{"id":786,"date":"2021-09-04T13:59:06","date_gmt":"2021-09-04T13:59:06","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2021\/09\/04\/down-to-a-science-how-johnson-johnson-boosts-its-business-with-mlops\/"},"modified":"2021-09-04T13:59:06","modified_gmt":"2021-09-04T13:59:06","slug":"down-to-a-science-how-johnson-johnson-boosts-its-business-with-mlops","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2021\/09\/04\/down-to-a-science-how-johnson-johnson-boosts-its-business-with-mlops\/","title":{"rendered":"Down to a Science: How Johnson & Johnson Boosts Its Business With MLOps"},"content":{"rendered":"
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Healthcare giant Johnson & Johnson is injecting data science across its business to improve its manufacturing, clinical trial enrollment, forecasting and more.<\/p>\n

\u201cI actually like to call it decision science,\u201d said Jim Swanson, the company\u2019s executive vice president and enterprise chief information officer, in a panel discussion<\/a> at the most recent NVIDIA GPU Technology Conference<\/a>. \u201cIt\u2019s not just about creating a model \u2014 it\u2019s actually what decisions and insights you\u2019re trying to derive from these models.\u201d<\/p>\n

Machine learning operations, known as MLOps<\/a>, is a set of best practices for businesses to run AI successfully. With an MLOps strategy, companies can harness data to answer difficult questions and measurably boost business operations.<\/p>\n

Johnson & Johnson, for example, has formed an internal Data Science Council and developed something Swanson calls \u201cbilingual data scientists\u201d \u2014 roles that combine deep domain expertise with data science skills.<\/p>\n

\u201cThey have that understanding of the main business problem, and they have the skills to do data science,\u201d he said.<\/p>\n

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Jim Swanson, Rima Alameddine, and Nick Elprin discussed data science at Johnson & Johnson during the most recent GPU Technology Conference.<\/figcaption><\/figure>\n

This strategy helps integrate a company\u2019s data science community into the business workflows, enabling faster application of machine learning models, feedback and impact, Swanson said. It also helps overcome hesitation to data science adoption.<\/p>\n

\u201cYou\u2019ve really got to show them by proving over and over again: Hey, this model doesn\u2019t replace the valuable asset of people skills you have in your business domain, it gives you longitudinal views you can\u2019t get on your own,\u201d he said.<\/p>\n

As companies scale up their adoption of MLOps, they need a powerful AI infrastructure to support their engineers and data scientists, said panelist Nick Elprin, CEO of Domino Data Lab.<\/p>\n

\u201cSo many companies, tragically, waste precious engineering resources trying to build this tooling themselves, and it\u2019s a lot harder,\u201d he said, recommending that companies get started with a third-party platform like the NVIDIA GPU-accelerated Domino Data Lab<\/a>. \u201cYour engineers are going to be creating much more value when focused on problems that are competitively differentiated and unique to your business.\u201d<\/p>\n

To help businesses get started with MLOps, NVIDIA provides a suite of open-source tools on the NGC<\/a> software hub for managing an AI infrastructure based on NVIDIA DGX systems<\/a>. Healthcare companies and medical researchers use DGX systems and the NVIDIA Clara application framework<\/a> to run healthcare models that parse electronic medical records, boost computational drug discovery and power AI-enabled medical devices and imaging workflows.<\/p>\n

From Vision to Business Impact<\/b><\/h2>\n

In addition to embedding over 1,000 data scientists in its business, Johnson & Johnson is working to build digital literacy across the whole company \u2014 helping employees understand the potential of machine learning models in action.<\/p>\n

Swanson gives the example of an internal hackathon Johnson & Johnson held to improve forecasting in its vision care business. By better predicting how many customers will need each product in its line of Acuvue contact lenses, the company can more efficiently manufacture the ones people want.<\/p>\n

For every percentage point Johnson & Johnson improves forecast accuracy, the company boosts revenue, \u201cbecause you have the right product going into the market,\u201d said Swanson.<\/p>\n

Dozens of teams across the company \u2014 most outside the vision care business \u2014 signed up for the hackathon, which used Domino Data Lab\u2019s Enterprise MLOps platform<\/a>. The procurement team came up with the best model.<\/p>\n

\u201cWe solved a really big problem with real impact, and they learned some new tooling that they hadn\u2019t known before,\u201d Swanson said. \u201cWith a simple project aligned to a really significant outcome, you can do amazing things.\u201d<\/p>\n

Find the full talk replay<\/a>, and thousands of other free sessions, on NVIDIA On-Demand<\/a>.<\/p>\n

Subscribe to <\/i>NVIDIA healthcare news<\/i><\/a> and follow <\/i>NVIDIA Healthcare on Twitter<\/i><\/a>.<\/i><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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