You've invested time and money into developing a cutting-edge product or service to help your business stay competitive. But, to your surprise, just as your offering is ready to launch, a new generative AI tool hits the market, promising to do it better, faster, and with less human involvement. Scenarios like this are becoming too common as generative AI advances at lightning speed. So how do you respond? One of the best ways to stay ahead of the curve is to integrate generative AI into your tech stack to boost innovation, efficiency, and scalability. This blog will help you identify the right generative AI development services for your business, so you can quickly make the most of this technology before your competitors beat you to it.
Lamatic’s Generative AI tech stack offers a robust solution to help you achieve your goals. For example, it seamlessly integrates the most effective generative AI development services into your tech stack to drive innovation, scalability, and efficiency, keeping your business competitive and future-proofed.
What is a Generative AI Tech Stack?
Generative AI refers to the various AI techniques, tools, and models designed to generate entirely new content based on input. It can understand and create:
- Text
- Images
- Video
- Audio
- Other modalities
While it’s a subset of Artificial Intelligence (AI), what sets Gen AI apart is the Foundation Model (FM). FMs are Machine Learning (ML) models pre-trained on vast amounts of data across modalities. Foundation Models are generalized. Unlike earlier forms of AI, FMs are not trained for specific tasks and can be adapted to solve a wide range of problems.
FMs are based on a deep-learning neural network architecture called Transformers. Transformer models can absorb large amounts of text content and understand the relevance and context of every word in a sentence, paragraph, or article. They can also understand relationships between words.
Foundation Models are built in two steps:
Step 1: Pre-trained on vast amounts of raw, generalized, and unlabeled data across modalities such as:
- Text
- Images
- Audio
- Video
- Etc
This training is largely unsupervised. FMs have a large number of tunable parameters, which allows them to comprehend complex topics.
Step 2: Fine-tuned to specific tasks like:
- Question/Answer
- Summarization
- Sentiment Analysis, etc
FMs are generalized and can be adapted to solve various tasks. Today's most mature FMs are text-focused mainly because of the mountain of textual content already available for training. This has accelerated the development of a specific type of FM for language-specific tasks: Large Language Models (LLM).
What is a Tech Stack?
A tech stack, or technology stack, combines software, tools, and technologies used to build and run applications. It has two layers: the front end, or client side, and the back end, or server side. The front end consists of the visible features of an application that users interact with directly. The back end includes the underlying processes that occur out of sight to ensure the application runs smoothly.
What is in a Generative AI Tech Stack?
Generative AI is not just another application you build. It brings along an entirely new tech stack. While foundation models are at the heart of the stack, several new layers are added that enterprises need to build, buy, or just be aware of, depending on what they are trying to achieve. The tech stack comprises several new tools, technologies, and techniques organized into distinct layers.
1. Compute
At the bottom of the stack are the compute hardware chips required for model training and inference. In recent years, raw computing power from Graphics Processing Units (GPUs) has increased, with processing efficiency doubling every 18 months.
Nvidia is the leader in GPUs, but AMD and Intel also release GPUs and associated developer tools. Google introduced Tensor Processing Unit (TPU) chips designed to handle large-scale ML deployments. Startup SambaNova has built proprietary hardware, including processor and memory chips, and software designed to run large LLMs.
Companies that decide to build their own Foundation Models or fine-tune existing FMs for their domain or use case will need to work with AI hardware accelerator firms that combine hardware and software for a lower total cost of ownership. All other companies will leverage existing FMs hosted by the cloud providers and do not have to deal with this layer of the stack.
2. Cloud Platforms
Behind the scenes, infrastructure vendors play a pivotal role in Gen AI solutions. This includes cloud hyperscalers (Amazon AWS, Microsoft Azure, Google GCP) that provide the storage and computational resources required to analyze massive amounts of data and models, full-stack tools, and services for building Gen AI applications. The offerings from the cloud providers are very similar, but differentiation will come over time.
3. Foundation Models
Rather than building a Foundation Model from scratch, most enterprises will choose an existing FM that suits their needs. There are open-source options such as Falcon and Llama 2 as well as commercial, closed-source models available as APIs such as:
- OpenAI
- A121labs
- ANTROP\C
Several factors are involved in selecting FMs, such as the number of tunable parameters, context window size, output quality, inference speed, cost, fine tunability, security and privacy needs, and license permission. FMs can be accessed through APIs by the model providers, or you can download the model and self-host it in your infrastructure (on cloud or on-premises).
4. Fine-Tuned Models
If FMs' accuracy is insufficient, consider fine-tuning or customizing an existing FM. Fine-tuning is the process of adjusting the parameters of an existing model by training it on your enterprise dataset to build expertise for your specific use case or domain.
5. MLOps (or LLMOps)
Machine Learning Operations (MLOps) have always existed in traditional Machine Learning. MLOps with Generative AI, also called LLMOps, is more complex mainly due to the large scale and size of the models involved. LLMOps involve selecting a foundation model, adapting this FM for your:
- Use case
- Model evaluation
- Deployment
- Monitoring
Adapting a Foundation Model is done mainly through prompt engineering or fine-tuning. Fine-tuning brings additional data labeling, model training, and model deployment complexity to production. Several tools have emerged in the LLMOps space. There are point solutions for:
- Experimentation
- Deployment
- Monitoring
- Observability
- Prompt engineering
- Governance
6. Data Platforms and Management
Data is the lifeblood of Gen AI. The better the data used to provide context or train and fine-tune Foundation Models, the better the outcomes. Roughly 80% of the time spent in Gen AI development is to get data to the right state:
- Data ingestion
- Automating data pipelines
- Cleaning
- Data quality
- Vectorization
- Storage
Many organizations already have a data strategy for structured data, but Generative AI can take that a step further and unlock value from unstructured data. You need an unstructured data strategy to align with your Gen AI strategy.
7. Application Experience
At the top of the stack are applications integrating Gen AI models into a great user experience. These applications can use one or more LLMs or a combination of models working together to solve different problems and deliver a holistic experience.
Some notable examples include Midjourney, an AI image generator, and GitHub Copilot, an AI pair programmer. GitHub Copilot is a cloud-hosted application based on a modified version of the GPT-3 FM. The model is fine-tuned on billions of lines of open-source code repositories like GitHub.
Related Reading
- How to Build AI
- Gen AI vs AI
- GenAI Applications
- Generative AI Customer Experience
- Generative AI Automation
- Generative AI Risks
- How to Create an AI App
- AI Product Development
- GenAI Tools
- Enterprise Generative AI Tools
20 Generative AI Development Services for a Robust Tech Stack
1. Lamatic: The Managed Generative AI Tech Stack
Lamatic offers a comprehensive Generative AI tech stack that empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our generative AI tech stack.
2. McKinsey & Company: Generative AI Business Applications
McKinsey helps businesses leverage generative AI by advising on the selection and application of AI tools for tasks like:
- Content creation
- Automated design
- Product development
It also provides strategic support for implementing and scaling these tools to improve operational efficiency and enhance competitiveness.
3. Bain & Company: Generative AI Business Applications
Bain & Company assists businesses in utilizing generative AI by developing customized strategies that integrate AI-driven creative and predictive capabilities into their existing workflows. They provide advisory services to ensure these AI technologies' ethical, effective, and scalable deployment, stimulating innovation and growth.
4. Accenture: Generative AI Business Applications
Accenture’s AI strategy services are aimed at helping businesses identify and implement AI use cases, including generative AI applications.
5. Boston Consulting Group (BCG): Generative AI Business Applications
BCG supports businesses using AI by advising on integrating AI into their processes and decision-making. The company helps organizations identify relevant AI applications, implement solutions, and scale technologies to improve operational efficiency and performance.
6. Clickworker: Generative AI Training Data Collection Services
Clickworker is a crowdsourcing platform that offers data collection and annotation services for training generative AI models and large language models, including text, image, and video data generated by humans. This service provider is good for large-scale projects due to its large network of contributors.
7. Appen: Generative AI Training Data Collection Services
Appen offers data annotation and model training for generative AI, supporting tasks like natural language processing, image generation, and speech synthesis. Due to its midsized network of participants, Appen is good for midsized projects.
8. Amazon Mechanical Turk: Generative AI Training Data Collection Services
Amazon Mechanical Turk offers a marketplace for data labeling and verification services for generative AI models, including text generation, image tagging, and content moderation. Due to its small network of participants, MTurk is suitable for small-scale projects.
9. OpenAI: Generative AI Foundation Model Providers
OpenAI is renowned for its GPT series, including GPT-3, GPT-4, and its latest o1 reasoning models. These models are powerful language processors capable of generating human-like content, answering questions, and performing various language and programming tasks. They are widely used in applications like content creation, virtual assistants, programming, and more.
10. Google: Generative AI Foundation Model Providers
Google has developed several generative AI models such as BERT, T5, and LaMDA (Language Model for Dialogue Applications). These models enhance search algorithms, power conversational agents, and assist in tasks like translation and summarization.
11. H2O.ai: Generative AI Training and Development Services
H2O.ai offers a machine learning platform that helps build AI models to improve business operations, including generative AI, without necessarily having an extensive background in AI.
12. DataRobot: Generative AI Training and Development Services
DataRobot provides an enterprise AI platform enabling users to prepare data, build, train, and deploy machine learning models, including generative ones.
13. Microsoft Azure: Generative AI Training and Development Services
Azure’s Machine Learning service provides tools to build, train, and deploy machine learning models, including support for generative AI.
14. AWS SageMaker: Generative AI Training and Development Services
Amazon’s SageMaker is a fully managed service that allows developers and data scientists to build, train, and deploy machine learning models, including generative AI models.
15. Clickworker: Reinforcement Learning with Human Feedback Service Providers
Clickworker offers RLHF services through its crowdsourcing platform and a large network of contributors.
16. Prolific: Reinforcement Learning with Human Feedback Service Providers
Prolific offers AI/ML training and evaluation services through its network of contributors. Its service pool also includes RLHF services through its relatively small network of contributors.
17. TensorFlow: Deep Learning Frameworks for Generative AI
TensorFlow, an open-source software library, is widely renowned for its role in dataflow and differentiable programming. It empowers the creation of machine learning models, including neural networks and deep learning architectures. One of the key factors contributing to TensorFlow’s popularity is its large community, which facilitates knowledge sharing and provides access to pre-built models and tools.
Designed to be versatile, TensorFlow supports multiple programming languages, such as:
- Python
- C++
- Java
- Go
Developed by the Google Brain team, TensorFlow continues to be one of the most sought-after AI frameworks in the industry. Its capabilities extend beyond traditional AI applications, making it a stable diffusion for researchers and practitioners. With TensorFlow, organizations can leverage large amounts of data while deploying generative AI solutions for various domains, ranging from social media analysis to interior design models.
18. PyTorch: Deep Learning Frameworks for Generative AI
PyTorch is an open-source machine learning library known for its dynamic computational graph. It has gained popularity among researchers and developers due to its flexibility and ease of use. With GPU and CPU computing support, PyTorch allows faster training and inference, making it a preferred choice for building generative AI solutions.
The platform offers a wide range of pre-built models for various domains, such as natural language processing and computer vision. In addition, PyTorch benefits from a large, supportive community that provides extensive resources for users at all levels. This thriving ecosystem and PyTorch’s AI capabilities make it a valuable tool.
19. Keras: Deep Learning Frameworks for Generative AI
Keras is a Python-based high-level neural network API with a user-friendly interface for building deep learning models. It can be used on top of popular deep learning frameworks like TensorFlow, CNTK, or Theano. With Keras, developers can easily experiment and build complex models such as convolutional and recurrent neural networks. Its intuitive design and extensive community support make it a popular choice among AI practitioners.
Keras allows for the seamless integration of different AI capabilities and simplifies the process of creating model layers. Its stable diffusion in the industry is evident from its widespread usage in diverse fields like data analysis, social media, and even interior design models. As part of the generative AI tech stack, Keras offers a versatile solution for developers and researchers seeking powerful tools to create generative AI models.
20. Caffe: Deep Learning Frameworks for Generative AI
Caffe is a widely used open-source deep-learning framework for image classification, segmentation, and object detection tasks. Developed by the Berkeley Vision and Learning Center, Caffe is written in C++ with a Python interface. With its support for popular neural network architectures like CNNs and RNNs, Caffe offers robust AI capabilities. It also includes pre-trained models for image classification using the ImageNet dataset.
One of Caffe's key advantages is its user-friendly interface, which allows users to design, train, and deploy custom models effortlessly. It’s a stable diffusion of generative AI technology, providing a powerful resource for researchers and developers alike.
Related Reading
- Gen AI Architecture
- Generative AI Implementation
- Gen AI Platforms
- Generative AI Challenges
- Generative AI Providers
- How to Train a Generative AI Model
- Generative AI Infrastructure
- AI Middleware
- Top AI Cloud Business Management Platform Tools
How to Build Generative AI Models?
Objective and System Mapping
Clearly defining the objective and understanding the problem you aim to solve is crucial to build your generative AI solution. This step involves mapping out the system on which you will apply generative AI, including identifying data sources and potential outputs. Considering constraints or limitations, such as computational resources and available data, is essential. Consulting with domain experts and stakeholders helps ensure alignment with business needs.
As you progress, document your findings and revisit the objective and system mapping as needed throughout the model-building process. By taking these steps, you lay a solid foundation for developing a tailored generative AI solution that addresses your specific requirements and goals.
Building the Infrastructure
Building the infrastructure is a crucial step in the generative AI journey. It involves setting up the necessary hardware and software components to support your AI models. The hardware requirements may include powerful GPUs and CPUs to handle the computational demands of training and inference. Storage devices are necessary to store and access large amounts of data efficiently.
Software frameworks like TensorFlow and PyTorch are essential for building and training your generative AI models. These frameworks provide the necessary tools and libraries to implement complex neural networks and optimize them for performance. Cloud platforms like AWS and Google Cloud offer preconfigured infrastructure designed explicitly for AI models, making scaling and deploying your solutions easier. Efficient infrastructure is paramount for training and successfully deploying generative AI models. It ensures you have the necessary resources and capabilities to handle the computational complexity.
Model Selection
Model selection plays a crucial role when building generative AI models. Several factors should be considered, such as the type of data, model complexity, and training time. Popular options for generative AI models include GANs, VAEs, and autoregressive models. Each model has strengths and weaknesses, so choosing the one best fits your use case is important. Experimentation and iteration are key to making an informed decision.
By exploring different models and evaluating their performance, you can identify the most suitable option for your needs. This process ensures that your generative AI solution leverages the right model layer to achieve the desired outcomes. Ultimately, the success of your generative AI project relies on selecting the most effective model to generate content.
Training and Refinement
It involves training and refinement to ensure the accuracy and robustness of generative AI models. A large and diverse training dataset provides a solid foundation for the model. The model’s accuracy and performance can be enhanced through iterative refinement and rigorous testing.
Fine-tuning with more data or hyperparameter adjustments can further optimize the model’s capabilities. Monitoring performance metrics such as loss and accuracy throughout the training process helps guide the refinement process. It’s essential to balance model complexity and generalizability to achieve optimal performance.
Training, Governance, and Iteration
Training teaches the model to recognize patterns and make predictions, leveraging stable diffusion techniques. It involves feeding large amounts of data into the model and adjusting its parameters to minimize errors. Governance is crucial in ensuring the generative AI solution operates within ethical and legal boundaries.
It involves monitoring and managing the model’s behavior and customer service and ensuring the accuracy of the generated content. Iteration, on the other hand, focuses on refining the model by incorporating feedback and new data. This iterative approach helps improve the model’s performance over time.
Related Reading
- AI Application Development
- Best AI App Builder
- AI Development Platforms
- AI Development Cost
- SageMaker Alternatives
- Gemini Alternatives
- LangChain Alternatives
- Flowise AI
Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack. Our solution provides:
- Managed GenAI middleware
- Custom GenAI APIs
- Low code agent builders
- Automated GenAI workflows
- GenOps
- Edge deployment
- Integrated vector databases
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.
