What is an AI platform?

Ar­ti­fi­cial in­tel­li­gence platforms make it possible to develop and optimise 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 cent­ral­ise 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 cus­tom­is­able. 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 cus­tom­ised 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 cus­tom­is­able 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 optimise the use and main­ten­ance of AI models. They use, for example, automated machine learning, visual modelling, 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 analysed 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 cent­ral­ised 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 optimise 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 in­di­vidu­al­ised treatment.
• Pro­duc­tion: AI tech­no­logy is used in man­u­fac­tur­ing to optimise (supply chain man­age­ment) and improve quality control.