AI platforms enable companies to build, evaluate, deploy and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, make predictions, and complete tasks faster and more accurately than they could manually.
AI
plays a key role as a catalyst for a new era of technological advancement. PwC calculates that “AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined.”PwC estimates that of this amount, “$6.6 trillion will likely come from productivity improvements and $9.1 trillion from consumer spending.”Given the potential impact on the industry, the McKinsey Global Institute estimates that new technologies that leverage artificial intelligence will generate up to $3.7 trillion in value in the manufacturing sector alone by 2025.Artificial intelligence technology is quickly proving to be a key component of business analytics in companies across various industries. Major cloud infrastructure providers such as IBM, Amazon AWS, Microsoft Azure and Google Cloud have expanded the market by adding AI platforms to their offerings.
AI platforms offer a wide range of features that can help companies streamline operations, make data-driven decisions, successfully implement AI applications, and gain a competitive advantage. These software platforms support collaboration between data science and engineering teams and reduce costs by reducing unnecessary effort and automating routine tasks such as data duplication or extraction. Some AI platforms also offer advanced AI features such as Natural Language Processing (NLP)
and speech recognition.
However, choosing a platform can be a difficult process, as the wrong system can increase costs and potentially limit the use of other useful tools or technologies. In addition to price, there are many other factors to consider when evaluating the best AI platforms for your business. Understanding the available AI tools and their capabilities can help you make informed decisions when selecting a platform that meets your business goals.
What types of features do AI platforms offer? The
AI platforms facilitate a variety of tasks, from applying data management to better load balancing to accelerating the creation of machine learning models.Since AI success typically depends on an organization’s ability to deploy models quickly and at scale, it is important to look for relevantfeatures in an AI platform that supports the organization’s goals. These include, among others:
MLOps features
Orchestration Pipelines: A single, unified platform enables teams to use a common set of tools for data analysis, data science, and machine learning and supports a wide range of machine learning algorithms, including neural networks for complex predictive analytics. This unified environment streamlines the process of developing and deploying machine learning models by streamlining workflows to increase efficiency. AutoML
tools: Automated machine learning (autoML) enables you to build models faster with low-code and no-code capabilities. Decision Optimization
: Simplifies the selection and implementation of optimization models and enables the creation of dashboards to share results, improve collaboration, and recommend best courses of action. You can optimize trade-offs between business goals, such as: B. reduce customer service costs or improve customer satisfaction and determine the best course of action in each situation.
Visual Modeling: Combine visual data analysis with open source libraries and notebook-based interfaces in a unified study of data and AI. By examining data from different perspectives using visualizations, you can see patterns, connections, insights, and relationships in that data and quickly understand large amounts of information.
Automated Development: With AutoAI, beginners can get started quickly and advanced data scientists can accelerate their AI development experiences. AutoAI automates data preparation, model development, feature engineering and hyperparameter optimization.
Synthetic Data Generator: Synthetic data can be used as an alternative or supplement to real data when real data is not readily available, which can be particularly useful in experiments.The platform’s features can help you create a synthetic tabular dataset leveraging existing data or a custom data schema. You can connect to an existing database, load a data file, anonymize columns, and generate all the data needed to fill data gaps or train classic AI models.
possibilities for generative artificial intelligence Content Generator
: Generative AI refers to deep learning models that can generate text, images, and other content based on the data they are trained on. AI platforms can generate content and facilitate various tasks such as creating marketing emails and creating customer profiles.
Automatic classification: AI platforms can read and classify written input, for example by evaluating and sorting customer complaints or checking customer reviews.
Summary Generator: AI platforms can also convert dense text into a high-quality summary, capturing key points from financial reports, meeting minutes, and more.
Data Mining: The platform’s features help you sort through complex details and quickly extract the necessary information from large documents. This is done by identifying named entities, parsing conditions, etc.
Key benefits of the AI platform
AI platforms can help you harness the power of AI technology and provide your business with numerous benefits such as: B. more automation, scalability, security and more. These platforms enable companies to analyze massive amounts of data, gain valuable insights, and quickly adapt to changing market dynamics, driving innovation and competitive advantage.
More automation
automation plays a key role in accelerating the scale and pace of operations across the data lifecycle. Once a repeatable and successful process is identified, such as consistent data labeling, teams can look for ways to automate it using machine learning. In this case, leveraging the capabilities of an AI platform to automate data labeling would result in greater predictive accuracy and greater utility of variable data.
Greater scalability Scalability during the training and production phases of machine learning models is critical because there are limitations to building and training models on a local computer such as a laptop. This may be sufficient for smaller data sets, but data scientists will not be able to use this approach for more robust models. To grow, they need a centralized workflow that allows visibility and collaboration with other specialists to keep data up to date and monitor
computing power availability and GPU and CPU utilization.
Better integration
The AI platform is also intended to offer user-friendly integrations that make it easier to use open source software and libraries. Most platforms are already compatible with popular open source frameworks such as PyTorch, TensorFlow and Scikit-learn. However, for a complete AI ecosystem, you should look for an AI platform that offers easy and convenient access to open source platforms such as MongoDB, Redis and PostgreSQL. .
Additionally, the best AI platforms are developed and maintained by organizations and teams that are deeply engaged in the open source community. They contribute to research, provide resources and offer their expertise, enriching the diversity of expertise and research contributions while expanding the range of innovative technologies available to data science and machine learning professionals.
IBM was an early proponent of open source, supporting influential communities such as Linux, Apache, and Eclipse by pushing for open licensing, open governance, and open standards. IBM’s relationship with open source software became even clearer after its acquisition of Red Hat.
Furthermore, it is essential to think about the strategy for implementingand using an AI platform.Will it be deployed on-premises or hosted via a cloud platform? Should it be used by internal teams or should it be accessible to external customers? These factors are also important in determining which AI platform can be most effectively integrated to align with business goals.
More security
open source packages are commonly used by data scientists, application developers, and data engineers, but can pose a security risk to organizations. Security controls are essential to detect and protect against rapidly evolving threats.The best AI platforms typically have a variety of measures in place to protect data, application endpoints, and identities.
Key security measures include:
Network Security: Network security has three primary goals: preventing unauthorized access to network resources, detecting and stopping ongoing cyberattacks and security breaches, and ensuring that authorized users can securely access the network resources they need when needed.
Data Security: Data security protects digital information throughout its lifecycle from unauthorized access, damage or theft.
Collaborator Security: Collaborator Security protects your workspaces by assigning role-based access controls to employees.
Better management Artificial Intelligence Governance
aims to ensure the ethical, responsible and compliant development and implementation of artificial intelligence and machine learning modelswithin the organization. An AI platform with intelligent management capabilities enables better collaboration and coordination in model approval, tracking and compliance management.AI governance is critical to building trust in data-driven decisions made by organizations using information from these platforms. This trust extends to meeting both internal compliance requirements and external regulations.
Failure to manage AI can lead to consequences such as inefficiency, financial penalties, and significant damage to brand reputation. It can also make machine learning processes difficult to scale, making results difficult to reproduce and risking errors due to invalid or incomplete data. The fines can be significant: bank operators face seven-figure fines for distorted credit approval models and potential GDPR fines of up to 20 million euros or 4% of their annual turnover.
Additionally, ensuring appropriate user access is an important management element in an AI platform as it can prevent some roles from inadvertently making a mistake that affects the entire system. IT administrators must beable to assign accounts based on job roles, monitor user activity, and facilitate information sharing and collaboration among professionals.
Look for a platform that implements good governance practices to ensure data standardization, bias reduction, and compliance with industry regulations.
Technical Support
If you need help with training and education, reliable error reporting and tracking, troubleshooting, or emergency response, it’s a good idea to choose an AI platform that can provide you with the support you need.
A strong user community with support resources (e.g. forums, documentation, customer support) can also be valuable for problem solving and knowledge sharing.
best AI platform use cases
The use of artificial intelligence is crucial for companies to remain competitive and avoid the risk of falling behind. The following use cases show how companies have integrated AI into their industries.
Health Care The strengths of
AI can help address the myriad challenges facing healthcare—challenges that are only growing.
Addressing challenges in radiology
As the volume and complexity of patient data increases, the pressure on radiologists to work more efficiently and handle larger patient volumes increases. The shift to value-based care is making it increasingly difficult to obtain reimbursement, forcing organizations to look for ways to increase efficiency and productivity to meet their financial goals.As expected, these changes and increasing demands have led to increasing frustration and burnout among providers.
With powerful image and data analysis capabilities, AI can help radiologists:
Image capture
lectures and interpretations
Prioritize and organize searches
recommendations for appropriate health record outcomes in electronic health records (EHRs).
recommendations for appropriate outcomes from the literature or clinical guidelines
IBM Watsonx is a data and AI platform with a suite of AI assistants designed to help you scale and accelerate the impact of data-driven AI at every level of your business.
The core components of
include: a study for new fundamental models, generative artificial intelligence and machine learning; a custom data warehouse built on Lakehouse’s open data architecture; and a suite of tools to accelerate AI-powered workflows designed for accountability, transparency and explainability.
Watsonx
AI assistants enable people across your organization to perform tasks without knowledge of a variety of business processes and applications, including customer service automation, code generation, and workflow automation in departments such as human resources.