Who is Akash Kachhadiya?

Akash Kachhadiya is an AI/ML Engineer at Calyza Tech (Ahmedabad, India) with a B.Com background and a transition into Data Science and AI. He holds three AWS certifications.

What are Akash's AWS certifications?

AWS Certified Machine Learning Engineer – Associate, AWS Certified Solutions Architect – Associate, and AWS Certified AI Practitioner.

What does Akash specialize in?

Machine Learning, Generative AI, Data Science, and building/deploying ML solutions on AWS using Python, SQL, and data platforms like Databricks.

Where is Akash based?

Home town Surat, Gujarat; currently based in Ahmedabad, Gujarat, India.

How can I contact or hire Akash?

Via the contact form on this site, email (akashkachhadiya55@gmail.com), LinkedIn, or GitHub.

Hello, I'm

Akash Kachhadiya

AI/ML Engineer

Building intelligent, cloud-native solutions — turning data into real business impact with Python, AWS & Generative AI.

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About Me

From Commerce to AI Engineering

I started with a B.Com foundation, which gave me a strong grip on business logic, finance, and data analysis. I then moved deep into Data Science and Machine Learning, and today I work as an AI Engineer at Calyza Tech, building ML and Generative AI solutions on AWS.

My edge is combining business understanding with technical depth — I don't just build models, I build solutions that solve real business problems. With 3 AWS certifications and hands-on production experience, I bridge the gap between data and impact.

📍 Ahmedabad, Gujarat, India

0AWS Certifications

0+ML Projects

0Companies

Skills & Technologies

What I Work With

Interactive Neural Physics•Drag & collide technologies to ignite electrical storm arcs

Generative AI & LLMs

Large Language Models (LLMs)RAG (Retrieval-Augmented Generation)RAGAS (RAG Evaluation)Prompt EngineeringGoogle GeminiAI AgentsMulti-Agent SystemsCustom AI ApplicationsFine-tuningVector DatabasesEmbeddingsFunction / Tool Calling

AI Observability & Evaluation

Arize AI / PhoenixDatadogLLM TracingModel EvaluationOpenTelemetryPrompt MonitoringDrift Detection

Machine Learning

Supervised & Unsupervised LearningDeep Learning (ANN, CNN)NLPEnsemble MethodsFeature EngineeringModel DeploymentMLOpsTime Series Analysis

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Cloud & AWS

AWS Solutions ArchitectureAmazon BedrockAmazon SageMakerAWS LambdaServerless ArchitectureCloud InfrastructureS3 / EC2 / IAMGoogle Cloud (GCP)

Data Engineering & Science

PythonSQLData PreprocessingEDAStatistical AnalysisETL PipelinesMongoDB

Tools & Collaboration

PyTorchTensorFlowScikit-learnPandasNumPyLangChainDockerGit / GitHubPower BIJupyterPostmanJiraConfluenceSlackMicrosoft TeamsOutlook

Generative AI & LLMs

AI Observability & Evaluation

Arize AI / PhoenixDatadogLLM TracingModel EvaluationOpenTelemetryPrompt MonitoringDrift Detection

Machine Learning

Supervised & Unsupervised LearningDeep Learning (ANN, CNN)NLPEnsemble MethodsFeature EngineeringModel DeploymentMLOpsTime Series Analysis

Cloud & AWS

AWS Solutions ArchitectureAmazon BedrockAmazon SageMakerAWS LambdaServerless ArchitectureCloud InfrastructureS3 / EC2 / IAMGoogle Cloud (GCP)

Data Engineering & Science

PythonSQLData PreprocessingEDAStatistical AnalysisETL PipelinesMongoDB

Tools & Collaboration

PyTorchTensorFlowScikit-learnPandasNumPyLangChainDockerGit / GitHubPower BIJupyterPostmanJiraConfluenceSlackMicrosoft TeamsOutlook

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Machine Learning Engineering

Featured Projects & Case Studies

Real-world predictive models and machine learning pipelines built from clinical, customer, and enterprise datasets with rigorous evaluation benchmarks.

Liver Disease Prediction & Clinical Diagnostics

Early clinical screening of hepatic disease leveraging routine blood panel biomarkers and SMOTE balancing

Clinical Recall (Sensitivity)

Gradient Boosting (High Recall) & SVM (High Precision)

78.57%

SVM Precision: 89.06% | F1: 76.74%

Hepatic disorders (cirrhosis, acute liver damage, chronic hepatitis) frequently progress asymptomatically until organ failure occurs. Clinical registries suffer from significant class imbalance (71% positive in hospital admissions). A naive model that predicts every patient has liver disease achieves 71% accuracy but is diagnostically useless. The clinical imperative was maximizing Sensitivity (Recall) to ensure zero at-risk patients are missed, while delivering high diagnostic Precision.

Indian Liver Patient Dataset (ILPD) · 583 patient records

PythonScikit-LearnGradient BoostingSVM+4

Case Study Code

Clinical Skin Disorder Multi-Class Classification

Automated differential diagnosis of 6 erythemato-squamous dermatology diseases with 98.65% multi-class accuracy

Multi-Class Test Accuracy

Bagging Classifier (200 Estimators)

98.65%

Macro F1-Score: 98.67% | 5-Fold CV: 97.60%

Erythemato-squamous skin disorders (Psoriasis, Seborrheic Dermatitis, Lichen Planus, Pityriasis Rosea, Chronic Dermatitis, Pityriasis Rubra Pilaris) present with nearly indistinguishable surface symptoms (erythema and scaling). Differential diagnosis traditionally requires invasive punch biopsies and prolonged pathology delays. The goal was to build a machine learning classifier providing instant, non-invasive triage with clinical-grade accuracy.

Dermatology Dataset · 366 patient cases

PythonScikit-LearnBagging ClassifierRandom Forest+3

Case Study Code

Telecom Customer Churn Analytics & Retention Pipeline

Machine learning churn risk engine achieving 93.51% retention prediction accuracy and 75.25% F1-score

Retention Test Accuracy

Gradient Boosting Classifier (500 Estimators)

93.51%

F1-Score: 75.25% | 5-Fold CV: 94.99%

Retaining existing telecom subscribers costs up to 5x less than acquiring new ones. 'No-Churn Telecom' in Europe faced an unsustainable 14.2% churn rate. Lacking proactive churn visibility, the marketing team could only react after customers had already switched to competitors. The business goal was building a machine learning retention engine to flag at-risk subscribers 30 days before contract termination.

No-Churn Telecom European Dataset · 4,617 subscribers (656 churned = 14.2%)

PythonScikit-LearnGradient BoostingXGBoost+4

Case Study Code

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Experience

My Journey

AI Engineer

Full-time

Calyza Tech · Ahmedabad, Gujarat, India · On-site

Mar 2026 – Present

Building Generative AI and Cloud Computing solutions.
Developing and deploying ML models in production environments.

Generative AICloud ComputingPythonAWS

AI/ML Intern

Internship

Calyza Tech · Ahmedabad, Gujarat, India · On-site

Sep 2025 – Mar 2026

Worked on AI/ML research and development projects.
Gained hands-on experience with production ML pipelines.

Machine LearningPythonData Science

Data Scientist

Internship

Rubixe – AI Solutions Company · Karnataka, India · Remote

Nov 2024 – May 2025

Assisted in data cleaning, preprocessing, and EDA for client projects.
Developed predictive models using Python and scikit-learn.
Created dashboards and visual reports using Power BI, matplotlib, seaborn.
Collaborated cross-functionally to translate business problems into data solutions.

PythonScikit-learnPower BIStatistical AnalysisData Visualization