DevOps: Why is it Important and How Does it Help?

As you’re probably already well aware, DevOps skillsets continue to be in high demand. Plain and simple, there are more job openings than qualified candidates. Ultimately, the only remedy to this is time – as more firms adopt these methods, more engineers are exposed to the tools and ideas, resulting in a larger pool of qualified candidates.

How do we get there?

How can an organization effectively teach their workforce about these new ideas, while not interrupting business as usual? First, you need buy-in from the team. Teaching people how DevOps can help their day-to-day and improve the quality of their job is one way to get a team interested in learning. I think almost every engineer would love to be able to spend less time on mundane tasks that can be automated, and more time on building something that they are passionate about.

Added value 

DevOps, when used correctly, is a force multiplier and does not have to be as daunting as it may seem. In the past I worked with a gentleman who consults for small businesses that operate online stores. He was able to give his clients instant ROI by implementing automated systems to scale servers up or down as needed.

Automated infrastructure scaling is one area that can result in instant savings. Some firms would only need this temporarily for an expected traffic spike (i.e. Christmas shopping), some needed it permanently. The main point is that there was an immediate value add with zero disruption.

Another thing that can be done is implementing monitoring tools. When you begin to use multiple different services within a cloud provider, it can become a job in itself to manage and monitor performance. Using tools to automatically pull data and create dashboards will allow your organization to get insights on system/network performance. It can also provide valuable information on how end users interact with the service.

Adapting is essential

Adding new tools and methods will always carry an element of risk, but failure to adapt will almost guarantee your downfall in today’s tech-focused landscape. DevOps will make it easier to make changes down the road, it will allow you to re-use resources and standardize best practices across your organization, and ultimately will be necessary to keep up with the rest of the pack.

Every industry is changing (recruitment included) – being on the winning or losing side will come down to technology. Would you rather be Blockbuster or Netflix? Your tech stack will decide.

 

Are looking for talented DevOps professionals to join your team?  Speak with our specialist DevOps consultants to hear about the hottest DevOps talent.

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React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

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Programming in Python, R, and SQL
Python
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Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
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Real-Time Data Streaming

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

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LLMs: Open AI, Anthropic, Llama, Gemini ML Platforms: SageMaker, Vertex AI, Azure ML ML Libraries: scikit-learn, XGBoost, LightGBM DL Frameworks: TensorFlow, PyTorch, Keras LLM Frameworks: LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Milvus Data Science Notebooks: Jupyter, Colab, Kaggle Data Visualization Libraries: Matplotlib, Seaborn NLP Libraries: Hugging Face Transformers CV Libraries: OpenCV, Pillow, Albumentations MLOps Tools: MLflow, Kubeflow, W&B

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Deep Learning Architectures
NLP and NLG
Computer Vision
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MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications

Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications