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@zestrakesh
#Batch Processing In Machine Learning
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LILet’s see if I can cover the ML pipeline in 60 seconds ⏰😅
Machine learning isn’t just training a model. A production ML lifecycle typically looks like this:
1️⃣ Define the problem & objective
2️⃣ Collect and (if needed) label data
3️⃣ Split into train / validation / test sets
4️⃣ Data preprocessing & feature engineering
5️⃣ Train the model (forward pass + backpropagation in deep learning)
6️⃣ Evaluate on held-out data to measure generalization
7️⃣ Hyperparameter tuning (learning rate, architecture, etc.)
8️⃣ Final testing before release
9️⃣ Deploy (batch inference or real-time serving behind an API)
🔟 Monitor for data drift, concept drift, latency, cost, and reliability
1️⃣1️⃣ Retrain when performance degrades
Training updates weights.
Evaluation measures performance.
Deployment serves predictions.
Monitoring keeps the system healthy.
It’s not linear. It’s a loop.
And once you move beyond a single experiment, that loop becomes a systems problem.
At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle.
Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍
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CHBatch reacter
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OPLabeling + Batch coding Machine.
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@opacko_machinery
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DA📍6 Pillar Machine Learning Algorithms (Episode 88 of 100): DM to download the Free PDF👇
1. Support Vector Machine (SVM):
SVM is a commonly applied supervised machine learning algorithm that searches hyperplane with maximal separation from each data class.
2. Naive Bayes (NB):
Naive Bayes, another supervised ML algorithm, is a probabilistic method based on Bayes’ law.
3. Logistic regression:
Logistic regression is a classification algorithm utilized for probability prediction of target class by logistic function.
4. K-Nearest Neighbors:
The K-Nearest Neighbors is a distance-based algorithm as it first finds all the closest points around
new unknown data point and calculates the distance between them to determine the class of new data points.
5. Decision Trees:
Decision tree, a supervised machine learning algorithm, is a tree-structured classifier that continuously divides the data based on specific parameters.
6. Random Forest:
The random forest comprises multiple decision trees and can provide more accurate predictions by combining all of them.
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USEnsemble Technique in Machine learning
@usmar_hyder
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PASeu modelo de Machine Learning fica muito bom na hora dos testes mas quando vai colocar pra rodar em produção a acurácia despenca? #inteligenciaartificial #machinelearning #backend #deeplearning #python
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STMIPS assembly is actually kinda intuitive. Assembly rlly isn't that bad once u know the basics
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This is not my content. All credits to the owner. Dm for credit or removal.
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@codewithbeng
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