Agile Machine Learning

Project Methodology

Machine Learning projects are a combination of multiple domains and disciplines. Data Science, Software Engineering, Operations Management, and Information Technology are the primary examples. Because of the high levels of complexity, specialized project methodologies are required.

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
Integration
- Endpoint Service
- REST API
- Web Service
- Micro Service
- User Interface

More on Machine Learning (2460)

Top weighted terms correlating to ‘machine learning’. (2460)

Term	Wt.	Token
machine learning	147	6.21.
applications of artificial intelligence	91	6.4.
artificial intelligence in finance	91	6.4.8.
ai applications	91	6.40.
quantum machine learning	64	6.21.37.
glossary of artificial intelligence	60	6.18.
machine learning in earth sciences	59	6.21.33.2.
general adversarial network	54	6.15.2.22.
adversarial machine learning	54	6.21.2.
machine learning in bioinformatics	54	6.21.26.
list of datasets for machine learning research	53	1.10.4.1.53.
artificial intelligence in healthcare	39	6.7.
distbelief	34	6.13.6.5.1.
tensorflow	34	6.21.36.2.
thomas g. dietterich	33	1.10.4.1.87.
explainable artificial intelligence	32	6.5.13.1.3.
interpretable artificial intelligence	31	6.5.13.1.2.1.
explainable ai	30	6.129.
ai control problem	28	6.37.
ml.net	27	6.21.109.
learning classifier system	27	6.21.21.
algorithmic bias	27	6.21.3.
machine learning in physics	24	6.21.27.
bayesian model averaging	23	1.10.1.22.
ensemble methods	23	1.9.2.21.
ensembles of classifiers	23	6.18.50.
ensemble learning	23	6.21.11.
stacked generalization	23	6.21.33.41.
knowledge mining	22	2.7.1.12.46.
federated learning	22	6.1.7.1.11.
information mining	22	6.13.111.
web usage mining	22	6.21.73.
data mining	22	6.21.9.5.
web mining	22	6.21.9.5.33.
knowledge discovery in databases	22	6.22.8.1.
list of machine learning algorithms	21	1.2.5.594.
list of machine learning concepts	21	6.13.2.68.
outline of machine learning	21	6.21.33.
isabelle guyon	21	6.21.42.1.
machine learning algorithms	21	6.8.73.
artificial neural networks	20	1.10.2.57.
neural nets	20	2.7.1.53.
artificial neural network	20	6.13.2.
neural computing	20	6.13.2.12.17.
stochastic neural network	20	6.13.2.22.1.
ai software	20	6.13.6.1.1.
neural network model	20	6.14.3.9.
neural net	20	6.17.53.
neural network models	20	6.26.7.1.
cognitive system	20	6.5.13.23.
artificially intelligent	20	6.6.2.16.
receiver operating characteristic	19	1.10.2.3.28.
andrew ng	19	1.10.4.2.137.
roc curve	19	1.10.4.2.245.
machine thought	19	2.7.1.17.1.83.
artificial intelligence	19	6.
deep learning	19	6.13.
ai research	19	6.13.12.12.
michael i. jordan	19	6.13.17.17.
deep neural network	19	6.13.29.
deep neural networks	19	6.13.9.1.1.
ryszard s. michalski	19	6.18.20.
kernel embedding of distributions	19	6.21.33.264.
applications of deep learning	19	6.4.127.
ai safety	19	6.5.13.1.
tensor machine learning	18	6.21.43.
geoff webb	17	1.10.4.1.88.
deepmind	17	2.11.2.1.107.
reward function	17	6.1.5.5.
generative adversarial networks	17	6.1.6.5.
deepmind technologies	17	6.13.103.
reinforcement learning	17	6.13.2.21.
google deepmind	17	6.13.6.
generative adversarial network	17	6.21.16.
vasant honavar	17	6.21.33.92.
inverse reinforcement learning	17	6.5.13.8.2.
sub symbolic	16	6.124.
australian institute for machine learning	16	6.13.12.1.38.
symbolic artificial intelligence	16	6.13.15.
subsymbolic	16	6.14.6.3.
data stream mining	16	6.18.82.
variable selection	16	6.21.26.14.
feature selection	16	6.21.6.2.
autoai	16	6.21.6.5.
symbolic ai	16	6.45.
computational economics	15	1.6.18.
reward modeling	15	6.13.12.1.16.
convolutional neural network	15	6.13.2.6.
stochastic gradient descent	15	6.21.30.1.
adagrad	15	6.21.31.6.
ai alignment	15	6.5.13.1.1.
control problem	15	6.5.13.1.1.1.24.
reward hacking	15	6.5.13.1.1.1.5.
phi coefficient	14	1.2.7.34.
multi task learning	14	1.8.384.
google cloud platform	14	4.4.1.2.
support vector machines	14	5.4.1.45.
max pooling	14	6.13.2.6.30.
convolutional neural networks	14	6.13.20.
hyperparameter optimization	14	6.21.19.1.
matthews correlation coefficient	14	6.21.33.295.

Terms correlating to ‘machine learning’ exceed 100 (2460).
Results limited to top 100 weighted terms.

Originally published on 2021-11-04 02:39.00

Last update on 2023-09-21 17:50:56