Data Management Challenges in Production Machine Learning By Google #Data #Management #MLOps #MachineLearning

This post is part one in a three part series on the challenges of improving a production machine learning system. Find part two here and part three here. Suppose you have been hired to apply state of the art machine learning technology to improve the Foo vs Bar classifier at FooBar International. Foo vs Bar classification is a critical business need for FooBar International, and the company has been using a simple system based on a decade-old machine learning technology to solve this problem for the last several years. As a machine learning expert, you are shocked that FooBar International hasn't gotten around to modernizing this system, and you are confident that replacing the Foo vs Bar classifier with the latest machine learning hotness will dramatically improve system performance.

This year, RoboCup will be taking place from 22-28 June as a fully remote event with RoboCup competitions and activities taking place all over the world. The RoboCup Logistics League (RCLL) is a sub-league of the RoboCup Industrial League. It focuses on in-factory logistics applications. The goal is for a team of autonomous robots to assemble products on demand, using a set of production machines. Each team comprises up to three autonomous robots which can produce using seven machines.

There is (rightfully) quite a bit of emphasis on testing and optimizing models pre-deployment in the machine learning ecosystem, with meta machine learning platforms like Comet becoming a standard part of the data science stack. There has been less of an emphasis, however, on testing and optimizing models post-deployment, at least as far as tooling is concerned. This dearth of tooling has forced many to build extra in-house infrastructure, adding yet another bottleneck to deploying to production. We've spent a lot of time thinking about A/B testing deployed models in Cortex, our open source ML deployment platform. After several iterations, we've built a set of features that make it easy to conduct scalable, automated A/B tests of deployed models.

How did farming affect your day today? If you live in a city, you might feel disconnected from the farms and fields that produce your food. Agriculture is a core piece of our lives, but we often take it for granted. The world's population is expected to grow to nearly 10 billion by 2050, increasing the global food demand by 50%. As this demand for food grows, land, water, and other resources will come under even more pressure. The variability inherent in farming, like changing weather conditions, and threats like weeds and pests also have consequential effects on a farmer's ability to produce food.

PALO ALTO, Calif., May 6, 2020 – Cloudera (NYSE: CLDR), the enterprise data cloud company, today announced an expanded set of production machine learning capabilities for MLOps is now available in Cloudera Machine Learning (CML). Organizations can manage and secure the ML lifecycle for production machine learning with CML's new MLOps features and Cloudera SDX for models. Data scientists, machine learning engineers, and operators can collaborate in a single unified solution, drastically reducing time to value and minimizing business risk for production machine learning models. "Companies past the piloting phase of machine learning adoption are looking to scale deployments in production to hundreds or even thousands of ML models across their entire business," said Andrew Brust, Founder and CEO of Blue Badge Insights. "Managing, monitoring and governing models at this scale can't be a bespoke process. With a true ML operations platform, companies can make AI a mission-critical component of their digitally transformed business."

At this point, it should be a surprise to no one that Python is the most popular language for machine learning projects. While languages like R, C, and Julia have their proponents--and use cases--Python remains the most universally embraced language, being used in every major machine learning framework. So, naturally, our codebase at Cortex--an open source platform for deploying machine learning models as APIs--is 87.5% Go. Machine learning algorithms, where Python shines, are just one component of a production machine learning system. Cortex is built to automate all of this infrastructure, along with other concerns like logging and cost optimizations. A user can have many different models deployed as distinct APIs, all managed in the same Cortex cluster.

Machine learning is about rapid experimentation and iteration, and without keeping track of your modeling history you won't be able to learn much. Versioning lets you keep track of all of your models, how well they've done, and what hyperparameters you used to get there. This post will walk through why data versioning is important, tools to get it done with, and how to version your models that go into production. If you've spent time working with Machine Learning, one thing is clear: it's an iterative process. There are so many different parts of your model--how you use your data, hyperparameters, parameters, algorithm choice, architecture--and the optimal combination of all of those is the holy grail of machine learning.

To learn more about cutting-edge data science tools like Apache Kafka, check out the Strata Data Conference in San Jose, March 5-8, 2018--registration is now open. Machine learning has become mainstream, and suddenly businesses everywhere are looking to build systems that use it to optimize aspects of their product, processes or customer experience. The cartoon version of machine learning sounds quite easy: you feed in training data made up of examples of good and bad outcomes, and the computer automatically learns from these and spits out a model that can make similar predictions on new data not seen before. What could be easier, right? Those with real experience building and deploying production systems built around machine learning know that, in fact, these systems are shockingly hard to build. This difficulty is not, for the most part, the algorithmic or mathematical complexities of machine learning algorithms. Creating such algorithms is difficult, to be sure, but the algorithm creation process is mostly done by academic researchers.

To learn more about cutting-edge data science tools like Apache Kafka, check out the Strata Data Conference in Singapore, Dec. 4-7, 2017--early price ends October 20. Machine learning has become mainstream, and suddenly businesses everywhere are looking to build systems that use it to optimize aspects of their product, processes or customer experience. The cartoon version of machine learning sounds quite easy: you feed in training data made up of examples of good and bad outcomes, and the computer automatically learns from these and spits out a model that can make similar predictions on new data not seen before. What could be easier, right? Those with real experience building and deploying production systems built around machine learning know that, in fact, these systems are shockingly hard to build. This difficulty is not, for the most part, the algorithmic or mathematical complexities of machine learning algorithms. Creating such algorithms is difficult, to be sure, but the algorithm creation process is mostly done by academic researchers.