Frequent changes are made easy by continuous software delivery concepts that create an automated pipeline and fragility is reduced by applying production infrastructure operations best practices. The solution provides a self-service platform so that teams can focus on exploring their datasets, writing their algorithms, generating more experiments, and conducting their analysis rather than worrying about deploying and managing the underlying infrastructure. The Mesosphere Jupyter Service (MJS), a new package addition to our DC/OS Service Catalog (notated in the Catalog as "JupyterLab"), is one of the first steps to help tackle some of these challenges and introduce operational best practices. An entire team can spend weeks to setup the underlying stack and yet running into many operational pitfalls that come with operating these environments.Īccelerate machine learning models with Mesosphere Jupyter Service (MJS)
INSTALL APACHE SPARK FOR JUPYTER MANUAL
This hidden technical debt creates a tremendous amount of manual work to operationalize orchestration of distributed systems like Spark or TensorFlow.ĭeployment requires many lines of DIY codeĭeploying Jupyter Notebooks and interfacing with Spark or Distributed TensorFlow in a multi-tenant environment requires many lines of DIY code and a significant investment of time before even one single line of machine learning code can be written. When software engineers use code that is easy to implement in the short run, instead of applying the best overall solution, it can slow shipping speeds and can become increasingly difficult to implement changes later on, especially when there's frequent changes to data and models that need to be continuously updated. Moving quickly often introduces technical debt While machine learning models present many opportunities for enterprises, there are many challenges when it comes to operationalizing them. Enterprises all over the world use artificial intelligence today, ranging from insurance, healthcare, to the automotive industry.
Machine learning is key to digital transformation and will become a competitive differentiator for enterprise organizations over the next years.
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