Leading MLOps Tools Are The Next Frontier Of Scaling AI In The Enterprise

By Mark Minevich, Contributor
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Machine Learning Operations (MLOps) is on the rise as a critical technology to help to scale machine learning in the enterprise. According to McKinsey, by 2030, ML could add up to 13 trillion dollars back into the global economy by enabling workers in all sectors to improve their output. Furthermore, MarketWatch indicates that, in 2021, the global MLOps market size will be USD million and it is expected to reach USD million by the end of 2027, with a CAGR during 2021-2027. According to IBM  by 2023, 70% of AI workloads will use application containers or be built using a serverless programming model, necessitating a DevOps culture. What’s more, according to Algorithmia, 85% of machine learning models never make it to production. For businesses, creating machine learning applications, managing those models and putting them into action is challenging. Different companies, such as DataRobot, have emerged as top machine learning operations tool enablers for the industry to handle these challenges. 

Processing, implementing and deploying machine learning models requires specific tools that can solve challenges in the process. The challenge of getting data from aa data to decisions is made more accessible by applying various operations on-device or in the cloud as needed. To do this at scale, businesses need a platform to add support for new ML frameworks through open interfaces. There are several ways to add or remove models and processes.

The leading machine learning operations tools for enterprise are:

DataRobot

BRAZIL - 2021/05/11: In this photo illustration the DataRobot logo seen displayed on a smartphone screen. (Photo Illustration by Rafael Henrique/SOPA Images/LightRocket via Getty Images) SOPA Images/LightRocket via Getty Images

DataRobot specializes in automated machine learning for businesses, which eases the process of model development and upkeep within an app or platform. DataRobot’s suite of products also gives users access to a pre-trained model store. DataRobot offers several features that help businesses get started with ML data pipelines and operations, including a visual debugger for debugging machine learning code.

DataRobot's competitive advantage is the ease of use for non-technical users. DataRobot's user interface enables ML beginners to input data and build a model without in-depth coding knowledge or background. Some unique solutions include the ability to run models in a web browser, prototyping tools to test data pipelines and algorithms before launching them in production, and the ability of DataRobot’s AutoML suite to choose between hundreds of machine learning algorithms automatically. The model store can add more than 200 open-source frameworks from TensorFlow, SciKit-Learn, XGBoost, PyTorch, and TensorRT.

Some of DataRobot's top customers are Deloitte, Panasonic, US Bank, Lenovo, among others. An example success story is a cross-functional team at Panasonic that used DataRobot to build predictive maintenance models that identified and repaired equipment problems up to 9 days earlier than their previous method. This reduced the number of machine failures and increased productivity by 5%.

H2O.ai

H2O is a complete platform for data science and machine learning that enables companies to implement end-to-end workflows from data preparation to model building with one consistent SDK. The company also offers support in developing, deploying and managing models.

H2O's automation engine enables businesses to create, deploy and manage machine learning applications in a visual environment. These environments offer pre-configured workflows for common machine learning tasks like feature engineering, model training and deployment. This is where the competitive advantage comes: it speeds up results for non-technical users who can run experiments from one interface that includes data preparation with automated feature engineering and model training with XGBoost. H2O's platform supports any data type, scales to large clusters of GPUs and integrates with Spark, Python, R and other languages.

Some companies using H20 include global leaders in retail, banking, telecommunications and insurance. An example success story is a telecom company that wanted to analyze customer experience data to predict potential churners. The telecom company reduced churn by 10% and increased the number of customers contacted per month from 30,000 to 100,000.

Amazon SageMaker

Close-up of sign with logo on facade of the regional headquarters of ecommerce company Amazon in the Silicon Valley town of Sunnyvale, California, October 28, 2018. (Photo by Smith Collection/Gado/Getty Images) Getty Images

Amazon SageMaker is a platform for data scientists. It was built to address businesses’ challenges in getting from raw data to production-ready machine learning models. Amazon’s cloud software enables enterprises to implement end-to-end workflows and create, train, deploy and manage machine learning applications. This eliminates the need for companies to maintain their internal data, science teams.

Amazon SageMaker's competitive advantage is that it offers pre-configured templates for deep learning, reinforcement learning and multi-cloud training across multiple frameworks, like Apache MXNet, TensorFlow and others. Amazon also provides custom configurations for businesses that need a more specific type of model or tool. With support for feature engineering and automatic hyperparameter tuning, Amazon SageMaker speeds up building a model and reduces time spent debugging.

Amazon SageMaker's biggest customers range from Toyota to Nielsen, ExxonMobil to Epic Games. An example success story is Nielsen, which migrated its National Television Audience Measurement platform to AWS and built a new, cloud-native television rating platform that allowed the company to grow its measurement capabilities from measuring 40,000 households daily to more than 30 million households each day. 

MLFlow

MLFlow is a machine learning platform that enables collaborative experimentation and tracking. This speeds up the entire process of building, training and deploying models across data teams. MLFlow has an open-source lightweight library for Python developers who want to track experiments on TensorFlow, SciKit-Learn and PyTorch via one API. The company also offers a server product that allows teams to track experiments on Spark via one API.

MLFlow's main competitive advantage is allowing employees outside of the data science team to collaborate on building, training and deploying models. The platform also speeds up time for deploying models and tracking experiments across tools.

Some companies that use MLFlow include Microsoft, Zillow, Facebook, Booking.com and Genpact. For example, Microsoft supports open-source MLflow in Azure Machine Learning to provide its customers with maximum flexibility. This means developers can use the standard MLflow tracking API to track runs and deploy models directly into the Azure Machine Learning service.

IBM Watson Machine Learning

HANOVER, GERMANY - MARCH 02: Visitors check out a slimmed down version of the IBM Watson supercomputer recently featured on the Jeopardy television game show at the IBM stand at the CeBIT technology trade fair on March 2, 2011 in Hanover, Germany. CeBIT 2011 will be open to the public from March 1-5. (Photo by Sean Gallup/Getty Images) Getty Images

IBM Watson Machine Learning allows businesses to deploy self-learning models at scale, allowing AI to be used in applications and is available for free or with a price based on workload.

The main competitive advantage of IBM Watson Machine Learning is that it provides the possibility to train, deploy and manage models according to a company’s specific requirements. The platform supports the deployment of models on any infrastructure (cloud or on-premises) for many businesses.

IBM Watson  Studio is the ideal platform for companies to build their multicolored ModelOps practice. It provides an integrated development environment that allows developers to use the latest cognitive computing tools from within a single package, also part of IBM Machine Learning. This means businesses can develop, build and train models in one place and deploy them on any framework like TensorFlow, SparkML or H20.

An interesting case study is American Airlines. American Airlines needed a new technological platform and a different method of development that would help it provide digital self-service functionality and customer value more swiftly throughout its business. By providing the airline with a common platform, IBM assists it in moving some of its critical applications to the IBM Cloud and using new methods to develop creative apps quickly while improving customer experiences.

Algorithmia 

Algorithmia is a single platform that covers all aspects of machine learning operations (MLOps). It allows for collaboration between data experts and engineers on complicated applications. 100,000 people are using the service, including UN staff members and Fortune 500 businesses.

The company’s main competitive advantages include the ability to ramp up speed and productivity by streamlining data science operations and reducing costs by bringing data science operations in-house. The platform also allows developers to automate data science tasks with code. It enables the creation of workflows for predictive apps using standard tools like Jupyter Notebooks, RStudio, Apache Spark and TensorFlow via a simple drag-and-drop interface.

Customers of Algorithmia include Tevec, EY and Github. According to EY Partner Carl Case, EY successfully used Algorithmia's MLOps solution: “We’ve reduced false positives in institutional systems by 40-60%, sometimes more, and the real benefit of working with Algorithmia has been taking deployment timelines down and getting models to production.”

MLOps tools are essential for enterprises that want to turn their valuable datasets into actionable insights at the pace of digital transformation. These tools focus on model management and deployment, both to the cloud and device. In addition, there is also support for new frameworks as they are released to enable businesses to handle ongoing machine learning operations. The significance of these tools is only expected to grow as enterprises apply machine learning at scale. Lastly, MLOps should leave businesses feeling empowered to test and run their models, eliminating errors and misfires.


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