What Happens When Machine Learning And DevOps Join Hands

What Happens When Machine Learning And DevOps Join Hands?
Machine learning, the Artificial Intelligence application that facilitates systems to learn as well as
improve from experience, without having to depend on being programmed manually for each of such
instance is gaining momentum in recent times.
DevOps, the exclusive software engineering practice that aims at unifying software operation and
software development, has been instrumental in exponentially improving software development related
to increasing both productivity and quality. The relationship between DevOps and Machine Learning is
evident, and is capable of improving the ability of organizations to analyze and manipulate huge volume
of data in a rapid and accurate manner than any human resource. Here’s where app development
companies find the combo of machine learning and DevOps effective.
How Does Machine Learning Fit into DevOps Methodology and Help
Implementing machine learning in DevOps results in two distinct benefits: reduction of noise-to-signal
ratio and replacement of reactive mode with proactive approach that is based on accurate predictions.
Most teams are using the threshold approach that is based on habit, gut feelings, and conventional
wisdom for monitoring.
Compared to this, the machine learning approach, that is more mathematical, is grounded. Here,
models and methodologies such as classification, linear and logistic aggression, and deep learning are
being used for scanning huge sets of data. Identifying correlations and trends and making predictions
are enabled. Threshold defining is based on what is logically sound and statistically significant.
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Benefits Achieved by Gelling Machine Learning with DevOps
Root Cause Identification: Machine Learning helps in discovering the root cause, enabling the teams to
fix performance issues at one strike.
Learning from Mistakes: Issues caused by mistakes committed by DevOps teams can be located and
rectified by Machine Learning systems that help in analyzing the data and portraying what has taken
place.

Development metrics can be Viewed Differently: Collecting data about aspects such as bug fixes,
delivery velocity, and continuous integration systems is enabled.
Fault Prediction: Machine Learning application helps draw meaningful insights from the data produced
by monitoring tools during failure generation.
Orchestration Measurement: Orchestration process monitoring becomes easy; team performance can
be evaluated effectively with the help of Machine Learning.
Going Beyond Threshold Setting: With voluminous data, DevOps teams are held up with setting
thresholds rather than analyzing the entire data –Machine Learning applications help with predictive
analytics.
Key Areas of Machine Learning Use
• Production failure prevention
• Triage analytics and troubleshooting
• Production management
• Application delivery assurance
• Alert storms
The Need for Combining Machine Learning with DevOps
An increased number of next generation tools related to DevOps have started supporting machine
earning to varied extents.
Today’s DevOps engineers have to be aware of how to code, know how the infrastructure works, and
learn how DBaaS can be utilized in the cloud. Most of the contemporary DevOps engineers not being
mathematicians, it is a huge challenge to add machine learning skills to the skill sets mentioned here.
Challenges Faced by DevOps Engineers in Adding Machine Learning
Gap in Machine Learning Skills: For understanding machine learning that is based on applied
mathematics, developers are expected to have a clear understanding of calculus, logarithms, linear
algebra, linear programming, trigonometry, infinite series and sequences, statistics, and regression
analysis.
Organizational Challenges: Machine learning is mostly data science that has to be divided across varied
skill sets. Putting together a multi-disciplinary team consisting of Big Data programmers, Big Data
engineers, and Data Scientists is one obstacle faced by organizations.
What Does the Future Hold?

Regardless of the obstacles and challenges faced, adoption of machine learning will only be growing as it
is lucrative and is certain to attract more and more IT professionals and engineers with factors such as
high income. With algorithms becoming easily understandable and convenient to implement, thanks to
proliferation of frameworks, in future, what was once the domain of PhD scholars will be feasible to Big
Data programmers and data scientists.
With several huge benefits that can be reaped by enterprises by using the machine learning-driven
DevOps infrastructure, App development companies and managers have already started ways to boost
machine learning among their teams
How Does Machine Learning Help Optimize DevOps?
Machine Learning helps accomplish the following:
• Looking for trends becomes possible
• Fault can be predicted at fixed point of time
• Specific goals or metrics can be optimized
• Correlation across varied monitoring tools is enabled
• Historical context of data can be provided
• Effective analysis of data
Although it may take time, with the network architecture and algorithms chosen appropriately, Machine
Learning system is sure to produce great results.
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