What is an AI platform?

Ar­ti­fi­cial in­tel­li­gence platforms make it possible to develop and optimize models for machine learning (ML). Important features of AI platforms include scal­a­bil­i­ty, au­toma­tion, MLOps and gen­er­a­tive AI. They support users in making data-based decisions, stream­lin­ing processes and ef­fec­tive­ly using AI tools.

What is an ar­ti­fi­cial in­tel­li­gence platform?

An ar­ti­fi­cial in­tel­li­gence platform (or AI platform) is an in­te­grat­ed set of tech­nolo­gies for de­vel­op­ing, training and im­ple­ment­ing machine learning and deep learning models. AI platforms provide tools and in­fra­struc­ture for de­vel­op­ing and main­tain­ing complex AI ap­pli­ca­tions. They can help cen­tral­ize data analysis, make de­vel­op­ment and pro­duc­tion processes more efficient and improve col­lab­o­ra­tion between de­part­ments. That in turn allows de­vel­op­ment teams and companies to implement AI-based solutions with lower costs and fewer resources.

What are the different kinds of AI platforms?

Companies have three main options for using an ar­ti­fi­cial in­tel­li­gence platform, each with its own ad­van­tages. While pre-con­fig­ured AI platforms allow you to get to work quickly, self-developed or user-defined solutions are much more cus­tomiz­able. Al­ter­na­tive­ly, open-source AI platforms provide a flexible foun­da­tion for beginners and more complex projects alike.

Pre-con­fig­ured AI platforms

Pre-con­fig­ured AI platforms are perfect for companies looking for a quick and easy way to implement AI apps, models and al­go­rithms. They offer a wide range of ready-to-use tools, APIs and pre-tested al­go­rithms. Sometimes they also include pre-trained models for specific use cases, which you can integrate seam­less­ly into your existing workflows.

User-defined AI platforms

De­vel­op­ing your own AI platform could be the right option for you if you have specific re­quire­ments, such as strict data pro­tec­tion rules or special use cases. User-defined AI platforms are cus­tomized from start to finish, so they can meet your in­di­vid­ual needs. For example, Uber developed its own AI platform that uses natural language pro­cess­ing (NLP) and machine vision to improve its GPS system and crash detection features.

Building a custom platform takes more time and resources, because main­te­nance, support and ad­min­is­tra­tion need to be done entirely in house. In return, you’ll benefit from maximal control and flex­i­bil­i­ty.

Open-source AI platforms

Open-source solutions like Ten­sor­Flow and PyTorch offer an af­ford­able way to benefit from AI. In fact, they are often free. Active com­mu­ni­ties ensure that open-source platforms are being con­stant­ly developed, es­pe­cial­ly in the case of popular tools and frame­works. Open-source platforms are a par­tic­u­lar­ly good option for companies looking for a flexible and cus­tomiz­able solution.

What is the purpose of AI platforms?

AI platforms provide support with a variety of tasks, from data pro­cess­ing and analysis to workload dis­tri­b­u­tion to de­vel­op­ing machine learning models. Their most important features fall into two cat­e­gories, MLOps and gen­er­a­tive AI:

• MLOps: Machine learning op­er­a­tions (MLOps) aim to optimize the use and main­te­nance of AI models. They use, for example, automated machine learning, visual modeling, dash­boards for pre­sent­ing results and automated de­vel­op­ment (AutoAI). They also enable you to generate synthetic data for training AI models.

• Gen­er­a­tive AI: Gen­er­a­tive ar­ti­fi­cial in­tel­li­gence is based on training with large data datasets (Big Data), which are analyzed by neural networks and deep-learning models. It is used for text and image gen­er­a­tion, data expansion and ex­trac­tion, clas­si­fi­ca­tion au­toma­tion, as well as in dialog-based AI such as chat bots.

Other features of AI platforms include:

• Au­toma­tion: Machine learning makes it possible to automate processes, which speeds up workflows.
• Scal­a­bil­i­ty: AI models can be trained and used in a wide variety of en­vi­ron­ments, thanks to cen­tral­ized workflows.
• Seamless in­te­gra­tion: Modern AI platforms support common languages and frame­works, and can be in­te­grat­ed into open-source software and your entire tech stack.
• Increased security: AI platforms have various security measures in place to ensure that data, iden­ti­ties and ap­pli­ca­tion endpoints are ad­e­quate­ly protected.
• Improved gov­er­nance: AI systems enable the central control of data, models and processes, which makes it easier to ef­fi­cient­ly uphold security, com­pli­ance and quality standards.
• Technical support: Many pre­con­fig­ured AI platforms come with com­pre­hen­sive support, including on­board­ing and training resources and help with problems. If you choose an open-source tool, look for one that provides support for AI features and ar­chi­tec­tures.

What are the use cases for AI platforms?

An in­creas­ing number of companies are turning to AI platforms to stay com­pet­i­tive or create com­pet­i­tive ad­van­tages. Product de­vel­op­ment and services are two of the most common areas AI platforms are used in. Specific use cases include:

• Financial services: AI models are used by credit in­sti­tu­tions to automate credit checks, prevent money laun­der­ing and detect fraud in real time. AI is also used in re­ceiv­ables pro­cess­ing.
• E-commerce: Online stores use AI platforms to show customers tailored product sug­ges­tions and to optimize pricing and the purchase of goods.
• Health­care: AI is trans­form­ing medicine, enabling faster diagnoses and increased access to patient services. That enables medical pro­fes­sion­als to make more precise diagnoses and offer more in­di­vid­u­al­ized treatment.
• Pro­duc­tion: AI tech­nol­o­gy is used in man­u­fac­tur­ing to optimize (supply chain man­age­ment) and improve quality control.