Machine Learning Development Timeline: From Proof of Concept to Production
How long machine learning development takes, phase by phase from discovery and data preparation to proof of concept, validation, MLOps and operation, with…
Hetal KENGINEERING BLOG
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19 articles in Generative AI & LLMs
Hetal K
Machine Learning Consulting vs ML Development: Which Do You Need First?
How machine learning consulting and ML development differ, what each should deliver, a decision matrix for which to start with, and how a…
Harshad Patel
Custom ML Model vs Off-the-Shelf ML Platform: Ownership, Cost and Fit
When to use an off-the-shelf ML platform and when to build a custom ML model: the option spectrum, decision criteria, a decision path,…
Vimisha K
The Real Cost of LLM Hallucinations: Error Budgets, Controls and ROI
How to estimate the cost of LLM hallucinations, set error budgets by use case, choose controls that pay back, and measure hallucination rates…
Hetal K
How to Choose a Machine Learning Development Company: An Evaluation Checklist
How to choose a machine learning development company: six areas to evaluate, questions to ask, running a paid pilot, engagement models and red…
Vimisha K
Machine Learning Development Cost in 2027: Model, Data and MLOps Budgets
Indicative 2027 machine learning development costs by project type, where the budget goes, running and retraining costs, custom ML vs foundation models and…
Paresh Purohit
Build vs Buy AI: A Decision Framework for CTOs and Product Leaders
A practical build vs buy AI framework: the spectrum of options, decision criteria, a decision matrix, three-year cost comparison, vendor checklist and the…
Darshil Hirpara
How Long Does AI Development Take? Realistic Timelines from PoC to Production
Realistic AI development timelines by project type, a phase-by-phase schedule, the most common causes of delay, milestone exit criteria and ways to deliver…
Vimisha K
Fine-Tuning LLMs with LoRA and QLoRA: Architecture, Data Quality and GPU Cost
Compare LoRA and QLoRA architecture, dataset quality, training memory and GPU cost. Includes adapter configuration, evaluation gates and deployment checks.
Mansukh Marakana
AI Model Drift Detection: Data Drift vs Concept Drift and Monitoring Techniques
Distinguish data drift from concept drift, design monitoring windows and investigate alerts before retraining. Includes a transparent distance example and production checklist.
Vimisha K
Speaker Diarization Explained: Models, Accuracy Metrics and Production Challenges
Understand speaker diarization, DER components, overlap and attribution failures. Includes a pipeline, evaluation checklist and production error taxonomy.
Paresh Purohit
Speech-to-Text Architecture: Real-Time vs Batch Transcription for Contact Centers
Compare real-time and batch transcription for contact centers, including streaming latency, diarization, scheduling, protected recordings and evaluation checks.
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