Data science / ML / AI platform

We can define artificial intelligence (AI) as the machines that can mimic human intelligence to perform tasks and learn from them. These tasks require human capabilities like decision-making, visual perception, and speech recognition. You can read more about this in the related section of our in-depth AI guide.

AI platforms can be separated into three main layers for enabling businesses to deploy machine learning models from a broad range of frameworks, languages, platforms, and tools. These three layers are:

• Data and Integration
• Experimentation
• Operations and Deployment

You can read the related section of our AI platforms guide to learn more about these layers.

The evolution of AI platforms is highly connected to the future of AI. We are bullish about AI approaches becoming more accurate and effective due to the factors listed below. In addition, we expect AI platforms to further automate manual aspects of machine learning such as feature engineering by incorporating mature capabilities of auto ML software. Feel free to visit our AutoML vendor list, if you are interested.

• Advances in computing power: AI platforms will be able to handle more complicated machine learning models with advances in computing power. These advances include AI-powered chips, quantum computing, and intelligent GPUs.
• The growing amount of data: The amount of data available for businesses rapidly grows every day.
• Advances in algorithm design: With better algorithm designs, AI platforms will offer more accurate AI-powered solutions to improve business performance. To achieve this, research on Explainable AI, transfer learning, and reinforcement learning is still ongoing.
• Advances in tools that enable AI model development: With new technologies like automated machine learning (AutoML), AI platforms can create new machine learning models automatically and continuously improve their performance without human intervention.

To learn more about the future of AI, feel free to read our in-depth guide.

With the increasing number of citizen data scientists, and increasing data availability, accessibility and ease-of-use of advanced analytical resources become critical. AI platforms are valuable resources for democratizing building and maintenance of ML models (i.e. offering solutions for handling the end-to-end machine learning development cycle). Without these platforms, companies would need to spend a significant share of resources on developing and maintaining machine learning models.

These platforms can be implemented in any situation where machine learning is involved. Some common use cases are include:

• Marketing analytics
• Prescriptive Sales
• Intelligent Call Routing
• Data Management & Monitoring
• Fraud Detection

Feel free to visit our AI use cases/applications article for 100+ examples.

By using AI platforms, businesses can create machine learning models with ease. For example, these are some of the common machine learning approaches that businesses rely on while using AI platforms:

• Neural Networks: Neural networks are a set of algorithms and mathematical models that aim to mimic the human brain. It performs a particular task without using explicit instructions, relying on patterns and inferences. To create successful neural network models, businesses should identify what they want to do and decide if their available data is reliable enough.
• Transfer Learning: AI platforms can be used as a tool for transfer learning instead of creating a new model from scratch. When there is not enough data or time to train data, transfer learning enables businesses to benefit from a previously used AI model for a different task.
• Explainable AI: The advances of AI technologies also require creating understandable models. With Explainable AI, businesses can generate self-explanatory models that help them understand how their AI algorithms work and why they come up with particular results.
• Reinforcement Learning: Rather than traditional learning, reinforcement learning doesn’t look for patterns to make predictions. It makes subsequent decisions to maximize its reward, and it learns by experience. AI platforms can also benefit from this technology while creating new algorithms or models.

These machine learning techniques can be combined with symbolic (i.e. human-readable) approaches to solve problems in various domains:

• Natural Language Processing (NLP): This technology helps businesses to process and evaluate large volumes of data with natural language understanding, natural language generation, and speech recognition.
• Computer Vision: Businesses can automate specific tasks that require visual perception as humans do. Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world. Object recognition, motion estimation, and image restoration are a few examples of this technology.
• Cloud Systems: A robust cloud infrastructure provides improved scalability and access to resources for the implementation of complex AI and machine learning solutions. Considering large amounts of data, businesses need to combine both AI and cloud to make full use of their advantages.