With more and more applications moving to the cloud, an increasing amount of telemetry data (logs, metrics, traces) is being collected, which can help improve application performance, operational efficiencies, and business KPIs. However, analyzing this data is extremely tedious and time consuming given the tremendous amounts of data being generated. Traditional methods of alerting and simple pattern matching (visual or simple searching etc) are not sufficient for IT Operations teams and SREs. It's like trying to find a needle in a haystack.

Deep Learning is a type of Neural Network Algorithm that takes metadata as an input and process the data through some layers of the nonlinear transformation of the input data to compute the output. This algorithm has a unique feature, i.e., automatic feature extraction. It means that this algorithm automatically grasps the relevant features required for the solution of the problem. It reduces the burden on the programmer to select the features explicitly. It can be used to solve supervised, unsupervised or semi-supervised type of challenges.

I've recently developed a strong interest for performance monitoring. This is the opportunity for me to expand my skills on Application Insights, which I've been using for many years, but also complement them with an overview of all the monitoring services available on Azure. As often on Azure, these services provide similar and overlapping features and even though this page gives helpful examples of "when to use which", I thought I would outline here, and in my own words, a review of their features, differences and similarities. Infrastructure and application monitoring are very different tasks that require different metrics and different exploitation of those metrics. Infrastructure monitoring calls for both a detailed reporting of the activity of each service of your system, and a synthetic, high-level overview of the global health of that system.

Deep Learning is a type of Neural Network Algorithm that takes metadata as an input and process the data through a number of layers of the non-linear transformation of the input data to compute the output. This algorithm has a unique feature i.e. automatic feature extraction. This means that this algorithm automatically grasps the relevant features required for the solution of the problem. This reduces the burden on the programmer to select the features explicitly. This can be used to solve supervised, unsupervised or semi-supervised type of problems. In Deep Learning Neural Network, each hidden layer is responsible for training the unique set of features based on the output of the previous layer. As the number of hidden layers increases, the complexity and abstraction of data also increase.

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The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. Dendrite - It receives signals from other neurons. Soma (cell body) - It sums all the incoming signals to generate input. Axon - When the sum reaches a threshold value, neuron fires and the signal travels down the axon to the other neurons. The amount of signal transmitted depend upon the strength (synaptic weights) of the connections.

The human mind is no longer capable of keeping up with the velocity, volume, and variety of Big Data streaming through daily operations, making AI a powerful and essential tool for optimizing the analyzing and decision-making processes. Using all this information, it makes a data reservoir of relevant insights that may contain solutions to a wide range of critical issues, faced by IT operations and DevOps teams on a daily basis. The real-time obstacles DevOps engineers, IT Operations managers, CTOs, VP engineering, and CISO face numerous challenges, which can be mitigated effectively by integrating AI in log analysis and related operations. Quickly find the needle in the "IT operations" haystack and eliminate the main problems The good AI integration can yield Using AI driven log analytics systems, it becomes considerably easy to find the needle in the haystack, and efficiently solve issues.

Opening a Kibana dashboard at any given time reveals a simple and probably overstated truth -- there are simply too many logs for a human to process. Sure, you can do it the hard way, debugging issues in production by querying and searching among the millions of log messages in your system. But this is far from being a methodological and productive method. Kibana searches, visualizations, and dashboards are very effective ways to analyze a system, but a serious limitation of any log analytics platform, including the ELK Stack, is the fact that the people running them only know what they know. A Kibana search, for example, is limited to the knowledge of the operator who formulated it.

Opening a Kibana dashboard at any given time reveals a simple and probably overstated truth -- there are simply too many logs for a human to process. Sure, you can do it the hard way, debugging issues in production by querying and searching among the millions of log messages in your system. But this is far from being a methodological and productive method. Kibana searches, visualizations, and dashboards are very effective ways to analyze a system, but a serious limitation of any log analytics platform, including the ELK Stack, is the fact that the people running them only know what they know. A Kibana search, for example, is limited to the knowledge of the operator who formulated it.