Gal Batz, Chief Architect Cloud & Software
Companies in a variety of industries use artificial intelligence and machine learning to solve complex business problems and improve their products. Today, with the rise of cloud AI/ML services and open source tools, we can develop and use AI/ML capabilities and solutions, based on proven algorithms and models, allowing us to create insights and forecasts, classify data, detect fraud, and more.
Cloud providers divide their AI/ML services into two categories:
Specific task services are designed as standalone applications or APIs on top of pre-trained models. Each cloud provider offers a range of services that enable developers to add intelligence to their applications without developing or training their machine learning models. These services include:
Specialized AI/ML services are designed to handle a limited set of tasks. To deploy and train our custom models, we need general-purpose machine learning (ML) services. For example, Amazon SageMaker, Google Cloud ML Engine, and Azure Machine Learning Workbench are general-purpose ML services.
MLaaS. Cloud providers offer Machine Learning as a Service solutions that don't require deep knowledge of AI, machine learning theory, or data science skills.
Cost. Cloud-based workloads using artificial intelligence or machine learning can benefit from the pay-per-use model.
Elasticity and Scalability. With cloud services, you can easily experiment with machine learning capabilities and scale up when the project goes into production, and demand increases.
Competitive advantage. With AI/ML cloud services, your organization can grow and rise to new levels with relative ease.
Vendor lock-in. Some companies worry that if they start using too many services from one vendor, it will be hard to switch. As a result, they may be vulnerable to price increases from cloud service providers.
Integrating data. A lot of machine learning projects rely on data that come from many different places. Collecting that data and transforming it so that it is used can be a difficult task, whether you are using a cloud machine learning platform or another type of ML solution.
Connectivity. AI/ML in cloud-based resources requires constant internet connectivity. Poor internet connectivity can hinder the benefits of cloud-based machine learning resources.
Find out more about empowering your product with top-of-the-line AI/ML solutions.