2022 will be a year of unparalleled artificial intelligence (AI) innovation, and excellent data will ultimately drive it. But how can enterprises guarantee best-in-class machine learning (ML) data operations needed to create truly excellent data and scale with their applications?
In the keynote 2022: The Year of ML Data Ops, iMerit CEO Radha Basu cited ML data operations excellence as a combination of talent, technology, and processes. By effectively leveraging these three components, enterprises can guarantee a scalable ML data pipeline that will yield optimal model performance.
As 2021 comes to an end, here are three reasons why 2022 will be the year of ML data operations.
AI Products Are Going Into Production
AI and ML models across various industries are now going into production. While autonomous vehicles seem to be leading the way, other industries like medical AI and finance are also taking cutting-edge models into production.
This sets the stage for rapid innovation as once a model enters production, a feedback loop of results will force enterprises to adapt their ML data operations to meet the demands of their models. Algorithms in the field will come back with edge cases, which data operations will scramble to resolve before the algorithm is redeployed.
At this phase of production, quality data is the most important ingredient for continued success. The data lifecycle starts here, where we begin with labeling, and continuously build and annotate data for models in production to continuously train the algorithms.
iMerit has conducted a billion annotations for enterprises around the world. This unparalleled insight into ML data operations has allowed iMerit to develop expertise through rigorous algorithmic training and model building.
The insights from this point of view are clear: AI is moving faster than ever, and with so many models going into production in 2022, ML data operations will ultimately decide which models are successful and which ones are not.
Data Pipelines Require Scale and Experts-in-the-Loop
Data is only as useful as the expertise of the annotators preparing it for production. In order to scale data pipelines for fast deployment, enterprises must ensure annotators understand the needs of their project and can meet the highly-skilled parameters necessary for success.
Edge cases are continuously highlighting the need for increased expertise on the annotation front. For example, computer vision models in autonomous mobility are challenged by unique situations. These include reflections in streetside windows, which can trick the model into thinking its own reflection is another vehicle, or holidays like Halloween, where pedestrians are dressed in unfamiliar ways. As workforces continue tackling these challenges, their expertise is compounding as they continuously improve the performance of their models.
While edge cases improve workforce annotation expertise, they’ve also highlighted the need for subject matter experts in annotation. In the field of medical AI, medical expertise is needed to accurately annotate images of things like x-rays and surgeries. That’s why annotation workforces like iMerit’s employ full-time solutions experts including surgeons and radiologists, who bring the PhD-level knowledge to annotation.
For ML data ops to be successful, annotation workforces must employ the necessary expertise to succeed across all four phases of the ML ops ecosystem including:
- Labeling – to extract structured data from an unstructured medium
- Mapping – to capture the highly dynamic change of information in the real world
- Validating – ensuring variances in the data or surroundings are correct
- Monitoring – detecting changes that may require human oversight and input
Expertise is required across each of these phases, particularly when it comes to which tools are best for fulfilling the needs of each phase. AI data solutions experts have the knowledge to navigate these tools and employ the best ones for the job.
End-to-End AI Data Solutions are Coming to Market
As AI progresses, so too does the technology behind it. Platforms like iMerit’s give companies the power to leverage virtual armies of highly-skilled annotators complete with real-time matching based on the skills required for the project. Enterprises have never before leveraged an end-to-end technology platform that automatically matches them with highly-skilled annotation experts based on the needs of their project.
AI data solutions platforms are bringing together all aspects of the ML data ops lifecycle, from labeling all the way to monitoring. Instead of manually pushing through these aspects, these platforms will automate it, exponentially speeding up the feedback process that will take machine learning to the next level.
This combination of technology and human-in-the-loop expertise gives enterprises a true end-to-end AI data solution as they move to deploy their models in the field. By bridging together the right expertise, judgment, and technology, the highest quality data possible will be generated.
Real-time analytics and insight will also give project managers insights into what is happening in the ML ops ecosystem. As previously mentioned, this lifecycle has been managed manually and often improvisationally. Now, every aspect of ML data operations will be tracked and optimized.
2022 is the Year of ML Data Ops
2022 is just the beginning of a vibrant future for artificial intelligence and machine learning. Machine learning is still at the beginning, and autonomous vehicles will continue driving this technology into the future.
But as more of these models are pushed into production across various industries, the strongest possible feedback cycle will inevitably determine model success. By utilizing the people, processes, and technology to efficiently and effectively create high quality ML and AI data, enterprises will be able to scale like never before.
To watch Radha’s full keynote at the iMerit ML DataOps Summit on why 2022 will be the year of ML data operations, click here.
