Challenges in AI development
Artificial Intelligence market size is growing and it is said that it can grow up to $15.7 trillion by 2030, as quoted in the research paper https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI(2019)637967_EN.pdf
As AI grows, the impact and challenges rise parallely as well. Let's see some of the most common challenges in Artificial Intelligence Development.
Navan.ai has figured out six challenges for developing Artificial Intelligence models:
1. Research: Wondering why “research”? I am sure that most of the articles might not add this as a challenge, but the first thing every developer does when they have a problem is that they do research.
- Research can be for Datasets.
- Research can be done on what are the available solutions/ ways to solve the problem.
- Research can be on the code.
- Research can be done
- By reading Blogs and articles.
- By watching Vlogs.
- By studying research papers.
- By browsing multiple websites and forums.
2. Dataset Preparation: We know that the output AI model is completely dependent on the data which we use to train the model, so dataset preparation is very challenging for every company.
- Dataset requirement understanding-
- finding the right dataset for your problem and understanding the data.
- Collection of data-
- Getting the data which is collected from a number of sources and millions of users so there might be some sensitive data which leads to data breach.
- Getting good quality data requires a lot of time to find and label the trained data, preprocess the images, and annotate the images.
- We have to store large and comprehensive data in data storage and we need to version it when the new data gets updated.
3. Code Development:
- The big challenge for everyone here is writing the code for the AI model.
- Complex libraries are involved while developing ML code.
- Storing the code and code versioning .
4. ML Model Training:
- Finding efficient Model
- What model is the most efficient for the user? We may not be knowing all the models. We should know a few parameters of the models.
- Understanding algorithm
- Need to understand the algorithm, model, libraries.
- Parameters to be set during model training
- When the model is trained, how the model is validated.
- Improving model and fine tuning.
- Versioning the model and storing the trained model.
5. ML Model Testing:
- Testing trained models either manually or automatically and both have their own challenges. -Automated testing involves a lot of code.
- Analysing and visualising metrics.
- Storing test results history logging.
- Comparing model versions.
6. ML Deployment:
- Complex libraries are used in ML deployment and setting up servers to support those libraries.
- Model serving and handling changes from model to model.
- Libraries installation and managing versions.
- API development and integration of the models with applications.
- Using technologies like Docker application to avoid portability and cross platform integration.
Build Your Own Model Without Writing a Single Line of Code using navan.ai
- The Navan.ai no-code platform for now is mainly focusing on Computer Vision problems.
- Will provide examples to help you understand which model will be relevant for your application.
- Example: you can use Navan.ai to build an image classification model using the datasets of different types of leaves - leaves with diseases and leaves without diseases.
- Easy to add/update/delete custom data
- One click model training is available.
- The trained models are stored and can be accessed anytime.
- The trained model is validated and tested in the same platform.
- Multiple export options are available for deployments and automatic integrations.
Ready to create your own computer vision model without writing a single line of code? Request access to studio.navan.ai NOW by filling up this form: https://docs.google.com/forms/d/e/1FAIpQLSfP0tFJSwqLY1NLXAg_AMzQnF_8c4yt7IybpfCG8kjBCkbY_Q/viewform