deepsense.io congratulates Cloudera on acquiring Sense.io and their recent launch of the Cloudera Data Science Workbench. “Well done, but too late!” – says deepsense.io’s CEO Tomasz Kulakowski, and asks: “Why allow Cloudera’s vendor locking instead of using deepsense.io’s Neptune – a DevOps platform for machine and deep learning experiments, which works with any Hadoop distribution (incl. Hortonworks, Cloudera and MapR) and any cloud provider (such as AWS, MS Azure, Google Cloud Platform) as well as available on-premises?”
At Strata+Hadoop World in San Jose, Cloudera announced their new tool currently available in beta. Most of the Cloudera Data Science Workbench’s features are the same as Neptune’s, which is deepsense.io’s DevOps platform for data scientists, helping them increase their work efficiency, and thereby making machine learning experiments management and deployment faster and easier, allowing to save time for their core activity – thinking.
“Cloudera says that they give data science teams the freedom to work how they want, but I think that the real freedom is when you can choose any tool that suits your needs best.”
– Tomasz Kulakowski, CEO of deepsense.io
Tomasz Kulakowski, CEO of deepsense.io said today: “We are happy to see that the data science sector grows and that there are more and more tools supporting data scientists’ day-to-day activities. Our data scientists and software engineers have jointly created a high-performance DevOps platform for machine learning experiments, Neptune, and it’s easily available with any Hadoop distribution (incl. Hortonworks, Cloudera and MapR) and can also be deployed on-premises and in any cloud (incl. AWS, MS Azure, Google Cloud Platform, any many other). Cloudera says that they give data science teams the freedom to work how they want, but I think that the real freedom is when you can choose any tool that suits your needs best. That’s why we’ve developed Neptune, which works with any Hadoop distribution, with any cloud or on premises, with no additional deals and limits inside. I invite all data scientists and their managers around the world to visit neptune.ml to learn more.”
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