Best Data Management Software for MLflow

Google Cloud is a cloud-based service that allows you to create anything from simple websites to complex applications for businesses of all sizes. New customers get $300 in free credits to run, test, and deploy workloads. All customers can use 25+ products for free, up to monthly usage limits. Use Google's core infrastructure, data analytics & machine learning. Secure and fully featured for all enterprises. Tap into big data to find answers faster and build better products. Grow from prototype to production to planet-scale, without having to think about capacity, reliability or performance. From virtual machines with proven price/performance advantages to a fully managed app development platform. Scalable, resilient, high performance object storage and databases for your applications. State-of-the-art software-defined networking products on Google’s private fiber network. Fully managed data warehousing, batch and stream processing, data exploration, Hadoop/Spark, and messaging.

Dagster is a next-generation orchestration platform for the development, production, and observation of data assets. Unlike other data orchestration solutions, Dagster provides you with an end-to-end development lifecycle. Dagster gives you control over your disparate data tools and empowers you to build, test, deploy, run, and iterate on your data pipelines. It makes you and your data teams more productive, your operations more robust, and puts you in complete control of your data processes as you scale. Dagster brings a declarative approach to the engineering of data pipelines. Your team defines the data assets required, quickly assessing their status and resolving any discrepancies. An assets-based model is clearer than a tasks-based one and becomes a unifying abstraction across the whole workflow.

Log, store, query, display, organize, and compare all your model metadata in a single place. Know on which dataset, parameters, and code every model was trained on. Have all the metrics, charts, and any other ML metadata organized in a single place. Make your model training runs reproducible and comparable with almost no extra effort. Don’t waste time looking for folders and spreadsheets with models or configs. Have everything easily accessible in one place. Reduce context switching by having everything you need in a single dashboard. Find the information you need quickly in a dashboard that was built for ML model management. We optimize loggers/databases/dashboards to work for millions of experiments and models. We help your team get started with excellent examples, documentation, and a support team ready to help at any time. Don’t re-run experiments because you forgot to track parameters. Make experiments reproducible and run them once.

TrueFoundry is a Cloud-native Machine Learning Training and Deployment PaaS on top of Kubernetes that enables Machine learning teams to train and Deploy models at the speed of Big Tech with 100% reliability and scalability - allowing them to save cost and release Models to production faster. We abstract out the Kubernetes for Data Scientists and enable them to operate in a way they are comfortable. It also allows teams to deploy and fine-tune large language models seamlessly with full security and cost optimization. TrueFoundry is open-ended, API Driven and integrates with the internal systems, deploys on a company's internal infrastructure and ensures complete Data Privacy and DevSecOps practices.

DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training. Consistent setup across team, promote sharing and collaboration, Azure scale and management, Near-Zero Setup, full cloud-based desktop for data science. Quick, Low friction startup for one to many classroom scenarios and online courses. Ability to run analytics on all Azure hardware configurations with vertical and horizontal scaling. Pay only for what you use, when you use it. Readily available GPU clusters with Deep Learning tools already pre-configured. Examples, templates and sample notebooks built or tested by Microsoft are provided on the VMs to enable easy onboarding to the various tools and capabilities such as Neural Networks (PYTorch, Tensorflow, etc.), Data Wrangling, R, Python, Julia, and SQL Server.

The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.

Kedro is the foundation for clean data science code. It borrows concepts from software engineering and applies them to machine-learning projects. A Kedro project provides scaffolding for complex data and machine-learning pipelines. You spend less time on tedious "plumbing" and focus instead on solving new problems. Kedro standardizes how data science code is created and ensures teams collaborate to solve problems easily. Make a seamless transition from development to production with exploratory code that you can transition to reproducible, maintainable, and modular experiments. A series of lightweight data connectors is used to save and load data across many different file formats and file systems.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

H2O.ai is the open source leader in AI and machine learning with a mission to democratize AI for everyone. Our industry-leading enterprise-ready platforms are used by hundreds of thousands of data scientists in over 20,000 organizations globally. We empower every company to be an AI company in financial services, insurance, healthcare, telco, retail, pharmaceutical, and marketing and delivering real value and transforming businesses today.

At conDati we apply machine learning and data science techniques to developing out-of-the-box solutions that transform massive volumes of customer, event, and transaction data into accessible insights and usable information. Watch how quickly you can visualize your campaign performance from summary level to individual campaigns with conDati. conDati collects and blends all your important marketing data to create a unified data asset that is used to create dashboards, models and alerts across every channel. Revenue, cost, and metrics data (Sends, Opens, Impressions, Clicks, Transactions…) are available for every campaign by minute, hour and day. Results YTD are combined with the Model Forecast for the rest of the year giving a constantly updated Forecast for Revenue, Costs and other metrics.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Monitor your data health and pipeline performance. Gain unified visibility for pipelines running on cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. An observability platform purpose built for Data Engineers. Data engineering is only getting more challenging as demands from business stakeholders grow. Databand can help you catch up. More pipelines, more complexity. Data engineers are working with more complex infrastructure than ever and pushing higher speeds of release. It’s harder to understand why a process has failed, why it’s running late, and how changes affect the quality of data outputs. Data consumers are frustrated with inconsistent results, model performance, and delays in data delivery. Not knowing exactly what data is being delivered, or precisely where failures are coming from, leads to persistent lack of trust. Pipeline logs, errors, and data quality metrics are captured and stored in independent, isolated systems.

lakeFS enables you to manage your data lake the way you manage your code. Run parallel pipelines for experimentation and CI/CD for your data. Simplifying the lives of engineers, data scientists and analysts who are transforming the world with data. lakeFS is an open source platform that delivers resilience and manageability to object-storage based data lakes. With lakeFS you can build repeatable, atomic and versioned data lake operations, from complex ETL jobs to data science and analytics. lakeFS supports AWS S3, Azure Blob Storage and Google Cloud Storage (GCS) as its underlying storage service. It is API compatible with S3 and works seamlessly with all modern data frameworks such as Spark, Hive, AWS Athena, Presto, etc. lakeFS provides a Git-like branching and committing model that scales to exabytes of data by utilizing S3, GCS, or Azure Blob for storage.

Enabling all enterprise’s AI/ML initiatives to result in consistent and positive impact. Data scientists deserve a solution that makes all their experiments reproducible, every asset discoverable and simplifies knowledge transfer. Managers deserve a dedicated data science solution. to secure knowledge, automate reporting and simplify reviews and processes. Vectice is on a mission to revolutionize the way data science teams work and collaborate. The goal is to ensure consistent and positive AI/ML impact for all organizations. Vectice is bringing the first automated knowledge solution that is both data science aware, actionable and compatible with the tools data scientists use. Vectice auto-captures all the assets that AI/ML teams create such as datasets, code, notebooks, models or runs. Then it auto-generates documentation from business requirements to production deployments.