Model Monitoring in Production: Drift, Decay and Alerting
Model monitoring in production has to catch decay, not just drift. Four signals, the label latency problem, thresholds that hold, and alerts people act on.
Paweł Szczepanik
MLOps
LLMOps Consulting: When MLOps Isn't Enough
When your MLOps stack can't tell if an LLM feature is improving, LLMOps consulting fills the gap. What it covers, six signs you need it, how to choose.
Paweł Szczepanik
Data Quality for Machine Learning: A Guide
Models degrade quietly when inputs go bad. Data quality dimensions that matter for machine learning, tests with contracts and dbt, and drift monitoring.
Paweł Szczepanik
MLOps vs LLMOps: Key Differences in 2026
MLOps vs LLMOps explained: how operating LLMs differs from classic ML—evaluation, prompts, cost, monitoring—and what your team needs to run GenAI in production.
Paweł Szczepanik
MLOps Consulting Services: What They Cover & How to Choose
A model that never leaves the notebook is a cost. What MLOps consulting services cover, when you need them, and how to choose a partner that ships models.
Paweł Szczepanik
Applications of MLOps in FMCG: Optimizing the Supply Chain
MLOps in FMCG supply chains: automating the deployment, monitoring and management of ML models, with the key tools, best practices and results to expect.
Best Practices for Implementing MLOps in FMCG
Implementing MLOps in FMCG works best with solid data management, CI/CD automation, model lifecycle management and close collaboration between teams.
Leveraging MLOps in the FMCG Industry: Key Benefits, Tools, and Practical Examples
MLOps in FMCG: how automated ML deployment and monitoring support demand forecasting and inventory management, with key tools and practical examples.
The Emergence of MLOps
MLOps brings DevOps practices to machine learning. Its core components and how FMCG companies apply them to demand forecasting, inventory and marketing.