Your machine learning project hits a roadblock. How can you conquer self-doubt and push forward?

When your machine learning project hits a roadblock, it's important to stay calm and reassess the situation objectively. Check if the problem lies in data quality, model selection, or a simple coding error. Remember that machine learning is an iterative process, and not every model will work perfectly on the first try. Use validation techniques like cross-validation to evaluate your model's performance accurately. Setbacks are normal and not a sign of your inability, but part of the learning process. Keep experimenting, learning from mistakes, and pushing forward with a positive mindset.

It's important to look back at your machine learning system to identify problems. Generally, there are three types of issues: resources, data, and the model itself. Each of these should be carefully examined to find the root cause of the problem. In my experience, I once trained a vision model for fine-grained classification. My data had enough variety and size for good performance. However, I started with an overly complex model, thinking it was necessary for the task. This led to the issue of vanishing gradients. I then simplified the model by removing some layers and reducing the number of neurons. After that, the training went smoothly, and the model performed well.

In my experience the best way to overcome a roadblock in machine learning projects is to conduct brain-storming sessions and discuss what exactly are we stuck at. Brain-storming sessions with other team-mates brings in diverse perspective which can actually lead to a better solution and help us get rid of the roadblocks in our project.

Collaboration is a cornerstone of success in this field. Reach out to peers, mentors, or even community for fresh perspectives. Their insights can add innovative solutions you might not have thought on your own. Feedback can help you identify hidden biases that might be influencing your approach & unlock new problem-solving techniques. When seeking feedback, be clear & concise about the challenges you're facing & target the right audience - people with relevant expertise in ML or specific project domain. Remember, approaching feedback with open mind is key. Not all will be positive, but it can be valuable for your growth. Sharing struggles is a sign of strength, not weakness and shows your commitment to learning & improving your skills.

Failure is just an opportunity to learn more about what did not work for you, rather than the end of your project. Assessing failures in machine learning and brainstorming solutions can lead to success. Failures are an inevitable part of the process in building ML systems, and consistently iterating on them defines a successful ML system. Each failure provides valuable insights into potential improvements. By understanding and addressing these setbacks, you can refine your approach and develop more robust models. Embracing failures as learning opportunities fosters innovation and drives continuous progress in your projects.

Imagine chopping a huge problem into bite-sized pieces – that's the power of simplification. Maybe your data needs a second look, or a simpler model is the way to go. Focus on what your project absolutely needs to do, not fancy bells and whistles. Stuck? The amazing ML community is there to help – use forums and resources to learn from others. And hey, celebrate every win, no matter how small. Small victories add up to big breakthroughs! By simplifying your approach, you can conquer self-doubt and crush your ML project. Just to add, don't run behind large ML Problems, but try to visualize it in smaller problems & solve them individually - Inspiration from MicroServices, do one thing at a time.

Learning more helps resolve doubts and unlocks new ideas. Delving deep into machine learning (or any subject) is crucial when facing a roadblock. For example, in an embedding task, the sentences "I love pizza" and "I hate pizza" might seem similar if we only focus on the word "pizza." However, they are very different when considering the context. Therefore, it's essential to understand embedding models and how they represent sentences to choose the best one for your use case.

A general process in building any machine learning product starts with creating a minimum viable product (MVP). Then, perform iterations to refine the system. Next, create efficient data pipelines to ingest large volumes of data smoothly. Build several models while engineering features to achieve the required accuracy. Scale the model vertically or horizontally to handle big data and then iteratively optimize it for low latency and high throughput. With each iteration, the system improves, and the team's confidence in the product grows. By following this iterative process, the final product becomes more robust, reliable, and ready for real-world applications.

Let me share a quick story from my experience. In a project tackling Sulphur Prediction in Steel Manufacturing, I hit a major roadblock with very low accuracy. Doubts crept in about the project's success. Luckily, I had good connections with a domain expert who simplified the entire concept to me and where I was doing wrong. Inspired, I streamlined my approach, focused on key features, and sought advice. The result? Significant accuracy gains. When doubts hit, simplify, seek help, and stay persistent—it often leads to breakthroughs.