Minich Analytics

We help you develop, deploy and monitor your Machine Learning and AI systems in production.

[Minich Analytics @MinichAnalytics]

Why Keyword Search Alone Won’t Scratch the Search Itch https://t.co/yDjUW7KMTB

[Minich Analytics @MinichAnalytics]

Knowledge graphs are becoming the retail brain.
In my current project, they connect:

What’s in stock

What’s selling

What’s moving

What’s at risk

Paired with GenAI, they turn complex inventory data into real answers.

#AI #SupplyChain #RetailTech

[Minich Analytics @MinichAnalytics]

Traditional inventory systems track stock.
We’re building one that understands it.
Knowledge graphs help us model:

> Supplier delays

> Promo campaigns

> Freight constraints

> Forecast shifts

This is real-time inventory intelligence.
#AI #RetailOps

[Minich Analytics @MinichAnalytics]

Imagine asking:
"Which products at risk from a supplier delay affect our top 10 stores next month?"
Spreadsheets can't answer that.
Graphs can.
We're using #KnowledgeGraphs + #GenAI to turn inventory data into decisions.
#RAG #AIinRetail

[Minich Analytics @MinichAnalytics]

I’m working on a project using knowledge graphs to fix stock imbalances.
Overstock in one region. Stockouts in another.
The issue? Disconnected data.
Now we link inventory, demand, logistics, and promos to recommend dynamic rebalancing.
#RetailTech #InventoryOptimization

[Minich Analytics @MinichAnalytics]

Inventory isn't just a spreadsheet problem. It's a graph problem.
Most retailers don’t know how a supplier delay in one region affects promos in another.
We’re using a knowledge graph to connect products, warehouses, suppliers & demand.
#GenAI #KnowledgeGraphs #RetailAI

[Minich Analytics @MinichAnalytics]

Navigating the AI Ethics Landscape: A Call for Responsibility
https://t.co/yLR6HMazzS

[Minich Analytics @MinichAnalytics]

The Philosopher's Stone for Data Scientists: The Quest for Ultimate Insight. https://t.co/SjlVk9l02P

[Comet @Cometml]

Curious about what's actually happening an LLM's attention mechanism?

Check out our latest guide to attention visualization with BertViz!

[Minich Analytics @MinichAnalytics]

Check out my latest video on developing a Flask web app and integrating with Mailtrap for personalized email sending. Also, learn how to store data and predictions in MongoDB. #Flask #Mailtrap #MongoDB
https://t.co/f1PFlKuOl6

[Minich Analytics @MinichAnalytics]

Mastering ML Deployment

Myths around ML deployment can mislead your strategy. Embrace continuous updates, scalability, and the power of multiple models to drive success. #MachineLearning #AI #TechTruths

[Minich Analytics @MinichAnalytics]

Debunking ML Deployment Myths

From deploying multiple models to frequent updates and scalability concerns, understanding the realities of ML deployment is key to success. Stay informed and keep your models performing optimally. #MLMyths #AI #DataScience

[Minich Analytics @MinichAnalytics]

Importance of Scaling in ML

Thinking scalability isn't a concern for ML engineers is a myth. Efficiently managing large datasets and real-time predictions requires robust scaling strategies. #MachineLearning #BigData #TechMyths

[Minich Analytics @MinichAnalytics]

Myth: Most ML Engineers Don’t Need to Worry About Scale

Scalability is crucial for ML engineers. Handling large datasets and ensuring performance at scale are essential for successful model deployment. #Scalability #MLDeployment #AI

[Minich Analytics @MinichAnalytics]

Do you remember when you joined X? I do! #MyXAnniversary

[Minich Analytics @MinichAnalytics]

The Need for Frequent Model Updates

Contrary to the myth, ML models require regular updates to stay effective. New data and changing objectives demand continuous retraining and adjustments. #MachineLearning #ModelMaintenance #TechMyths

[Minich Analytics @MinichAnalytics]

Myth: You Won’t Need to Update Your Models as Much

In reality, frequent model updates are necessary to keep up with new data and evolving business needs. Regular retraining ensures relevance and accuracy. #MLUpdates #AI #DataScience

[Weights & Biases @wandb]

If you cannot rely on your users to create reliable prompts, make sure you know how to make good system messages. System prompts simplify prompt engineering, automate context setting, and improve output clarity. Understand how to: https://t.co/AC0F8QdDbs

[Minich Analytics @MinichAnalytics]

Reality Check: Model Performance Over Time

Assuming model performance remains constant is a myth. Regular updates and retraining are essential to combat model drift and ensure continued effectiveness. #MachineLearning #ModelUpdate #TechMyths

[Minich Analytics @MinichAnalytics]

Myth: If We Don’t Do Anything, Model Performance Remains the Same
ML models degrade over time due to changing data distributions. Continuous monitoring and updating are crucial to maintain accuracy and reliability. #ModelDrift #MLMaintenance #AI