AI Tools for Non-ML Specialists: How to Navigate?

AI Tools for Non-ML Specialists: How to Navigate?

The advent of open and cloud-based AI libraries has made AI more accessible than ever before. Now, even non-ML specialists can harness the power of AI to add value to their projects. 

In this blog, we’ll discuss how non-ML specialists can make the most out of AI tools, what are the best resources to use, and when hiring a dedicated ML specialist is necessary.

OpenAI tools for non-ML specialists

The following tools and resources can leverage the capabilities of AI in various projects, even without an ML background:

  • OpenAI Codex: An AI model that powers GitHub Copilot, offering AI-based code completion and suggestion for various coding tasks.
  • GPT-3: an NLP model for tasks like text generation, summarization, translation, and question-answering.
  • DALL-E: capable of generating images from textual descriptions (no official API available yet).
  • CLIP: understands images and texts for tasks such as zero-shot image classification and image captioning.
  • OpenAI Gym: a toolkit for developing and comparing reinforcement learning algorithms with various environments and tasks.

Other AI tools for non-ML specialists

  • TensorFlow and Keras: open-source ML libraries by Google that offer pre-built models for developing deep learning models.
  • Scikit-learn: Python library provides simple and efficient data mining and analysis tools.
  • Microsoft Azure ML Studio: a cloud-based, drag-and-drop tool for building, testing, and deploying machine learning models without coding.
  • Google Cloud AutoML: service that enables building custom ML models with minimal expertise.
  • open-source, in-memory, distributed platform with various algorithms and an AutoML functionality.
  • IBM Watson: a suite of AI services on the cloud, including NLP, speech-to-text, text-to-speech, and visual recognition.
  • Amazon SageMaker: fully managed service for building, training, and deploying ML models quickly with built-in algorithms and an AutoML feature.

AI libraries: benefits & considerations

So, what are the top advantages of open and cloud-based AI libraries?

  • Quick integration: AI libraries provide ready-to-use solutions, enabling non-ML specialists to quickly integrate AI capabilities into their projects without developing complex algorithms from scratch.
  • Cost-effectiveness: Open-source AI libraries are free, while cloud-based libraries often offer pay-as-you-go pricing models, making them an affordable option for startups and small businesses.
  • Scalability: Cloud-based AI libraries automatically scale with your project, ensuring you have enough resources to handle increasing workloads.
  • Continuous improvement: The community constantly updates and improves open and cloud-based AI libraries, ensuring you benefit from the latest advances in AI technology.

When to hire an ML specialist?

While the variety of AI libraries makes it possible for non-ML specialists to leverage AI technology, there are cases when hiring a dedicated ML person might be necessary:

  1. You need customized solutions. An ML specialist can develop custom algorithms to fit your needs if your project requires a tailor-made AI solution that existing libraries cannot adequately address.
  2. You’re working on complex projects. An ML specialist can ensure optimal performance and efficiency for projects with detailed ML requirements, such as integrating multiple AI models or handling large datasets.
  3. You seek advanced optimization. If your AI model’s performance does not meet expectations, an ML specialist can tune it and optimize its performance.
  4. You’re designing the AI strategy. An ML specialist can help you devise a comprehensive AI strategy, aligning initiatives with your business goals and ensuring a competitive advantage.

What if you’re a non-ML specialist…

and work on your must-have skill set? Here’s what a non-ML specialist should know:

  • Basic AI concepts: Familiarize yourself with things like supervised, unsupervised, and reinforcement learning to help you choose the suitable library and approach for your project. 
  • Data preparation: Ensure your data is clean, well-structured, and properly labeled to maximize the performance of AI models.
  • Evaluation metrics: Understand how to evaluate the performance of your AI model using metrics like accuracy, precision, recall, and F1 score.
  • Ethical considerations: Be aware of potential ethical implications of using AI, such as privacy concerns and algorithmic bias, and take steps to mitigate them.

To conclude 

By integrating OpenAI tools and other AI libraries, non-ML specialists can significantly enhance their projects. However, it’s essential to understand the basics of AI and the limitations of these libraries. In cases where custom solutions or advanced optimization are needed, hiring a dedicated ML specialist can be invaluable in unlocking the full potential of AI for your project.