Improve Performance in Your AI and ML Projects

The majority of AI ML projects fail. In fact, over 85% never make it to production. This means the overwhelming majority of companies are pouring money into failing prospects. Companies need a better way to make data science projects work. There has to be a better data science process that transforms the way machine learning models are built and operationalized. If that doesn’t change, we will see data science continue to be a niche prospect that only a few massive corporations can actually be successful in.

These corporations will continue to be ahead of their peers, meaning that most companies will never be able to catch up as time goes on. The best thing that companies can do in the data science world is to start changing how they deploy AI ML projects. This change will enable them to completely improve the results they are getting.

The Product-first Mindset

The main problem with AI ML projects is the focus on the laboratory environment instead of operationalization. Data science projects are useless when they stay in the lab. However, the focus during the development is on the data science process instead of improving how you put things into production. Data science in production is a completely different beast, and you need to consider other factors when deploying your models.

The companies that will be the most successful are the ones focusing on operationalization and making data science projects work in a production environment instead of the lab.

Use a Better Platform

Another source of problem for bringing AI ML projects to production is that companies try to do everything themselves. Data science is rapidly innovating, meaning there are now many platforms like xpresso.ai that can help you bring your models to production easily.

Companies that try to do everything in the data science process by themselves end up failing. They spend most of their resources on the infrastructure instead of the actual model. By doing this, you guarantee that your data science projects will never see the light of day. A better choice is to focus on platforms that will help you automate all aspects of the data science process. This means you can focus on building effective models that do what you want. It also means you don’t have to spend time and effort on things that don’t give you a return.

Other Ways to Improve

The best thing you can do is to focus your time and effort on finding MLOps platforms like xpresso.ai that can do a good job. These platforms will enable you to find success, meaning your data science projects become easier to put into production. The major stumbling blocks in developing machine learning algorithms can be automated, and you then focus on fine-tuning your model to give it the best performance possible. These platforms will also enable you to maintain and monitor your model in production. This drastically shortens the development cycle and makes life as a machine learning engineer easier.