There is no doubt that more needs to be said about how time series data analysis advances DevOps. Time series classification is a tertiary aspect of time series data itself. By harnessing performance benefits from the powerful capabilities of a machine learning deployment platform, multiple types of objects are processed. The objects are classified using feature extraction to represent the data for our consumption in new ways.
Objects processed for time series classification include images, text, and audio. However, a wider range of applications from financial, security, or even medical diagnoses take full advantage of this form of deep learning AI. We may see DevOps use time series classification to identify the success of a product launch using the site’s social media data.
DevOps can use time series classification to ensure stability
Data scientists are currently engineering ML models designed to do sentiment analysis. By using this AI technology, the objects being processed are categorized by how an end-user may feel about a scenario like a product launch. Looking at the end users’ collaboration on internal and external support forums is one such source of data.
From the time a product or upgrade is released to a production environment, initial data starts flowing from the major social media outlets. Additional log and environmental data are combined that share the same timeline. The culmination of this information is further processed by deep learning AI.
By harnessing the results, DevOps can immediately start making decisions toward additional stability modifications or even a rollback to a prior state. The goal of which is to prevent customer impact as much as possible. This also relieves stress on sometimes already stretched support staff.
Microservices generate mass metrics
With the emergence of microservices, log files and other data are no longer a single stream. They include information from a large number of services hosted on serverless computing platforms. This information is in quantities multitudes greater and more complex than anything DevOps engineers usually encounter.
For example, a simple website API service may normally be hosted on a single IIS server. This service has a number of log files that show traffic patterns as well as problems that the end user may be experiencing. Current DevOps tooling includes software that helps visualize and filter these log files. However, the amount of data coming from a fully scaled implementation is far too great for most current tools in use, today.
By storing this large amount of critical information directly to cloud storage, the data is ready for more intense processing by new advancements in artificial intelligence. Since most companies have made the switch to microservices in the cloud, the application and the storage area for the application’s logs are contained in the same environment.
Algorithmia makes data analysis simple
Algorithmia is working diligently to make the working lives of DevOps engineers and data scientists easier. Our platform provides a means to access the large amounts of big data companies amass from today’s microservices and the Internet of Things. Direct access to data backed by a scalable data science platform means the possibilities for innovation are endless.
Choosing Algorithmia will allow your data scientists to focus on solving big data challenges with a scalable and highly customizable platform. All the while, DevOps engineers no longer have to manage the vast infrastructure required for a successful implementation. Their focus can remain on adding additional security, stability, and automation processes.
When team members are allowed to focus on their specializations instead of wearing multiple hats, the results can only benefit the project on which they are collaborating. Getting real results from information that would normally sit dormant is today’s new standard for the science of data analysis.
Each tier of Algorithmia’s platform includes existing pre-trained models you can build upon to solve complex problems without having to reinvent the wheel. By referencing these, or your own models, a complete solution can be developed that benefits from everything Algorithmia has to offer.