Dask

Aggregate data from all observability, monitoring, change and topology tools. BigPanda’s Open Box Machine Learning will correlate the data into a small number of actionable insights so incidents are detected in real-time, as they form, before they escalate into outages. Accelerate incident and outage resolution by automatically identifying the probable root cause of problems. BigPanda identifies both root cause changes and infrastructure-related root causes. Resolve incidents and outages faster. BigPanda automates and streamlines the incident response lifecycle across incident triage, ticketing, notifications, and war room creation. Accelerate remediation by integrating BigPanda with enterprise runbook automation tools. Applications and cloud services are the lifeblood of every company. When there’s an outage, everyone is impacted. BigPanda cements AIOps market leadership with $190M in funding, $1.2B valuation.

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.