- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
A
C
D
G
L
M
N
P
R
S
T
X
- Activation Function
- Confusion Matrix
- Convolutional Neural Networks
- Forward Propagation
- Generative Adversarial Network
- Gradient Descent
- Linear Regression
- Logistic Regression
- Machine Learning Algorithms
- Multilayer Perceptron
- Naive Bayes
- Neural Networking and Deep Learning
- RuleFit
- Stack Ensemble
- Word2Vec
- XGBoost
- Attention Mechanism
- BERT
- Binary Classification
- Classify Token ([CLS])
- Conversational Response Generation
- GLUE (General Language Understanding Evaluation)
- GPT (Generative Pre-Trained Transformers)
- Language Modeling
- Layer Normalization
- Mask Token ([MASK])
- Probability Distribution
- Probing Classifiers
- SQuAD (Stanford Question Answering Dataset)
- Self-attention
- Separate token ([SEP])
- Sequence-to-sequence Language Generation
- Sequential Text Spans
- Text Classification
- Text Generation
- Transformer Architecture
- WordPiece
- AUC-ROC
- Analytical Review
- Autoencoders
- Bias-Variance Tradeoff
- Decision Optimization
- Explanatory Variables
- Exponential Smoothing
- Level of Granularity
- Long Short-Term Memory
- Loss Function
- Model Management
- Precision and Recall
- Predictive Learning
- ROC Curve
- Recommendation system
- Stochastic Gradient Descent
- Target Leakage
- Target Variable
- Underwriting
What Does it Mean to Operationalize Artificial Intelligence (AI)?
Organizations operationalize artificial intelligence (AI) first by getting small experimental use cases into production (solving real business problems), then by applying the learnings to additional use cases or similar problems. As use cases scale, so do the learnings and data, which allows the organization to apply those learnings to other use cases.
Operationalizing AI is the final milestone for AI practitioners in getting their systems into production. When AI systems have been “deployed” to production or live environments, they are “operationalized.”
ModelOps
AI model operationalization (ModelOps) has two primary focuses: (1) governance and (2) lifecycle management of AI and decision models.
H2O.ai has developed a rich ecosystem of MLOps and model management capabilities to push models into production faster and keep them there. H2O AI Cloud offers complete capabilities to deploy, monitor, test, explain, challenge, and experiment with real-time models in production. H2O’s MLOps technology enables users to watch, in real-time, how predictions change as well as monitor alerts to flag risks.
Why is Operationalizing AI Important?
AI enhancements allow organizations to focus their human resources on driving customer value rather than the repetitive tasks that require low skill or knowledge. Human resources are then more freely available to use creativity, intuition, and environmental awareness where they are most critical to business operations.
Artificial intelligence also reduces the complexity of large data sets by identifying important trends and anomalies. AI uses past outcomes combined with human input to continue to learn.
AI has the best chance for success when human inputs are included as controls in the automations or processes.
The Four Core Capabilities of AI
AI can solve the unsolvable in business, DevOps, and IT operations due to four core capabilities:
Complete tasks without explicitly coded instructions
Receive reliable results for cases that were not explicitly included in the training set of example
Rapidly identify relevant information in vast bodies of data
Increase prediction quality based on continuous feedback
DevOps and IT
Operationalized AI helps DevOps and IT use data-driven decisions to improve many areas including:
Processes optimize
Trend recognition
Proactive issue prevention
Rapid problem resolution
How H2O.ai Helps Operationalize AI
H2O.ai helps operationalize AI through comprehensive automated machine learning (autoML) campabilities. These capabilities simplify AI lifecycle management and help control how AI is created and consumed. H2O.ai makes it easier and faster to use AI, while still maintaining expert levels of accuracy, speed and transparency.
The H2O AI Cloud has two deployment options, Hybrid and Fully Managed.