Mediaboss Marketing

In this special guest feature, Rob Dickinson, CTO, Resurface Labs, suggests that to achieve greater success with AI/ML models, through accurate business understanding, clear data understanding, and high data quality, todays API-first organizations must shift towards real-time data collection. Robs built all kinds of databases and data pipelines. Keeping the end result in mind, Rob builds data architectures that focus on the consumption of data, whether its blazing fast queries against very large datasets or finding the needle in a haystack. Ultimately delivering better data access across all purposes and teams. Years at Intel, Dell, and Quest Software, framed his passion for customer input, and to find elegant ways to architect and build scalable software.

Whether data scientist or CEO, everyone hungers for more data. Its not just a matter of volume, and not simply an exercise in data viz, todays algorithm-driven organizations want insights as fast as possible those business markers that AI and machine learning teams strive to deliver on.

You cant do effective machine learning without having the Big Data, so organizations must learn to harness the millions (billions?) of daily interactions they have inside and outside their walls. APIs offer an existing and logical pipeline to get data into modelling and analytics processes.

To achieve success with AI and ML models, here are a few API-driven principles around business understanding, data comprehension, and data quality.

Machine learning begins with data access

Did Amazon raise the bar too high? The e-commerce giant blazed the path towards making services visible to everyone through APIs and now, every CEO, CFO, and CMO wants to rule them all. But without the scale and resources of Big Tech, data scientists are forever told the data is coming by IT teams, leading to C-suite executives boxed in by assumptions and guesswork rather than empowered by real-world patterns.

This is especially painful for organizations building out their API strategy at the same time as their AI and ML expertise. Its often a lose-lose race between the teams responsible for infrastructure and the data scientists needing more information now.

For non-Amazon organizations, three principles are fundamental to the success of data analytics:

Additionally, with a greater focus on data access, come the safeguards that all organizations must face, such as implementing privacy and security standards. These processes will only get more complex over time, and restrict how the ML pipeline operates, incurring significant change and compliance overhead the longer a company waits to get it right.

The chances of success in these areas are higher when the barriers to collecting data are lowered, and when the data accurately represents the real-world scenarios being modeled. APIs contain this information already, its just a matter of knowing how to capture, store, and secure it.

Fueling the ML pipeline with the right data

Real-time behavioral data is the pathway towards better business understanding and comprehension. It cannot be overstated that any biases or errors in models are not overcome by looking at the model itself; they can only be mitigated by looking at the original source data.

For example, the success or failure of AI-based personalization engines can only be determined by understanding how customers behave and by adjusting the recommender model with those observations. With a higher level of observability in the business, using current and complete API data raises the ability to bootstrap AI systems more effectively and improve the accuracy of predictions.

To achieve success in real-time API data collection, organizations must:

Ultimately, shifting to real-time API data collection to train, validate, and iterate AI and ML models leads to more timely results and fewer gaps filled by assumptions and guesswork. By arming teams with the skills and tools that connect APIs to data science and DevOps, models will be better able to deliver on the promises of accurate business knowledge, clear data understanding, and high data quality.

Sign up for the free insideBIGDATAnewsletter.

Join us on Twitter:@InsideBigData1 https://twitter.com/InsideBigData1

The rest is here:
APIs: The Real ML Pipeline Everyone Should Be Talking About - insideBIGDATA