Data Science Central’s predictions for 2019
Prediction 1: Data Becomes More Important Than Algorithms
We’re well into the period when having more and better data is the key to success as companies make their journey of digital transformation. As a practical matter this opens up opportunities for competition in providing data-related solutions that’s moving in several directions at once.
One axis is that getting accurately labeled training data for either image or text is still extremely expensive and time consuming. Another axis is access to third party data. The third axis is automatically tracking and recording the provenance of data used in models. Look for service offerings around data to expand significantly this year.
Prediction 2: Everything Gets Easier as AI/ML Moves Off Analytic Platforms Onto Industry or Process Specific Applications.
A quick look around the world of AI/ML startups shows that competition is moving to industry or process specific applications, including for sectors such as fintech. These new applications are focused on embedding the AI/ML so that the user’s organization does not need support from a large in-house group of data scientists and can rely on these developers to continue to provide updates and improvements.
Watch also for accelerating rates of AI/ML adoption in mid-size and smaller companies who no longer have to have large data science teams or to rely exclusively on custom developed models.
Prediction 3: Rise of the Data Engineer and Data Analyst
Since the volume of models does not decrease, in fact increases, this moves the workload to data engineers who have two primary functions. First, to be able to create the required infrastructure required for data science like data lakes and Spark instances. The second is to be the one who takes the models and ensures they are implemented in operational systems and tracked for accuracy and refresh. Some data engineers are also responsible for DataOps, ensuring a clean and preprocessed data stream.
The other evolution of analytic platforms is the growth of Visual Analytics and Data Visualization tools. These are now mostly fully integrated alongside the data science toolset and allow data analysts and LOB managers to extract more value and even guide efforts in analytics. They don’t replace data scientists. It reinforces the team aspect that advanced analytics is becoming.
Prediction 4: Neuromorphic Chips: AI Comes to the Edge for IoT
Two different technologies are reaching semi-maturity at the same time to solve a long standing problem. That problem is latency.
The first of two technologies to solve this problem is 5G networks. The second solution is the introduction of new and better neuromorphic chips (aka spiking neural networks).
Prediction 5: Different AI Frameworks Learn to Speak to Each Other
AWS, Facebook and Microsoft have collaborated to build Open Neural Network Exchange (ONNX), making models interoperable on different frameworks. ONNX is shaping up to be a key technology this coming year as the number of models being shared among developers, apps, and devices becomes larger and larger.