Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. As organizations embark on machine learning initiatives to derive value from their data and become more “AI-driven” or “data-driven”, it’s essential to find a faster and simpler way to productionize machine learning projects so that they can make business impact faster.

Flutter blurs the lines between designer and developer and endorses a new designer developer archetype. Part designer part engineer, part Picasso and part Pascal. With ambitious designs comes the responsibility to make those designs run on the screen without losing frames. While Flutter is performant by design, how much should we really pay attention to performance optimisation? In most cases … we don’t.

Apache Kafka has gained traction as one of the most widely adopted technologies for building streaming applications - but introducing it (and scaling it) into your business can be a struggle. The problem isn’t with Kafka itself so much as the different components you need to learn and different tools required to operate it. For those motivated enough, you can invest money, effort and long Friday nights into learning, fixing and streamlining Kafka - and you’ll get there.

For decades, machine learning engineers have struggled to manage and automate ML pipelines in order to speed up model deployment in real business applications. Similar to how software developers leverage DevOps to increase efficiency and speed up release velocity, MLOps streamlines the ML development lifecycle by delivering automation, enabling collaboration across ML teams and improving the quality of ML models in production while addressing business requirements.

We are certainly in a faster-paced world. As we move forward, organizations need to get out of their silos and follow a more collaborative and efficient process to achieve excellence. Organizations that adopt DevOps are able to evolve and improve software products much faster when compared to those that use traditional software development processes. DevOps is a bridge between development and operations in an organization and aims to improve productivity as a whole.