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Artificial intelligence (AI) simulates human intelligence by using data to make informed decisions and solve problems.
AI relies on machine learning to gather and adapt to new data, making it possible for software to learn independently without much human oversight.
Today’s developers typically build AI platforms and AI software to perform specific tasks.
Some common applications of the technology include:
The broad range of uses has encouraged more businesses to use artificial intelligence and machine learning.
Below, you will find some of the most popular AI platforms and tools to consider for your team.
Google Cloud Vertex AI is a unified artificial intelligence platform Google uses to power its search results. It offers excellent flexibility to meet diverse needs.
It can help you build advanced ML models while avoiding about 80% of the coding required by other platforms.
Being a Google based program, you will have access to all Google’s top notch AI technologies like Tensorflow, TPUs, etc., and Google’s open-source platform Kubeflow with which you can create portable machine learning pipelines.
Vertex AI also comes with pre-trained APIs that meet the needs of organizations that don’t employ data scientists.
Some top features of Vertex AI include:
You can use Vertex AI for practically any project. It has such a large suite of features, though, that you might feel intimidated by it.
Though, the platform isn’t ease to master. If you want a broad AI platform with plenty of machine learning models, Vertex AI will probably meet your needs.
If you want to perform a specific task, other software tools might work better for you.
Nvidia Deep Learning gives users access to powerful processors without investing in expensive hardware. Nvidia is at the forefront of GPU development, so it can accelerate AI, ML, and deep learning projects that lead to breakthrough discoveries.
Importantly, Nvidia Deep Learning does not create AI models. Instead, it provides access to the hardware needed to process big data quickly and train neural networks.
You can use it with any major framework, including:
H20.ai is an open-source AI platform available on-premises and in the cloud. It scales quickly to process information in real time, but you can also set the platform to process batch data.
Many data scientists and engineers like H20.ai because it can work with a broad range of data pipeline tools, including Snowflake, Apache Spark, and H20 Sparkling Water.
It is used for; banking, telecom, insurance, healthcare etc.
Programming languages such as R and Python are used in this platform to build models. H20 it helps in simplifying and accelerate making, operating and innovating applications with AI in any environment.
The platform is easy to use, it’s scalable and it follows a distributed in-memory structure.
Existing clients use H20.ai for projects involving:
H20.ai works with several popular languages, including R, Spark, Python, and Scala.
The idea behind IBM Watson software is to help making business processes smarter. It is a question-answering system which can learn from small data.
It is a robust system which supports distributed computing and it provides an API for application development.
IBM Watson is probably the most popular AI platform for businesses and researchers. IBM can give you access to Watson technology to solve issues in:
If you want an AI solution designed for your business, Watson can almost certainly help.
Azure Machine Learning studio stands out for its ability to conform to the user’s level of experience. If you have data scientists on your team, they can use their expertise to build sophisticated machine learning algorithms, train, and deploy ML models faster than other AI software.
No programming skill are needed to use Azure ML. The platform has a drag-and-drop interface that lets casual users build simple AI tools for automating workflows and improving customer segmentation. Deployment of the models in cloud is possible from the application itself.
This software is scalable and it can be integrated with open source technologies.
Other features that make Azure Machine Learning studio appealing include:
SalesIQ software helps with sales, marketing, and customer services. It’s especially useful for businesses that rely on e-commerce sales and customer services.
SalesIQ also has features for:
Cortana is a virtual personal assistant that connects to Microsoft 365 to perform tasks like:
Cortana’s voice recognition makes it a helpful tool for busy people. You don’t have to sit down at your keyboard to perform a search online.
Instead, you can ask Cortana to find answers for you.
Other personal assistants to consider include:
As the name suggests, Deep Vision focuses on training AI applications to recognize visual objects in the real world. Cities and businesses often use the technology for security tasks like:
You can also use it to enhance your brand through:
For example, you could use Deep Vision to recognize your brand’s logo in a video.
The AI software can review large video files to locate your logo. It doesn’t need a 100% match. Instead, it can make educated guesses about images that might contain your logo and products.
It can even give you a percentage of certainty that helps you pinpoint segments that interest you.
Salesforce Einstein is a suite of AI technologies that integrate with your Salesforce CRM software. By taking advantage of the cloud-based AI, you should find that you can:
You can use some features of Salesforce Einstein on an iOS or Android smartphone, but a desktop computer will give you access to even more AI tools.
TensorFlow is an open source library you can use to train your ML models. TensorFlow doesn’t expect every user to have years of experience working in data science and ML training.
It provides tutorials and education pages that target beginners and experts. Beginners will start with projects like “Your first neural network” while experts can learn more about “Generative adversarial networks.”
TensorFlow has collaborated with NVIDIA to improve deep learning with help from incredibly fast GPUs (graphics processing units). You can take advantage of NVIDIA GPU Cloud (NGC) or purchase machines for on-premises applications.
AI software engineers need highly specialized skills to do their jobs well. The specific skills vary depending on the types of AI they work with.
Ideally, they should have experience with languages like:
It also helps to have experience working with common AI models, including:
Artificial intelligence and machine learning rely on successful algorithms to do their jobs well. Deep learning might require accessing and processing unstructured data.
Engineers can help the process by choosing a reliable framework for the job.
Since artificial intelligence takes advantage of big data, data scientists working as AI engineers should know how to manage data sets with systems like:
You have plenty of AI tools to consider before committing to an option. We’re available to talk about everything from real-time machine learning platforms to natural language processing and help you choose the right AI approach and tools that match your project needs.
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