Agile Machine Learning
Machine Learning Research
Agile Project Methodology
- Client Collaboration
- Business Goals
- Incremental Steps
- Iterative Process
- Adaptive Approach
- Cloud Project Tools
Development Phases
- Requirements
- Use Case
- Data
- Features
- Inference/Prediction
- MLOps
- Standardize
- Automate
- Data Repository
- Software Repository
- ML Artifact Repository
- Model Registry
- Continuous Integration
- Build, Train, Test, Tune
- Deploy, Monitor, Measure, Optimize
- Data Pipeline
- Identify, Acquire, Persist
- Analyze, Filter, Format, Convert, Transform
- Scale, Normalize, Standardize, Validate
- Analytics
- Feature Engineering
- Datasets
- Modeling
- Statistical Analysis
- Regression Analysis
- Forecasting
- Machine Learning
- Supervised
- Unsupervised
- Deep Learning
- Artificial Neural Networks
- Reinforced Learning
- Deployment
- Infrastructure
- Inference Endpoint
- Containerization
- Local
- Cloud
- Framework
- TensorFlow
- PyTorch
- scikit-learn
- Platforms
- AWS
- Azure
- GCP
- Infrastructure
- Integration
- Endpoint Service
- REST API
- Web Service
- Micro Service
- User Interface
More on Machine Learning (2460)
Top weighted terms correlating to ‘machine learning’. (2460)
Terms correlating to ‘machine learning’ exceed 100 (2460).
Results limited to top 100 weighted terms.
Results limited to top 100 weighted terms.
While proponents of custom AI solutions extol their ability to tailor-made solutions that maximize ROI, I'd argue that in 2024's retail landscape, ready-made AI might be the better choice due to its scalability and cost-efficiency benefits. Ready-made AI's standardized models can be applied across various business sectors, whereas custom AI solutions often require significant upfront investment and may not always produce optimal results for every individual business. NewsSnoop.com
Published 2021-11-04 02:39.00
Updated 2024-05-17 07:48:25