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Wesley Carter
Wesley Carter

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

Today at Google I/O, we announced the general availability of Vertex AI, a managed machine learning (ML) platform that allows companies to accelerate the deployment and maintenance of artificial intelligence (AI) models. Vertex AI requires nearly 80% fewer lines of code to train a model versus competitive platforms1, enabling data scientists and ML engineers across all levels of expertise the ability to implement Machine Learning Operations (MLOps) to efficiently build and manage ML projects throughout the entire development lifecycle.

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

Today, data scientists grapple with the challenge of manually piecing together ML point solutions, creating a lag time in model development and experimentation, resulting in very few models making it into production. To tackle these challenges, Vertex AI brings together the Google Cloud services for building ML under one unified UI and API, to simplify the process of building, training, and deploying machine learning models at scale. In this single environment, customers can move models from experimentation to production faster, more efficiently discover patterns and anomalies, make better predictions and decisions, and generally be more agile in the face of shifting market dynamics.

In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). By taking advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you use GPU-based hardware in the cloud according to your needs. You can switch to a GPU-based VM when you're training large models, or when you need high-speed computations while keeping the same OS disk. You can choose any of the N series GPUs enabled virtual machine SKUs with DSVM. Note GPU enabled virtual machine SKUs are not supported on Azure free accounts.

Multiple frameworks: Cortex can be deployed in all Python-based machine learning frameworks like TensorFlow, PyTorch, scikit-learn, Keras and other models. It has got the utmost compatibility that works with all deployment infrastructures.

According to the founders of Cortex Lab, they catch up with the idea of developing a uniform API to deploy the machine learning models quickly over the cloud. For that, they took all the open-source tools like Tensorflow, Docker and Kubernetes. Then they combined all of them with the AWS service like CloudWatch, EKS(Elastic Kubernetes Service) and S3 (Simple Storage Service) to achieve a single API to deploy any 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.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.

This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). Lime is based on the work presented in this paper (bibtex here for citation).

MLRun is an end-to-end open-source MLOps orchestration framework to manage and automate your entire analytics and machine learning lifecycle, from data ingestion, through model development to full pipeline deployment. MLRun eases the development of machine learning pipelines at scale and helps ML teams build a robust process for moving from the research phase to fully operational production deployments.

PrimeHub, an open-source pluggable MLOps platform on the top of Kubernetes for teams of data scientists and administrators. PrimeHub equips enterprises with consistent yet flexible tools to develop, train, and deploy ML models at scale. By improving the iterative process of data science, data teams can collaborate closely and innovate fast.

Spell is an end-to-end deep learning platform that automates complex ML infrastructure and operational work required to train and deploy AI models. Spell is fully hybrid-cloud, and can deploy easily into any cloud or on-prem hardware.

Lee: Probably the biggest one is that the only thing standing between you and your dream ML application is a team of data scientists. In actuality, a number of factors need to come together in order to achieve a successful machine learning implementation.

The third challenge is the skills gap. Again, the growth in artificial intelligence has led to a shortage of data scientists and machine learning experts. You may not be able to hire all the data scientists you need, so you should probably focus your energy on upskilling the level of your current workforce and/or leveraging outside resources.

And a fourth challenge is the tendency to think you have to develop everything on your own from scratch, when a cloud platform like AWS can provide many of the necessary tools and infrastructure needed for data access and machine learning model development, testing, and deployment. By taking advantage of these existing tools and services, you can focus on bringing your differentiated, value-added contributions, such as your domain and industry expertise and any special insights that you have, to solve the problem at hand.

ApacheTVM is an open-source machinelearning compiler framework for CPUs, GPUs, and ML accelerators. It aimsto enable ML engineers to optimize and run computations efficiently onany hardware backend. In particular, it compiles ML models into minimumdeployable modules and provides the infrastructure to automaticallyoptimize models on more backends with better performance.

In machine learning, however, monitoring plays a different role.First off, bugs in ML systems often lead to silent degradations inperformance. Furthermore, the data that is monitored in ML isliterally the code used to train the next iteration of models.

By definition, MLOps tools are single-use software or end-to-end platform that helps you execute a stage or an entire machine learning project. All the MLOps tools serve a particular purpose, but if you look at the bigger picture, they collectively work towards solving a real-world problem through data science.

The first stage of any machine learning project is deciding on the framework we will use. ML frameworks let data scientists and developers build and deploy models faster. Let us take a look at some of the best MLOps tools available to us in this phase.

PyTorch was created inside the Facebook research lab by Facebook AI Research in 2017. Since then, it has become quite popular with data scientists and machine learning engineers because of its flexibility and speed. is a framework that lets data scientists train models on the cloud at scale. Founded by William Falcon and Luis Capelo in 2019, it enables people without ML engineering or MLOps experience to develop ML models. is a git-based MLOps tool for data scientists and ML engineers with DVC (data version control) and CML (continuous machine learning). was created by Dmitry Petrov while working as a Microsoft data scientist, aiming to bring engineering practices to data science and machine learning.

MLflow is an open source platform built on an open interface philosophy helping us manage certain aspects of the machine learning workflow. So, any data scientists working with any framework, supported or unsupported, can use the open interface, integrate with the platform, and start working.

Model deployment in machine learning is the stage where we deploy our trained models into production. This enables the model to serve its purpose of predicting results as it was intended to do so. For a complete guide to model deployment, you can read our blog here.

Creators of Apache TVM spun out of the University of Washington and created OctoML to help companies to develop and deploy deep learning models in specific hardware as needed. OctoML supports a variety of machine learning frameworks, such as PyTorch and TensorFlow.

Seldon is an open-source platform that helps data scientists and ML engineers to solve problems faster and effectively through audit trails, advanced experiments, CI/CD, scaling, model updates, and more. In addition, Seldon converts ML models or language wrappers into containerized product microservices.

Wallaroo is a MLOps tool that helps in model deployment. The platform consists of four main components, MLOps, process engine, data connectors, and audit and performance reports. Wallaroo allows data scientists to deploy models against live data to testing, staging, and production using machine learning frameworks.

Arthur AI is a machine learning performance platform that monitors model performance, bias detection, and explainability. The platform enables data scientists, ML engineers, and developers to detect errors, data drift, and anomalies.

These platforms offer a comprehensive solution covering the entire machine learning pipeline spectrum. In addition, these platforms provide a one-stop solution for all, from data and pipeline, model experimentations, and hyperparameter tuning, to deployment and monitoring. is a complete MLOps platform that enables data scientists and ML engineers to build, deploy, and allocate jobs to their machine learning projects. The four core components of NimbleBox are their Build, Jobs, Deploy and Manage components. These features let anyone start their machine learning project with just a few lines of code and push their model deployment in the easiest ways.


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