What is Machine Learning?
Artificial intelligence, machine learning, deep learning, neural networks. These are all hot topics and they carry with them a lot of hype. Here is a a very brief explanation of each term.
Key Challenges of Machine Learning
Analytics professionals need highly speciﬁc software and hardware to produce accurate, actionable insights from machine learning. Provisioning these tools is a challenge for organisations and departments operating on a limited budget, and implementing them is often a hassle. Although they can acquire conventional machine learning tools from external vendors, they have to contend with the cost of software installation and maintenance, including the time and effort it takes the internal IT team to provide support for the extensive changes that traditional machine learning initiatives require. While there are hundreds of free machine learning libraries, the expense and expertise needed to install, manage, and maintain them constitute more effort than most marketing departments are willing to spend. How much time and money do you want to spend acquiring and maintaining the tools for predictive modelling rather than delivering the results?
Robots Don’t Rest
Data scientists are highly prized individuals, and rightly so. They exploit a unique combination of skills in statistical mathematics, programming and business domain expertise to harness the power of your data to create machine learning models to work business objectives. But data scientists are only human and suffer the same foibles as the rest of us: they sleep, take vacations, fall ill and make mistakes. Of course, this is normal and only to be expected. Automated machine learning can work with data scientists (or even without!) to take away some of the pressures and offer ongoing benefits and scalable value to your organisation.
Completeness of Vision
Data science projects, just like projects from other disciplines, have a life cycle. The success of your data science programme depends on all the steps along the way being delivered properly. Robotica Machine Learning does not leave the success of your programme to chance. We work across the entire life cycle to ensure you are supported at every step along the way.
Reasons Why Many Data Science Projects End in the Lab
Not all data science projects are successes. If a predictive model does not get deployed and used, it can mean months of lost effort, at least tens of thousands of pounds in costs and missed opportunities. Here are some of the reasons models do not reach production.
Machine Learning Operations
A machine learning project that doesn’t make it to production is an expensive failure, yet this is the reality for many. By treating each “phase” of work - business objective, modelling, scoring - as separate from each other, the risk of each transitioning smoothly to the next phase becomes more challenging.