The world is always leaning more and more into greater efficiency and convenience. Is that necessarily always going to be possible when relying on human talent and engineering alone? Machine learning startups are changing production up for the better. But what does this entail?
Machine Learning in Product Development: In Brief
Machine learning, accessible through artificial intelligence (AI), effectively involves teaching and training code. With specific instructions, AI can make decisions and take action. These may be to recognize patterns and to take prescribed steps. Machine learning may even assist production by taking over mundane or smaller tasks that consume human hours.
Ultimately, AI and machine learning are helping to automate the assembly line. This not only ensures peak efficiency and removes people from potential safety hazards but can also increase profit. Deploying human engineers elsewhere can ensure their talents are best used beyond assembly.
What’s more, the market for AI and machine learning is expanding. Beyond production alone, a record $38 billion in funding found its way to AI startups in 2021. That, impressively, is in just the first half of the year.
How are Startups Using Machine Learning in Production?
To better understand machine learning’s place in the wider ecosystem, we need to consider some success stories still unfolding.
H2O.ai is a leading name in AI solutions engineering that applies itself to multiple standards. For example, their landmark ‘Driverless AI’ technology deploys to help companies actively create their automated processes. Out of the box, it is a solution that allows teams to engineer their own unique AI in-house.
Their app building interface, H2O Wave, is a data science framework rooted in Python. This service, much like Driverless AI, helps give complete control to the user. However, the endgame is to divert control to AI altogether! In any case, these solutions help otherwise uninitiated engineers find their way towards simple, deployable AI operations.
Tecton, meanwhile, is an analytics-focused AI venture that’s made significant funding strides over the past two years. Their machine learning standards help production teams to streamline and re-visualize their processes to a simpler standard. For example, Tecton can allow brands to effectively blend multiple data channels into one swift, communicative stream.
Tecton’s standards help businesses to create automated, self-reporting analytics with improved accuracy. Ultimately, this could help complicated production lines see either end of the process with no confusion in the middle.
Machine learning startups will continue to make wave. Human error is no longer welcome in a society where solutions are demanded quicker and more accurate than ever before. Therefore, AI and machine learning standards are helping to keep production flows accurate and efficient.