My Invisalign app uses machine learning and facial recognition to sell the benefits of dental work


Align Technology uses DevSecOps tactics to keep complex projects on track and align business and IT goals.

Image: AndreyPopov/Getty Images/iStockphoto

Align Technology’s Chief Digital Officer Sreelakshmi Kolli is using machine learning and DevOps tactics to power the company’s digital transformation.

Kolli led the cross-functional team that developed the latest version of the company’s My Invisalign app. The app combines several technologies into one product including virtual reality, facial recognition, and machine learning. Kolli said that using a DevOps approach helped to keep this complex work on track.

“The feasibility and proof of concept phase gives us an understanding of how the technology drives revenue and/or customer experience,” she said. “Modular architecture and microservices allows incremental feature delivery that reduces risk and allows for continuous delivery of innovation.”

SEE: Research: Microservices bring faster application delivery and greater flexibility to enterprises (TechRepublic Premium)

The customer-facing app accomplishes several goals at once, the company said:

  • Offers a preview of life after braces via SmileView
  • Sends weekly treatment reminders
  • Keeps patients in touch with their doctors during treatment 

More than 7.5 million people have used the clear plastic molds to straighten their teeth, the company said. Align Technology has used data from these patients to train a machine learning algorithm that powers the visualization feature in the mobile app. The SmileView feature uses machine learning to predict what a person’s smile will look like when the braces come off. 

Kolli started with Align Technology as a software engineer in 2003. Now she leads an integrated software engineering group focused on product technology strategy and development of global  consumer, customer and enterprise applications and infrastructure. This includes end user and cloud computing, voice and data networks and storage. She also led the company’s global business transformation initiative to deliver platforms to support customer experience and to simplify business processes.

Kolli used the development process of the My Invisalign app as an opportunity to move the dev team to DevSecOps practices. Kolli said that this shift represents a cultural change, and making the transition requires a common understanding among all teams on what the approach means to the engineering lifecycle. 

“Teams can make small incremental changes to get on the DevSecOps journey (instead of a large transformation initiative),” she said. “Investing in automation is also a must for continuous integration, continuous testing, continuous code analysis and vulnerability scans.”  


To build the machine learning expertise required to improve and support the My Invisalign app, she has hired team members with that skill set and built up expertise internally.

“We continue to integrate data science to all applications to deliver great visualization experiences and quality outcomes,” she said.

Align Technology uses AWS to run its workloads.

Aligning business and IT goals to power transformation

In addition to keeping patients connected with orthodontists, the My Invisalign app is a marketing tool to convince families to opt for the transparent but expensive alternative to metal braces. 

Kolli said that IT leaders should work closely with business leaders to make sure initiatives support business goals such as revenue growth, improved customer experience, or operational efficiencies, and modernize the IT operation as well. 

“Making the line of connection between the technology tasks and agility to go to market helps build shared accountability to keep technical debt in control,” she said. 

Align Technology released the revamped app in late 2019. In May of this year, the company released a digital version tool for doctors that combines a photo of the patient’s face with their 3D Invisalign treatment plan.

This ClinCheck “In-Face” Visualization is designed to help doctors manage patient treatment plans.

The visualization workflow combines three components of Align’s digital treatment platform: Invisalign Photo Uploader for patient photos, the iTero intraoral scanner to capture data needed for the 3D model of the patient’s teeth, and ClinCheck Pro 6.0. ClinCheck Pro 6.0 allows doctors to modify treatment plans through 3D controls.

These new product releases are the first in a series of innovations to reimagine the digital treatment planning process for doctors, Raj Pudipeddi, Align’s chief innovation, product, and marketing officer and senior vice president, said in a press release about the product. 

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Machine learning platform MLflow joins the Linux Foundation


Handing the platform run by Databricks to the vendor-neutral foundation will speed growth, the organizations say.


Image: boygovideo, Getty Images/iStockphoto

Databricks, the company behind open source end-to-end machine learning (ML) platform MLflow, announced Thursday that it is handing control of MLflow to the Linux Foundation.

“Our experience in working with the largest open source projects in the world shows that an open governance model allows for faster innovation and adoption through broad industry contribution and consensus building,” said VP of strategic programs at the Linux Foundation Michael Dolan.

Under the control of the foundation, MLflow will be managed using Apache License v.2, which Databricks CEO Ali Ghodsi said will easily allow businesses to use it without worry. 

“Handing MLflow over to the Linux Foundation makes it more independent, and will drive even more businesses to contribute to the growth of the platform,” Ghodsi said. 

SEE: Hiring Kit: Computer Research Scientist (TechRepublic Premium)

Databricks, which was co-founded by Apache Spark creator Matei Zaharia, released the alpha build of MLflow in 2018, and said it has seen explosive growth in interest and use since then. To contrast, Ghodsi said, it took three years to get the same amount of participation in Spark that MLflow garnered in three months.

MLflow has been adopted for ML data projects by numerous large organizations, such as Microsoft, Accenture, Zillow, Virgin, and Starbucks

MLflow was built with an open interface “designed to work with any ML library, algorithm, deployment tool or language,” Databricks said in its 2018 MLflow introductory post. Because it’s designed to be end-to-end, MLflow also incorporates every step in the machine learning process from data preparation to presentation of results. 

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

In the same introductory post, Ghodsi explained that MLflow was designed to address several problems in the machine learning process that Databricks had repeatedly heard mentioned: 

  • Too many ML products meant wasting time searching for the right combination of tools,
  • There are too many variables in each ML experiment to keep track of,

  • Reproducibility is difficult because of the above two reasons, and the problem of passing projects between teams working from different perspectives, and

  • Deployment of ML models is difficult due to a lack of standardization between tools.

“MLflow keeps this process from becoming overwhelming by providing a platform to manage the end-to-end ML development lifecycle from data preparation to production deployment, including experiment tracking, packaging code into reproducible runs, and model sharing and collaboration,” Databricks said in a press release.

Developers interested in experimenting with MLflow, which is designed to scale from small projects to enterprise-level initiatives, can find out how to install it and learn to use it at MLflow’s GitHub page.

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