ai capitalwiggersventurebeat is a MLOps startup that has recently announced the successful close of a $20M Series A funding round, bringing its total raised to $26.5M.

The company aims to make Machine Learning and Artificial Intelligence more accessible and easier for businesses and developers of all levels.

This article will provide an overview of and its mission to bring MLOps to the masses.

MLOps startup nabs $20M is a startup that has created machine learning operations (MLOps) technology to facilitate the automation and standardisation of machine learning tasks. MLOps is a project that merges DevOps and Machine Learning (ML). With MLOps, organisations can speed up the development of software applications powered by machine learning models while decreasing operational costs.

The startup recently secured $20 million in funding from the venture capital firm True Ventures. This capital will be utilised to further its mission of bringing MLOps to the masses and enable businesses to reap the benefits of faster customer experiences and improved product data analysis without sacrificing quality or security.

The company was founded in 2020 with a vision for helping developers quickly package, deploy, and manage their AI-driven products efficiently using its flagship product Iterative Flow Platform. The platform offers self-managed serverless workloads for developers that greatly simplify model training, deployment, A/B testing, and performance management all at once.

These features allow developers to create an end-to-end ML distribution process that automates model management from experimentation through production deployment in real-time with just one drag-and-drop action within their existing coding environment. This saves time by eliminating complex API setup procedures required when connecting model training pipelines with distributed services like Kubernetes or TensorFlow Serving while maintaining threat mitigation measures required for highly secure applications based on AI models.

Mission of is a Machine Learning Operations (MLOps) startup striving to make building, deploying, and managing ML models easier and more accessible for everyone. Their goal is to remove the technical complexities of designing, constructing, and deploying machine learning models and allow users of all levels — from entry-level ML engineers to experienced data scientists — to accelerate the model development process. Additionally, provides a cost-effective platform for enterprises that want to use this technology, allowing them to leverage existing infrastructure while automating operations and streamlining development processes.

The company recently raised $20M in funding which will be used to expand their product line, improve existing features and services, extend its reach into new markets, and further develop its applications around automated machine learning (AutoML). Moreover, financial capital gives it an advantage in creating innovative solutions for organisations who want efficient enterprise-grade MLOps tools — tools that automate model management from development to production in multiple cloud architectures with built-in system monitoring capabilities.

With these advancements and capabilities enabled by fundraising funds, looks forward to developing groundbreaking products that can support organisations in their mission to deliver accurate prediction models with as much speed and cost efficiency.

MLOps, a MLOps startup, recently announced it has raised $20M in a funding round to bring MLOps to the masses. MLOps is a way to manage the full lifecycle of Machine Learning (ML) models, from coding and testing to deployment and monitoring. As a result, it helps organisations to reduce the risk and speed up the deployment of ML models.

This article will explore MLOps, its benefits, and’s mission to make MLOps available to everyone.

What is MLOps?

MLOps (sometimes called DevOps for Machine Learning, MLDevOps or MLManaged) is an emerging practice of combining software development and machine learning (ML) operations into a single cohesive process. It allows software development teams to collaborate on, manage and deploy the entire ML lifecycle. In addition, it helps organisations realise the full potential of their data science investments by providing better visibility into how data sets are used in models, how models are deployed in production systems and how these processes continuously improve over time.

MLOps streamlines the development process while increasing model accuracy and automation, enabling data science teams to quickly iterate on experiments and develop AI-driven products. In addition, automating common data science tasks such as preparing datasets, building networks and deploying models reduces time spent on mundane tasks that could be better spent working on complex initiatives to move a company forward.

MLOps also provides organisations with operational benefits such as cost reduction through accelerated feature engineering cycles, reduced downtime due to model health/stability assurances, easier maintenance due to traceability of models, simplified model deployment with automated release pipelines and increased data governance requirements related to model deployments. Ultimately, these all contribute towards faster iterations and more reliable automated AI development processes that lead to successful outcomes while reducing manual errors at scale. mlops ai series capitalwiggersventurebeat

Benefits of MLOps

MLOps, or Machine Learning Operations, are an increasingly popular way organisations develop and deploy machine learning models in their systems. This approach to artificial intelligence (AI) enables the automation and streamlining of ML development processes including model training, validation, versioning, deployment, and monitoring. As a result, it is often seen as the bridge between DevOps (Development Operations) and ML (machine learning).

MLOps offers several benefits compared to more traditional methods of working with AI:

  • Faster deployment: With MLOps businesses can deploy their machine models much faster—often in a matter of hours as opposed to days—allowing them to stay competitive and respond quickly to customer demands.
  • Improved development quality and efficiency: Automating manual tasks such as labelling data and applying format transformations helps free up resources to focus on developing more innovative customer solutions while ensuring code quality.
  • Intuitive monitoring capabilities: Being able to set thresholds for automated decision making ensures accurate results from machines while allowing organisations to correctly monitor their AI model performance.
  • Increased agility: Making updates without downtime or disruption allows businesses to adjust their models as new trends or behaviours arise. This allows for greater agility over traditional static models, which never change after being deployed.
  • Reduced complexity & cost reduction: By automating tedious process steps such as collecting data sources & training models, companies can reduce total project cost and complexity while improving quality assurance measures throughout the process overall.

Simply put, MLOps allows organisations to generate more value from AI naturally by allowing them to respond quickly and intelligently in times of crisis or change in almost any industry faster than ever. Iterative’s mission is key to fulfilling this demand for an easier way to automate the end-to-end delivery pipeline that companies need, ensuring fast implementation of complex automated processes with vital insights into creating valuable products efficiently without sacrificing quality or security in the long run!’s MLOps Platform is a startup that has just raised $20 million to bring MLOps (machine learning operations) to the masses. Their mission is to make MLOps more accessible to organisations of all sizes, allowing them to develop and deploy ML models more quickly, securely, and cost-effectively than ever before.

In this article, we’ll take a look at what’s MLOps platform can offer. mlops 20m series capitalwiggersventurebeat

Features of’s MLOps Platform is a startup focused on bringing Machine Learning (ML) and Operations (Ops) together to provide businesses with a comprehensive MLOps platform. The platform enables users to easily deploy ML models in production, monitor them in real-time, and quickly debug errors.

The MLOps platform offers the following features:

  • Automated Feature Engineering – Leveraging the latest technologies, enables customers to quickly create new features for their machine learning models from raw data sources.
  • Model Performance Tracking & Optimization – The feature-rich dashboards and logging tools enable customers to monitor and optimise model performance over time in a central repository.
  • Real-Time Monitoring & Alerts – helps businesses stay up-to-date on model accuracy by alerting when adverse impacts are detected due to data drift or other anomalies.
  • Automate Retraining & Redeployment – Through automation, models can be retrained frequently without manual intervention and modifications can be staged before deployment into the production environment without affecting the current setup.
  • Explainability – The AI explainability dashboard helps users understand why a particular decision was made by their ML models, allowing for more effective debugging of issues that arise from time to time. This also prevents chances of model bias from entering into decisions taken by businesses based on AI output results.
  • Deployment & Orchestration – Using containerized deployments through Docker, Iterative supports simplified deployment orchestration for multiple environments like Kubernetes or AWS Sagemaker, making it easy for customers to move models into production at scale quickly and accurately.

Advantages of’s MLOps Platform is an MLOps startup revolutionising how Machine Learning (ML) and Artificial Intelligence (AI) are applied in everyday business operations. It empowers organisations to build, manage and measure machine learning models faster, cheaper and with higher quality results than ever before. By providing a comprehensive AI platform, has enabled businesses across different sectors — such as automotive, healthcare and finance — to reap the benefits of automation while minimising operational complexity.

The MLOps platform offered by offers several advantages over traditional engineering processes due to its proven combination of four key capabilities: ML-assisted development tools for building deep neural networks; model tracking and reproduction for monitoring model performance; batch scheduling for re-training models with new data quickly; as well as distributed training capabilities for efficient scaling of operations with massive datasets.

This means businesses can rapidly validate their models without repeatedly starting from scratch by streamlining end-to-end ML operations utilising all these features in a single workflow. Further, teams no longer need to spend countless hours coding & debugging different components of an experiment since everything within the platform is preconfigured so it can be reused again in future experiments with minimal effort. In addition, Iterative’s visualisations allow teams to continuously track model performance curves under different conditions or configurations so they can iterate faster on their solutions & develop targeted remedies in real time if needed. Finally, scalability is one point which sets apart Iterative’s offering from its competitors; they provide an enterprise-ready infrastructure that makes it safe & easy to scale existing models on dedicated servers while maintaining high availability throughout the infrastructure stack even under heavy load conditions such as those seen during peak seasonable hours when ML applications are put through the most rigorous tests and experiments.’s Funding, an MLOps startup, recently secured $20M in Series A financing. This is an important milestone for the company, demonstrating growing investor interest in the startup.

The company plans to use the funds to ramp up the development of its flagship product, the Iterative Platform, and expand the scope of its MLOps services.

Let’s take a closer look at’s mission and the implications of this funding. mlops ai capitalwiggersventurebeat

Overview of’s funding has recently raised $20 million in a Series A funding round led by GV (formerly Google Ventures) with participation from Kleiner Perkins, Bain Capital Ventures, Threshold Ventures (formerly DFJ Venture Capital), and Costanoa Ventures. is a startup that develops MLOps products for data scientists and machine learning practitioners to easily operationalize their models across various software development cycles. Through its suite of AI-enabled products, helps expedite the process of turning data into predictions and insights within the enterprise’s systems. In addition to these features, Iterative offers a complete suite of data science tools for deriving insights from datasets.

Iterative will use the new funding to create additional MLOps tools and expand its customer base beyond the U.S., namely in Europe, Asia Pacific, and Latin America regions. In addition, the company aims to make future development faster while providing stable product releases which can drastically reduce manual operations within the development loop process by streamlining model delivery and governance within organisations across multiple markets.

Impact of’s funding’s mission to bring Machine Learning Operations (MLOps) to the masses has just been given a major boost with the startup’s recent funding of $20 million. MLOps is a key term for companies that want to use machine learning for their business and rely on automation tools to simplify their processes and achieve desired outcomes. With MLOps becoming increasingly important,’s funding will significantly impact businesses, startups and venture capitalists investing in this space.

With this financial backing, plans to accelerate their development of products such as their self-service data platform that simplifies the deployment of ML models and extend their offering into AI-driven insights on customer dynamics and decision making. The company also intends to invest in its engineering teams to bolster automated analytics and machine learning capabilities which will further enhance the performance of enterprise applications and create new opportunities for data-driven workflows.

One of the biggest potential impacts from the investment is that it might significantly reduce the complexity gap between advanced Machine Learning projects and traditional DevOps tasks by making MLOps more accessible and easier to use even for small businesses operating with limited knowledge or resources on hand. Ultimately, it could provide all companies working in an ever-changing landscape access to advanced technologies that bring operational excellence through Machine Learning automation processes.

tags = MLOps,, nabs $20M, developing data science and AI engineering workflows, Data Version Control, mlops ai 20m capitalwiggersventurebeat, mlops 20m capitalwiggersventurebeat, development based on data and metrics, machine learning, information technology operations, mlops series capitalwiggersventurebeat

About Author