Slow Down to Speed Up: How to Stop Losing Money on your AI Transformation
As companies take the leap and begin making investments in AI they often come up against several roadblocks along the way. Learning from the evolution of software development in the early 2000’s – what became known as DevOps – will allow companies to overcome these obstacles and will improve their AI and Analytics process. By investing in what is now being called ModelOps, companies can drastically cut down the time it takes to go from data collection to AI deployment.
ModelOps is a framework for standardizing the process of collecting data, exploring and applying AI models, and ultimately deploying these models into business operations. Standardization can lead to incredible value creation for an organization by reducing time to deployment and allowing projects to fail fast. So the question is: how can this be achieved and why are so few companies focussed on implementing a ModelOps framework in their business?
Firstly, companies encounter endless issues with their data. They have unstructured data that is difficult to access and has missing values. The data may also hold values that are invalid and cannot be relied upon. In contrast, there are companies on the opposite end of the spectrum who do not even have data capture capabilities. This leaves them without the insights they need to apply Machine Learning algorithms.
Second, there is a shortage of skilled data scientists who can understand the data and determine which models are best for a given business problem. Even when organizations have a team of data scientists, accessing data involves many time-consuming, manual processes.
Lastly, companies lack the technology required to allow their data science teams to quickly and efficiently go from data collection to model deployment. They will then assign data science resources to maintaining old models which limits new innovation. Companies may also have legacy systems that only allow their data science staff to program in certain coding languages. This limits the number of qualified data scientists they can hire and increases the talent shortage.
The three issues above are the largest barriers to achieving success in AI. To combat these, organizations must first focus on the data problem. Companies must seek out and identify the most important problems in their business. This could be to increase customer satisfaction, reduce costs, or diversify their revenue streams. Only once the problems are identified should the company investigate which data sources may help improve decision-making or fuel Machine Learning algorithms. By seeking out problems first, companies will avoid being driven towards solutions that their existing data makes available and increase the likelihood of achieving specific business goals. Companies can then invest in data storage that is easily accessed by data science teams and updated in real-time without human intervention. With heavy focus and investment on the data structuring, companies can move forward and empower their data science team.
Embedding automation into the data collection process allows the data science team to quickly apply their models and avoid emailing excel files back and forth. Companies should also enable their data scientists to code in their language of choice. By doing this, HR can recruit from a larger pool of qualified and intelligent data scientists. Businesses can potentially go a step further and adopt platforms that are friendly to those who do not code. This further increases the number of staff capable of working on data science projects.
Finally, once the above is successful, companies will be going quickly from data to deployment. As this happens, it is extremely important to coach data scientists to save their case studies and learnings in order to build a repository of model templates for common data science problems. AI rock stars may even embed Machine Learning within the ModelOps process to have algorithms quickly identify other useful algorithms that solve common problems (mind-blowing, I know!).
The above steps are intensive, technical, confusing and difficult. Thankfully, there are several firms with the skills and expertise to assist companies in setting up their ModelOps infrastructure. These include: SAS, Avanade, and Accenture. With so much at stake it may be worthwhile to take a step-back, stop dumping resources into inefficient AI projects, and begin the hard work of establishing a ModelOps architecture. Slowing down today will allow companies to speed up tomorrow and 10x their AI investment.