How MLOps Platforms Can Benefit Your Business
The adoption of the internet of things (IoT), connected devices, artificial intelligence (AI), and machine learning (ML) models — with the vastly intensified quantity of data available comes an arduous task. Here’s how MLOps platforms can help says Marc Meyer, CCO of Transmetrics.
Imagine you are a digital maps application; you collect live data from GPS signals, cell towers, and anonymous product users, such as the time taken to complete journeys, the zones where traffic slowed, and any passing roadworks. Each data source has varied ownership, and the formats, accuracy, and access can also change depending on the signal strength and other aspects.
Well, thanks to the adoption of the internet of things (IoT), connected devices, artificial intelligence (AI), and machine learning (ML) models, it is possible to predict and broadcast real-time map updates even when particular data streams are offline.
But with the vastly intensified quantity of data available comes an arduous task. In this day and age, manually developing, validating, deploying, monitoring, and managing datasets in ML model production is a large-scale effort that involves multiple teams, stakeholders, and domain knowledge. And by the time the process is complete, information is outdated, requirements have changed, and algorithms need tweaking.
Machine learning operations (MLOps) can help solve many data problems today. To do that, the focus now is less on amending ML algorithms with more to gain by improving data management practices and the quality of AI training data.
This advanced data management is where MLOps platforms come in. So, what exactly do they do?
Operationalize AI and ML for a Stronger Impact
MLOps platforms give you faster time-to-delivery by automating processes across data preparation, model training, evaluation, validation, and eventually prediction generation. Their purpose is to optimize and standardize the procedures that go into ML model lifecycle management.
Let’s say you trained your ML model with imagery to identify huskies among wolves. By basing algorithms not on features but on the data set, the machine ‘learned’ and considered snow as an attribute for huskies due to the images provided in the sample data. Rather than assuming 90% of the images were huskies, an automated evaluation process could recognize that percentage as abnormal and alert MLOps teams. This can help flawed conclusions by raising awareness of the anomaly for further investigation.
MLOps help ensure data accuracy. They must check and recognize oversights and fine-tune or extend the required data points gathered per image so that the ML model, for example, can identify the huskies’ less noticeable differences. As new data becomes available, MLOps platforms support data experts by automating the validation and retraining process to look for these additional features.
Automating the validation process with AI-powered MLOps platforms means operations teams can see data quality at each stage of the data lifecycle at a glance to deal with issues in near real-time.
See More: Review: AWS SageMaker vs. Azure ML: Which MLOps Platform Works Best for Businesses?
Boost Scalability
Many businesses use ML models to meet business objectives, but most problems lie in scaling the models. When data is validated with quality and obeys standards, this gatekeeping and governance make it easier to scale quickly.
If the data lifecycle follows a set of practices and standards outlined in the MLOps platforms, the company data becomes reproducible for data preparation and training. Businesses that expand into new fields can reproduce the data pipeline and revert to previous datasets or metrics at any stage to resolve potential failures smoothly.
For example, you have a linehaul ML model that forecasts parcel delivery logistics. You expand to last-mile services and replicate the data pipeline, saving time preparing data for your ML model development. If we go back to our map application, whether trucks drive to a warehouse or the end consumers’ homes, they need real-time routing to provide the quickest journey based on traffic, road width, and potential obstructions. The data required already meets the set standards.
Companies dealing with extensive data, upscaling rapidly, or with growing intentions can leverage MLOps platforms to help them prepare and cleanse their data. If the trained model already assesses features of huskies such as height, tail size, and eye color, it can analyze said dimensions when incorporating images of german shepherds.
Implementing set processes that consider each stage of the model lifecycle, from preparation and discovery to evaluation and prediction, makes it easier to scale quickly. You eliminate duplications across teams and can more clearly identify any issues holistically.
Enhance Collaboration
Knowledge-sharing is a vital aspect of fast-growing, thriving businesses and can often be tricky to maximize across departments.
With MLOps platforms, users can save winning projects and pull data from them in the future. Their emphasis is on data, workflows, and model visibility. Creating user-friendly dashboards that visualize the information fed across teams means experts of various disciplines can contribute to the MLOps process and understand the full data journey.
Your business might have a data analyst responsible for maintaining the overall data vision, data assets, and the problems that can arise with the collection and use of data. Take a logistics data analyst; they know there are no specific tire obligations in Italy, yet your end destination is in the UK, where breaching the minimum legal tread depth of 1.6mm would leave you with a hefty £2,500 fine. It’s vital that their teammates who discover and curate the data that selects vehicles for delivery are aware of this information. They can duplicate the selection model used for your national logistics planning and add specific parameters for UK trips.
However, it is only possible to minimize silos effectively if all teams commit to their role within the platform. Ensure all connected stakeholders across development and operations teams understand the tool’s purpose, what is available, how you expect each team member to use these tools, and their accountability.
When selecting an MLOps platform, businesses should consider greater business initiatives and plan for the future to implement the most appropriate building design. Discussions with executive boards, industry experts, and platform users will provide collective comprehension of the complications and opportunities so businesses can maximize profitability, productivity, and growth.
Businesses with an MLOps platform alongside ML projects are ahead of the game. Automating standard checks, validations, and collaborating with teams in one space enhances data quality at scale. AI-powered platforms can support real-time analysis providing MLOps teams with a holistic view of anomalies at a glance to ensure every cog in the data lifecycle is streamlined and functioning together.
Have you used MLOps platforms to scale up business operations? Let us know on LinkedIn, Twitter, or Facebook. We would love to hear from you!
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