Cortex Labs Helps Data Scientists Deploy Machine Learning Models In The Cloud

[Dulce Gleitz]

As enterprises grow their investments in data platforms, they increasingly want to go beyond using data for internal analytics and start integrating predictions from machine learning (ML) models to create a competitive advantage for their products and services. For example, financial institutions deploy ML models to detect fraudulent transactions in real-time, and retailers use ML models to personalize product recommendations for each customer.

Cortex enables engineers and data scientists to deploy ML models in production without worrying about DevOps or cloud infrastructure. Companies from cybersecurity, biotechnology, retail, and other industries use Cortex to scale production ML workloads reliably, securely, and cost-effectively.

Cortex Labs helps data scientists deploy machine learning models in the cloud

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Cortex Labs helps organizations put machine learning models into production on the cloud, much like AWS SageMaker. Its software as a service (SaaS) offering raises the abstraction level on machine learning deployment, thereby reducing the need for data scientists and machine learning engineers to know the ins and outs of operating core components of the cloud stack, such as using containers, Kubernetes, and Nvidia GPU drivers.

MLflow is a platform with a comprehensive approach to managing the ML lifecycle, from data preparation to model deployment. It allows data scientists to track experiments, package and share code, and manage models in a scalable way.

ZenML provides a streamlined solution for managing ML workflows. Its modular pipelines, automated data preprocessing, model management, and deployment options work together to simplify the complex machine learning process.

MLReef is a collaboration platform for machine learning projects. It offers tools and features that help everyone involved team up to work on machine learning projects and their key stages, such as version control, data management, and model deployment.

MLRun is yet another platform for building and running machine learning workflows. With MLRun, one can automate their machine learning pipelines, delegating to the tool data ingestion, preprocessing, model training, and deployment.

CML is a platform for building and deploying ML models in the CI/CD pipeline. CML also takes the hassle of automating data ingestion and model deployment, ultimately making it easier to manage and iterate on machine learning projects and improving development speed and quality.

Cortex Lab helps deploy machine learning models at scale, taking care of automatic scaling, monitoring, and alerts. Cortex Lab supports a variety of machine learning frameworks and enables easy integration with cloud infrastructure.

H2O AutoML automates the process of training, building, optimizing, and deploying models. It uses algorithms to tackle machine-learning problems, like predicting outcomes or classifying data.

Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading MLOps, open-source interoperability, and integrated tools.

Oracle Cloud Machine Learning makes it easier to build, train, deploy, and manage custom learning models. These services deliver data science capabilities with support from favorite open source libraries and tools, or through in-database machine learning and direct access to cleansed data

Databricks has acquired San Francisco-based machine learning operations (MLOps) tech startup Cortex Labs. The total amount of the deal was not disclosed. Headquartered in San Francisco, Cortex helps data scientists and engineering teams deploy native machine learning models in the cloud.

Cortex will also enable Databricks customers to scale production ML workloads reliably, securely, and cost-effectively by helping their data engineers deploy ML models in production without worrying about DevOps or cloud infrastructure.

CEO Omer Spillinger and David Eliahu founded Cortex Labs when they were both students at Berkeley. It all started when they observed that one of the problems around creating machine learning models was finding a way to deploy them. The duo eventually was able to create a mousetrap to address some of the problems plaguing data scientists who are not experts in infrastructure.

Deployment: In ML systems, deployment isn't as simple as deploying anoffline-trained ML model as a prediction service. ML systems can requireyou to deploy a multi-step pipeline to automatically retrain and deploymodel. This pipeline adds complexity and requires you to automate stepsthat are manually done before deployment by data scientists to train andvalidate new models.

Many teams have data scientists and ML researchers whocan build state-of-the-art models, but their process for building and deploying MLmodels is entirely manual. This is considered the basic level of maturity, orlevel 0. The following diagram shows the workflow of this process.

Disconnection between ML and operations: The process separates datascientists who create the model and engineers who serve the model as aprediction service. The data scientists hand over a trained model as anartifact to the engineering team to deploy on their APIinfrastructure. This handoff can include putting the trained model in astorage location, checking the model object into a code repository, oruploading it to a models registry. Then engineers who deploy the model needto make the required features available in production for low-latencyserving, which can lead totraining-serving skew.

MLOps is fundamental. Machine learning helps individuals and businesses deploy solutions that unlock previously untapped sources of revenue, save time, and reduce cost by creating more efficient workflows, leveraging data analytics for decision-making, and improving customer experience.

Operationalizing machine learning requires a lot of engineering. For a smooth machine learning workflow, each data science team must have an operations team that understands the unique requirements of deploying machine learning models.

Fabric provides a unique data and compute infrastructure that simplifies data access and helps monetize your cloud, big data, and machine learning investments. Fabric helps clients host their data, models, compute, and runtime services where they need it, within their enterprise or across multiple clouds. Fabric improves data collection, organization, and analysis as well as cross-functional collaboration and governance for machine learning and non-machine learning projects.

A high-profile emerging cloud AI company, DataRobot provides the experienced data scientist with a platform for building and deploying machine learning models. The software helps business analysts build predictive analytics with no knowledge of machine learning or programming and uses automated ML to build and deploy accurate predictive models quickly.

China-based Baidu is a company with a focus on AI and the cloud. Baidu supports AI platform-as-a-service (PaaS) and AI SaaS solutions across many industries, such as transportation, finance, manufacturing, and media. To help their customers, Baidu uses AI, machine learning, deep learning, language processing, video, and data analysis. Baidu is mostly used by developers.

San Francisco-based Numerai is a financial AI company that manages an institutional grade global equity strategy for investors. Using machine learning to transform and regulate their global network of data scientists. Numberai created the first encrypted data science tournament for stock market predictions.

Clarifai is an image recognition platform that helps users organize, filter, and search their image database. Images and videos are tagged, teaching the technology to find similarities in images. Its AI solution is offered via mobile, on-premises, or API interfaces. Beyond image recognition, Clarifai also offers solutions in computer vision, natural language processing, and automated machine learning.

AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI-tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker ML tools available across multiple integrated development environments (IDEs). You can store and share your data without having to build and manage your own servers. This frees you up to quickly get you or your organizations started to collaboratively build and develop your ML workflow. The provided SageMaker managed ML algorithms are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching with a few clicks from the SageMaker console.

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