How to choose the best cloud platform for AI

Cloud is a home to Artificial Intelligence.

A 2024 McKinsey study indicates that the adoption of AI has surged, with 72% of organizations now implementing it in at least one area of their business, compared to 55% in 2023 and 20% in 2017. That’s why decision-makers often grapple with multiple challenges: which cloud providers align best with our AI ambitions? What cloud strategies will yield the most value? How can we make sure our choices remain economically viable in a constantly changing tech ecosystem? This article dives into the heart of these questions. We’ll explore the nuances of different providers, examine key considerations for AI workloads, and share insights on how to balance performance with cost-effectiveness. As we peel back the layers of this complex decision-making, we’ll equip you with the knowledge to make informed and impactful choices.

What you need to know

As we discussed in our previous article, there are numerous key factors that will impact your strategy in this difficult choice of a cloud provider. Computational power stands at the forefront of these considerations. AI workloads, particularly in areas such as deep learning and large language models, require substantial computing resources. The chosen cloud provider should offer access to high-performance GPUs and specialized AI accelerators. This isn’t merely about raw power, but also about the flexibility to scale these resources up or down based on changing needs. A provider that has a diverse range of instance types, from general-purpose to AI-optimized, can accommodate various stages of AI development and deployment.

Closely tied to computational resources is the aspect of data storage and management. AI systems thrive on data, which often requires vast amounts of it for training and inference. The suitable cloud provider has robust and scalable storage solutions that can efficiently handle large datasets. This means capacity and data transfer speeds, which can substantially impact the performance of AI workflows. Additionally, it is desirable that the cloud platform offers tools for data preprocessing, labeling, and version control, as these are crucial for maintaining data quality and reproducibility in AI projects.

Compliance and security form another critical pillar in the decision-making process. SInce AI systems often deal with sensitive or proprietary data, the cloud provider should demonstrate strong security measures and compliance with relevant regulations. This includes features like encryption at rest and in transit, identity and access management, and adherence to standards such as GDPR, HIPAA, or industry-specific regulations. The provider’s track record in handling security incidents and their transparency in communicating potential vulnerabilities are also important factors to consider.

The AI and Machine Learning ecosystem offered by the cloud provider is another key differentiator. This encompasses the range of pre-built AI services, Machine Learning frameworks, and development tools available on the platform. A rich ecosystem can accelerate AI development and deployment, as it can contain everything from automated Machine Learning platforms to pre-trained models for common tasks. The provider’s commitment to the latest AI advancements and their ability to integrate cutting-edge technologies into their services and tools can give enterprises a competitive edge.

Last but not least, interoperability and vendor lock-in are concerns that warrant serious attention. While it’s tempting to fully embrace a single provider’s ecosystem, this approach can lead to dependencies that are difficult and costly in the long-term perspective. Often, companies need to evaluate a cloud provider’s support for open standards, containerization technologies, and hybrid or multi-cloud deployments. This flexibility allows businesses to distribute their AI workloads across different environments and facilitates easier migration in case of unpredictable situations.

Your AI Cloud Companion

Opting for a specific cloud provider or building your cloud strategy is a long and arduous process that can require months of work. Rather than doing the research on your own, you can use our AI Cloud Companion and find an appropriate cloud vendor or their combination. We can analyze which of the following providers is best suited to your projects:

AI Cloud Companion covers a broad range of AI-related tasks. It can be of great use in projects where data comes from different sources, be it image, text, audio, or video. We also focus on generative AI solutions that can create content, image/video/audio materials, code, and synthetic data. Here is a closer look at what’s possible with AI Cloud Companion:

Image processing

Our team works with projects on image processing, which encompasses face, text, or object detection. This can be typically used in autonomous vehicles, medical imaging, retail inventory management, quality control in manufacturing, and more. We can test for you which cloud platform will respond well to your requirements, for example, in situations where image data has low quality or there is partial visibility of an object of interest.

Document AI

With AI Cloud Companion, your organization can extract information from unstructured documents. The document AI capabilities include optical character recognition (OCR), key-value pair extraction, and tabular data extraction.

OCR converts text from images or scanned documents into machine-readable text. This allows you to process and analyze textual content that was previously inaccessible to computers.

Key-value pair extraction identifies and extracts key-value pairs from documents, such as names and addresses, email addresses, and phone numbers. For structured documents like invoices or forms, AI can find and extract key-value pairs, such as “Amount: $100,” etc.

Lastly, tabular data extraction helps pinpoint tabular data from documents, such as tables or forms. This data can be later integrated into databases or other systems for analysis and reporting.

Video processing

Video processing involves the analysis and extraction of information from video data. AI-powered video processing can be used for purposes like human and object tracking. For example, in sports analytics, AI can track athletes’ movements for performance analysis. In autonomous vehicles, human and object tracking can be used for safe navigation around obstacles.

Text processing

AI Cloud Companion is adept at text processing that encompasses three components. At first comes sentiment analysis. This implies identifying and classifying the emotional tone of the text, such as positive, negative, or neutral (see Fig. 1). It’s particularly useful for understanding customer feedback or social media sentiment.

Figure 1. Sentiment scores for the audio news samples presented in a 2023 research.

The next one is key phrase extraction. As a way to pinpoint the most important or relevant phrases within a text, it is helpful for summarizing lengthy documents, extracting keywords for search engine optimization, or categorizing content.

Last but not least, there is named entity recognition within Avenga’s AI Cloud Companion. This is a tool that classifies entities in a text, such as products, events, locations, organizations, etc. It’s essential for information extraction and knowledge graph construction and can also be used in question-answering systems.

Speech processing

AI Cloud Companion is also proficient in speech processing, a field that refers to the analysis and understanding of spoken language. One of the critical sub branches within speech processing is speech-to-text. Speech-to-text technology converts spoken words into written text and stands behind transcription services, virtual assistants, and voice-controlled devices.

We help companies work with various datasets and estimate which AI model and cloud platform would be most suitable for their requirements. Factors such as the size and complexity of the dataset, desired accuracy, and language variety are essential in this estimation.

Generative AI

Companies experiment with integration of generative AI into their businesses in a quest for more productivity gains and accelerated digital transformation. You can use our AI Cloud Companion as a starting point for creating efficient and accurate generative AI tools. Our expertise and guidance can help you avoid common pitfalls and achieve faster results in generative AI adoption.

Strengthens of AI Cloud Companion

AI adoption has become a critical differentiator for business success. Time is indeed of the essence, as companies often cannot afford lengthy evaluation periods or unsuccessful implementation attempts. AI Cloud Companion addresses this urgent need. For organizations eager to experiment with and implement AI solutions, it compresses the timeline from initial concept to actual deployment and allows organizations to stay competitive in their respective markets.

The platform’s ability to reduce time-to-market for AI initiatives creates tangible business advantages. Instead of spending months evaluating different cloud providers and their AI capabilities, companies can quickly determine and implement the most suitable solutions for their specific needs. This rapid deployment capability is coupled with data analysis features across various formats, which enables organizations to make faster and more informed decisions.

Beyond time savings, AI Cloud Companion delivers substantial cost benefits. It minimizes research and implementation expenses. Organizations avoid costly trial-and-error approaches in cloud provider selection and AI model implementation. The platform’s expert guidance helps companies navigate common challenges in AI adoption. This guidance is particularly valuable in complex scenarios like multi-cloud strategies or when implementing advanced features such as generative AI, where mistakes can be expensive and time-consuming.

Finally, AI Cloud Companion is a catalyst for digital transformation. It provides a clear pathway for AI implementation across various business functions, including automated document processing and advanced video analytics. In this case, businesses modernize their operations through a systematic approach and profound AI expertise. They gain the ability to deploy AI solutions across multiple departments, from automating routine document processing in administrative functions to implementing sophisticated video analytics in quality control.

Closing remarks

AI cloud services provide businesses a unique opportunity to experiment with AI adoption on different scales. But, while the core offerings of major cloud providers may seem comparable, the nuances and specific features can noticeably impact a project’s outcome. To make an informed decision, businesses may consider the specific AI workloads, desired level of control, scalability requirements, and budget constraints. Or they can lean on Avenga’s Cloud and test their AI hypothesis in a controlled environment before making a final commitment.

Use our AI Cloud Companion to explore different options and make an informed decision about the best cloud provider for AI capabilities: contact us.