This post demonstrates a *basic* example of how to build a deep learning model with Keras, serve it as REST API with Flask, and deploy it using Docker and Kubernetes. This is NOT a robust, production example. This is a quick guide for anyone out there who has heard about Kubernetes but hasn’t tried it out yet. To that end, I use Google Cloud for every step of this process.
Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we’ll talk about why incorporating AI into your UEM strategy may be inevitable.
This year, at Sumo Logic’s third annual user conference, Illuminate 2018, we presented Sumo Logic Notebooks as a way to do data science within the Sumo Logic platform. Sumo Logic Notebooks integrate Sumo Logic data, data science notebooks and common machine learning frameworks.
AppDynamics surveyed 6,000 global IT leaders about application performance monitoring and AIOps. Read on to discover the trends shaping the space.
Chronically understaffed and constantly stressed-out IT Ops and NOC teams are overwhelmed by today’s IT noise. Artificial Intelligence (AI) and Machine Learning (ML) can help these teams because ML (and AI) are exceptionally good at processing enormous volumes of very complex data in real-time, or near real-time, and surfacing actionable insights. But ML successes in IT Ops are still hit-or-miss.
You’ve just recovered from a critical application outage and your team is being asked to report on root cause and recommended remediation steps later this afternoon. Can you quickly analyze all the data, identify all the leading events, and discern which one was responsible for the cascading failure?
Rapidly increasing IT complexity, customer expectations around application availability and performance, and the importance of supporting new digital initiatives and services, taken together, are placing unprecedented demands on Network Operations Centers (NOCs) and IT Operations teams inside large, complex organizations like yours.
We all know the latest trend in today’s technology and how Machine Learning is changing the way business decisions are made. Machine learning replaces old manual repeatable processes and provides the systems the ability to get into a mode of self-learning without being explicitly programmed.
At first glance, applying machine learning to Continuous Delivery might sound a bit like cracking a peanut with a sledgehammer. I mean, how hard can deployment automation actually be? As it turns out, it’s way more complex than we think.
