After the launch of OpenAI’s ChatGPT, generative AI technology started to grow rapidly across industries. Generative AI tools work by using deep learning techniques to create new data instances similar to the input data, giving human creativity to AI. The technology has paved the way for diverse applications in various industries and areas (Figure 1). Such as:
But to successfully leverage this technology, businesses often seek the support of dedicated services like AI data collection services or hardware vendors. Here, we explore 7 types of generative AI services that are instrumental in enhancing businesses’ use of generative AI technology to gain a competitive advantage.
Figure 1. Gen AI adoption in the U.S. by industry in 2022
Table 1. A summary of all the service categories and service providers:
|Service Provider Examples
|Generative AI strategy & use case identification services
– McKinsey – Bain & Company – Accenture – Boston Consulting Group (BCG)
|Generative AI training data collection services
|Generative AI hardware solution providers
– Google’s Tensor Processing Un its (TPUs)
|Generative AI foundation model providers
|Generative AI training and development services
– Microsoft Azure
– AWS SageMaker
|Generative AI applications
|List of top vendors on the market
|Reinforcement Learning with Human Feedback (RLHF)
How did we select the service provider for this article
- The Vendors mentioned in this article are only examples for the service categories.
- We selected the top players for each service category based on platforms such as G2.
- We also evaluated the vendors based on publicly available and verifiable data
- We narrowed down the vendors based on the relevance of their services.
1. Generative AI strategy & use case identification services
A sound strategy is essential for any business planning to integrate generative AI into its business processes. This can be challenging because it requires a deep understanding of both AI technologies and the specific business context, including:
- Operational needs
- The skills of the existing workforce
- Ethical considerations
- The potential impacts and risks of AI deployment.
Strategy services help develop this roadmap, and top players include:
McKinsey aids businesses in harnessing the potential of generative AI by designing bespoke algorithms that create innovative solutions, such as unique content generation, automated design, and product development. It also provides strategic guidance on the implementation and scaling of these AI tools, fostering operational efficiency and competitive advantage.
Bain & company
Bain & Company assists businesses in utilziing generative AI by developing customized strategies that integrate AI-driven creative and predictive capabilities into their existing workflows. Additionally, they provide advisory services that ensure the ethical, effective and scalable deployment of these AI technologies, thereby stimulating innovation and growth.
Accenture’s AI strategy services help businesses identify and implement AI use cases, including generative AI applications.
Boston Consulting Group (BCG)
BCG’s Gamma team combines strategic thinking with powerful AI tools to help companies develop a winning generative AI strategy.
2. Generative AI training data collection services
Generative AI models require large amounts of data to be trained. Software developers can work with data collection services to fulfill their data needs without facing the hassle of collecting data. These services focus on data collection, preprocessing, annotation, and other services involved in preparing a training dataset for generative AI models.
Clickworker offers human-generated datasets for training generative AI models through a crowdsourcing platform. Its global team of over 4.5 million data collectors helps 4 out of 5 tech giants in the U.S. with their data needs. They can offer:
- Image datasets to train image generation models like Dall E
- Text or spoken audio datasets to train natural language processing or a Large language model
- Video datasets for video generation tools
To learn more about AI data collection.
3. Generative AI hardware solutions providers
Generative AI systems often require high-performance computing capabilities to efficiently process and learn from massive amounts of data, necessitating specialized hardware such as GPUs or TPUs. Working with a third-party service provider can help you achieve such computational capabilities. These services provide specialized hardware to help train and run generative AI models more efficiently:
NVIDIA is a leading name in AI hardware solutions, providing powerful GPUs that are crucial for training generative AI models due to their parallel processing capabilities.
Google’s Tensor Processing Units (TPUs)
Designed specifically for neural network machine learning, Google’s TPUs offer high-performance capabilities for training and deploying generative AI models.
4. Generative AI foundation model providers
AI foundation models are the backbone of any generative AI system. These are complex models trained on extensive datasets and can generate outputs in a range of tasks without task-specific training data.
Two notable services in this area include:
Renowned for its GPT-3 and GPT-4 large language models, and Dall E image generation model, OpenAI has delivered unprecedented advances in generative AI. GPT-n models are capable of creating human-like text, making it a top choice for businesses seeking foundation models for their AI applications.
Google’s BERT model has also revolutionized the way we process languages in AI, making significant strides in search optimization, sentence prediction, and other text-processing tasks. This robust and versatile model is a stalwart for any company seeking to utilize generative AI in language-related tasks.
5. Generative AI training and development services
Training a generative AI model is a challenging process that requires specialized skills because it involves:
- Understanding complex algorithms
- Optimizing neural network architectures
- Handling large datasets
- Fine-tuning models to generate high-quality outputs while avoiding pitfalls such as overfitting or mode collapse.
Third-party service providers can help streamline the whole development process.
Such services include:
H2O.ai offers an automatic machine learning platform that helps build AI models to improve business operations, including generative AI, without necessarily having an extensive background in AI.
DataRobot provides an enterprise AI platform that enables users to prepare data, build, train, and deploy machine learning models, including generative models.
Azure’s Machine Learning service provides a suite of tools to build, train, and deploy machine learning models, including support for generative AI.
Amazon’s SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning models, including generative AI models.
6. Reinforcement Learning with Human Feedback (RLHF) service providers
RLHF is an approach to machine learning that combines traditional reinforcement learning methods with human feedback. Usually, companies with a large network of contributors, like crowdsourcing platform/service providers, offer RLHF services. The services train AI models using a blend of algorithmic learning and human feedback, which refines model behavior and ensures alignment with human values.
Some of the service providers in this category include:
Clickworker offers RLHF services through its crowdsourcing platform with contributors of more than 4.5 million.
Prolific offers AI/ML training and evaluation services through its network of contributors. Its service pool also includes RLHF services through its network of 120K+ contributors.
7. Generative AI applications
Companies can take advantage of pre-existing generative AI applications on the market, saving the need to build custom solutions from scratch. These ready-to-use options can provide a robust, streamlined approach to harnessing the power of AI for business needs.
Here is our data-driven hub of generative AI applications, with vendor lists to compare your options and find the right fit.
- Generative AI Data: Importance & 7 Methods
- 5 AI Training Steps & Best Practices
- Enterprise Generative AI: 10+ Use Cases & LLM Best Practices
If you need help finding a vendor or have any questions, feel free to contact us:
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