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 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 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.
