- 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 is AI Cloud Computing?
Cloud computing delivers computing services across the internet or “the cloud.” The services available through cloud computing include servers, databases, software, networking, and analytics. An advantage of cloud computing is that an organization only pays for what is used, which helps lower costs.
How is AI Affecting Cloud Computing?
Using artificial intelligence (AI) with cloud computing gives more functionality to end-users. AI and cloud computing are being used on a daily basis when users ask Siri, Google, or Alexa for assistance with tasks such as starting a timer, adjusting the thermostat, or playing music. These examples are for personal use, the following are ways AI is affecting cloud computing in businesses:
Powering a Self-Managing Cloud
To aid in streamlining workloads and automate repetitive tasks, AI is being used in IT infrastructure. AI is becoming more experienced and will eventually have the ability to monitor, manage, and self-heal when an issue occurs. AI is used to automate basic operations, and with analytical capabilities, will eventually be utilized to design superior processes that are mostly independent.
Using Dynamic Cloud Services
Using AI in dynamic cloud services will change how an organization does business. A cloud-based retail center that can easily sell products and adjust the price automatically based on the competition, demand, and inventory would allow the organization to focus on other areas of business and keep the company’s pricing at optimum.
Improving Data Management
Businesses generate and collect large amounts of data, and with the help of AI tools, this data is more easily managed. AI is able to identify and catalog the data generated and stored in the cloud. AI tools are already helping with aspects of data processing in cloud computing. An example of this would be a financial organization needing to monitor thousands of transactions each day, which is made easier by AI.
What Are the Benefits of AI in Cloud Computing?
There are several areas where AI in cloud computing can benefit businesses. The following are benefits to businesses:
Lower Costs
When ML-based models first started out, the cost was higher than most small and medium sized businesses could afford. Additionally, the models ran on multiple GPUs in enterprise datacenters. Now, thanks to advances in virtualization with both public and private clouds, the cost to build, test, and deploy models has significantly decreased, making it easier for small-to-medium businesses to take advantage of AI and cloud computing.
Productivity
With a hybrid or public cloud being centrally managed, the IT team has the ability to focus on the non-repetitive tasks. Whereas before, AI-based algorithms needed significantly more time and effort from the admin when it came to managing the model.
Automation
Because AI is being inserted into the infrastructure of the cloud, everyday tasks are automated and streamlined.
Data Management
AI improves data management and when combined with cloud computing, it maximizes information security. This makes it possible to handle a large quantity of data automatically and efficiently. AI is also helpful with transferring data from on-premise to cloud environments.
What Are the Downsides to AI in Cloud Computing?
There are some concerns when it comes to AI in cloud computing. Those concerns are discussed here.
Data Privacy
When using AI in cloud computing there must be a privacy policy created to secure the data being stored/shared. Some of the data can include customer and vendor information that when tied to a person, is more valuable than anonymous data. When it comes to an individual's sensitive information it is imperative to have data protection and compliance policies in place.
Connectivity Concerns
To send data to the cloud, IT departments need the use of the internet. Poor internet access can cause problems and is a disadvantage of cloud-based machine learning algorithms. Even though processing data in the cloud is quicker than conventional computing, if there is a disruption in transmitting data to the cloud, this becomes a concern for those using the cloud service.
H2O AI Cloud
H2O AI Cloud is a fully managed cloud environment that takes care of infrastructure provisioning, scale, and software updates. It is quick, easy to get started, and is secure, using multi-layer lines of defense against cyber attacks. To learn more about the H2O AI Cloud, click here.
Conclusion
AI and cloud computing are changing the way business is done by helping companies streamline their work and manage data, making it more efficient and allowing the IT department to focus on other tasks.