Simplify Building Machine Learning Models & Pipelines

Arrikto Data Science Development Studio for Data Scientists and Machine Learning Engineers simplifies complex environments with a single end-to-end integrated local development platform with advanced MLOps workflows and GitOps software management.

Simplify

Consistently deploy an all-in-one integrated Kubeflow environment on your laptop or in the cloud in minutes. Use your favorite frameworks, libraries, and choice of Jupyter or VS Code.

Accelerate

Automate the manual process of building Docker images & converting code to pipelines with Kale. Leverage fast NVMe storage & tuning optimization to speed model training.

Collaborate

Securely share and validate versioned machine learning data, code, and artifacts with team members across your organization without storage administrators.

Accelerate Time to Value for Data Science Projects

Today’s data science projects are plagued by delays, often caused by the sheer complexity of machine learning frameworks, platforms, data lakes, storage systems, clouds, data mobility, and working across organizational silos.

Your Data Scientists need creative flexibility to design and experiment.

DevOps teams need a consistent process to reliably scale and support the models in production.

Environment Challenges

Deploying and configuring consistent development environments to efficiently build and test models is challenging.

Maintaining frameworks and the underlying infrastructure is time-consuming and not the role of the Data Scientist.

Disjointed Workflows

Data Scientists and Machine Learning Engineers want to use familiar tools like Jupyter Notebooks and VSCode to write model code and push to a pipeline.

Building Docker containers and Kubernetes deployment configurations, and managing storage slow down experimentation.

Data Management

Data has gravity and mass. It refuses to move easily and pulls applications toward it. Connecting data, and then versioning it for reproducibility, compliance, and lifecycle management is complex.

Relying on DevOps and Storage Administrators slows down delivery.

An Industry Leader In Machine Learning

Arrikto is a leading provider of machine learning solutions. Our Data Science Development Studio enables Data Scientists and Machine Learning Engineers to accelerate their model development, training, and debugging.

Built from the ground up to deliver rich automation and collaboration;

  • A common foundation: Deliver a standardized and consistent environment across local and cloud deployments.

 

  • Integrated workflows: Enabling data scientists to rapidly build and test models with code-to-pipeline automation and pipeline visualization.

 

  • Reproducible pipelines: Train and debug models collaboratively across your team with securely shared and versioned data snapshots.

Empower Your Data Scientists

Get Started in Minutes

A complete and integrated machine learning platform deployed on your laptop or in the cloud in minutes with the push of a button. Includes Kubeflow, TensorFlow, PyTorch, XGBoost, R, Python, Kubernetes, and more within a single instance.

Optimized Model Accuracy

Save time by automating your hyperparameter tuning to identify the most accurate model configuration. Remove the error-prone and time-consuming process of manually setting, testing, and comparing results with automated workflows.

Choice of Familiar Tools

Whether you prefer Jupyter Notebooks, VSCode, or another tool, enjoy the freedom of choice of your favorite tools to speed experimentation and iteration. Focus on writing models not learning new tools.

Rich Visualization

Manage and debug your projects with visualizations of Pipelines, Experiments, and Artifacts. Quickly review to identify errors and inefficiencies for faster model development. Track jobs step by step for easy debugging.

Automated Code to Pipeline

When you’re ready to test your model, push a button to convert your code into a Kubeflow pipeline without needing to build Docker containers, upload to a registry, or write Kubernetes deployments. Focus on machine learning and not infrastructure.

Global Data Management

Select, connect, ingest, manage, version, and share data via the GUI or API. Ensure compliance with reproducible and annotated snapshots for complete provenance. Securely collaborate with colleagues and partners across clouds without the need for a storage admin.

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Democratized Intelligence Automation

Achieve your business goals faster and with fewer resources by following the proven Arrikto Simplify, Accelerate, Collaborate approach to machine learning.

Simplify

Increase productivity with integrated model building environments that contain all the required tools, frameworks, and libraries. Run in any cloud or laptop. Focus on machine learning, not infrastructure.

Accelerate

Automate code to pipeline workflows and hyperparameter tuning, along with comprehensive data management to deliver an end to end MLOps process for model building through to training and serving.

Collaborate

Easily access and securely share data across clouds and between team members. Keep unlimited annotated snapshots for training reruns and reproducibility. 

Tutorials & News

Build An End-to-End ML Workflow: From Notebook to HP Tuning to Kubeflow Pipelines with Kale

Build An End-to-End ML Workflow: From Notebook to HP Tuning to Kubeflow Pipelines with Kale

In this tutorial, we will use Kale to unify the workflow across the above components, and present a seamless process to create ML pipelines for HP tuning, starting from your Jupyter Notebook. We will use Kale to convert a Jupyter Notebook to a Kubeflow Pipeline without any modification to the original Python code. Pipeline definition and deployment is achieved via an intuitive GUI, provided by Kale’s JupyterLab extension.

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