DEV Community: mansi kandari

Which Cloud Certification Is Most In Demand in 2026?

mansi kandari — Wed, 03 Jun 2026 10:41:54 +0000

Cloud-certified professionals are the most demanded IT and cybersecurity workers as organizations continue to ramp up their cloud adoption. In 2026, jobs for cloud architecture, security, and operations are still highly demanded throughout organizations globally.

Top Cloud Certifications include:

AWS Certified Solutions Architect – Associate/Professional
Microsoft Certified: Azure Solutions Architect Expert
Google Cloud Professional Cloud Architect
Certified Cloud Security Professional (CCSP)
AWS Certified Security – Specialty
Microsoft Azure Security Engineer Associate (AZ-500)

These certifications (AWS, Azure, CCSP) still continue to lead in job postings for the demand for more security and governance/compliance of cloud infrastructure and data as more organizations make their services cloud-based. Jobs for those who hold any of these certifications are as Cloud Architects, Cloud Security Engineers, DevSecOps Engineers, Cloud Consultants, and Security Analysts. Obtaining a certified cloud certification will only boost your career, and it's well worth to have it if you wish to become competitive.

Top Tools and Techniques for Model Interpretability

mansi kandari — Fri, 22 May 2026 10:49:19 +0000

With AI currently powering decisions on life, money and justice, it is not just the prediction but why it is predicted that matters in today’s world. Interpretation gives a way to understand complex, "black box" algorithms from humans' perspective: allowing model developers to debug their models, regulators to check their compliance and everyone else involved in a decision to confirm the fairness of a model's predictions.

Why Interpretability Matters

Benefit - Description

Transparency - Reveals how input features influence predictions

Accountability - Enables tracing decisions to specific model behaviors
Debugging - Helps identify biases, errors, or data leakage witness

Regulatory Compliance - Meets requirements like GDPR's "right to explanation" dzone

Trust Building - Increases stakeholder confidence in AI systems

Key Interpretability Techniques

1. SHAP (SHapley Additive exPlanations)
This technique relies on game theory and computes the contribution of each feature to the prediction via Shapley values, which define the average marginal contribution of every feature value over all possible feature combinations.

Strengths: Mathematically robust, model-agnostic, delivers local and global explanations

Suitable for: Any ML model for which we require detailed feature attributions

2. LIME (Local Interpretable Model-agnostic Explanations)
LIME approximates a black-box model locally, around a prediction, by using a simple interpretable model, such as linear regression, that approximates its behavior.

Strengths: Compatible with any model, provides explanations for individual predictions

Suitable for: Understanding specific individual predictions, such as those for text or image classifiers

3. Integrated Gradients
It attributes the prediction by integrating gradients along a path from a baseline input to the specific input for which we want to attribute the prediction and is useful for deep learning models.

Strengths: Satisfies completeness axiom, suitable for neural networks

Suitable for: Deep learning models, especially for image classification tasks

4. Partial Dependence Plots (PDP)
The PDP displays the marginal effect of one or two features on the predicted outcome of the model and it represents how the prediction averages over the effects of all other features.

Strengths: Visualizes global feature relationships, easy to interpret

Suitable for: Gaining insights about interactions and non-linear relations among features

5. Anchor Explanations
These are conditions that are minimal and uniquely identify a prediction and are effective especially for image classification tasks. Anchors provide "if-then" statements that make the predictions human interpretable and are very accurate.

Strengths: Results are easily interpretable, very accurate

Suitable for: Image classification and for generating explainable rules

6. Counterfactual Explanations
These reveal the minimum changes made to input features for the model's prediction to change and serve the purpose of answering "what if" questions.

**Strengths: **Actionable for decision making and very easy for people to understand

Suitable for: Decision support systems and loan approvals or medical diagnoses

7. ELI5 (Explain Like I'm 5)
This Python library explains ML models both locally and globally; it presents weights for features as well as decision trees in human readable format.

Strengths: Easy to use API, works with numerous frameworks, such as XGBoost, LightGBM, CatBoost and scikit-learn

Suitable for: Quick model exploration, parameter debugging

Best Practices for Model Interpretability

Start with inherently interpretable models when transparency is critical (linear models, logistic regression, decision trees)
Combine multiple techniques (e.g., SHAP for global understanding + LIME for local explanations)
Validate explanations with domain experts to ensure they align with real-world knowledge
Document interpretation methodology for audit trails and regulatory compliance
Use visualizations to make complex explanations accessible to non-technical stakeholders

**
Conclusion**
Model interpretability is no longer optional—it's essential for responsible AI deployment. By leveraging tools like SHAP, LIME, Captum, and techniques like Integrated Gradients and Counterfactual Explanations, data scientists can demystify complex models while maintaining performance. The key is selecting the right combination of tools based on your model type, use case, and stakeholder needs, ensuring AI systems remain transparent, accountable, and trustworthy.

What is Agentic AI?

mansi kandari — Wed, 20 May 2026 10:26:13 +0000

The Future of Autonomous Artificial Intelligence

The next frontier of AI are those that can not only analyze your input, but are actually autonomous-they can set goals, plan tasks, adjust to change, and take action to achieve objectives. Essentially Agentic AI works as a digital 'agent' who can work with less human intervention.

What is Agentic AI?

Today's tools are very reactive. You give it an input, and it provides an output. Agentic AI integrates Reasoning, Decision making, Goal planning, Memory, and autonomous action. So an AI agZZent can look at a situation, determine the best next step, execute actions and adapt the approach over time. Normal Chatbot just provides an answer. Agentic AI assistants could schedule a meeting, write an email, search for research, and monitor progress automatically.

How is Agentic AI different from Generative AI?

Core features of Agentic AI:

Autonomy: Agentic AI operate independently of human operators.
Goal oriented: Agentic AI are not designed to respond to one-time prompts; they are built to achieve specific outcomes.
Planning and reasoning: The systems can break down complicated tasks into smaller, manageable steps and execute them logically.
Memory and learning: Agentic AI remember previous events and apply the learning to future interactions.
Tool Integration: AI Agents have the ability to interface with APIs, other software, databases and other tools to get a task done.

Use cases for Agentic AI

Agentic AI are transforming industries:

Cybersecurity

Detection of threats in real time
Automated incident response
Suspicious activity monitoring
Reduced SOC alert fatigue

Customer Support

Automatic ticket processing
Self service and automated query resolution
Personalised support experience

Healthcare

Automated diagnostics and monitoring
Streamlined administration tasks

Software Development

Automatic code writing and debugging
Optimisation of existing code

Business Operations

Workflow automation
Scheduling and appointments management
Automated report generation and analysis

Advantages of Agentic AI

Increased productivity
Faster decision making
Lower operating costs
Greater automation
Enhanced cybersecurity defenses
Improved customer service

Organizations are investing heavily in Agentic AI because the agents can automate critical business processes and workflows that previously necessitated human input from a team.

Challenges and Risks

The risks associated with the use of Agentic AI include:

Security risks
Ethical challenges
Lack of transparency
Overreliance on automation
Potential for autonomous errors

Effective governance, monitoring, and human intervention continue to be important.

Future Applications of Agentic AI

Agentic AI is set to play a critical role in the future of:

Autonomous security systems
Intelligent personal assistants
AI-native businesses
Robotics and automation
Intelligent business operations

As AI technology matures, the demand will continue to rise for intelligent agents which have the capability to act autonomously in order to achieve a goal.

Concluding Thoughts

Agentic AI is the latest revolution in artificial intelligence. Where an AI that responds to the input of a user is passive in a way, the advanced capabilities of Agentic AI agents will allow them to 'think', 'plan', and 'act' autonomously.

Agentic AI is set to transform the ways businesses function and innovate in every domain from cybersecurity to automating entire business processes. Businesses which harness the potential of Agentic AI earlier will have a definitive advantage in the upcoming AI era.