Data Science & AI

In recent years, there has been an immense surge in the field of AI, machine learning, and deep learning. Nowadays, many products and projects claim to possess some form of AI capabilities, whether true or false.

However, creating AI-based systems is a challenging task that requires extensive knowledge and development efforts. In most cases, the issues are not with the algorithms or the development infrastructure, as there are numerous papers and advanced open source implementations available.

Many fields, such as computer vision and NLP, have been well-explored, and the infrastructure is well-established with various libraries and development environments like Numpy, Pandas, and Cuda.

As an experienced project manager in AI, I have encountered some main challenges in managing AI projects, such as:

1. Insufficient data for training and validation, specific to the problem at hand.
2. Lack of technical expertise in developing custom algorithms or adopting sophisticated algorithms to meet specific requirements.
3. Difficulty in effectively implementing research papers or open-source implementations, especially in systems with limited resources or those that require distributed computing.
4. Lack of knowledge in deploying ML systems into production using ML Ops.

Over the years, I have managed dozens of projects that required machine learning capabilities. Some of these projects used dedicated servers to execute the code, while others performed training on server-based systems and reduced to practice on small end devices.

If you need assistance with your AI project, please do not hesitate to contact me.