A recent announcement of Iterative raising $20M for its MLOps platform has sparked conversations about the market opportunity for MLOps.
MLOps (or DevOps for Machine Learning) is an emerging discipline of ML that aims to enable the operationalization of ML models, and enable the data scientists to take a model from prototyping to deployment in an automated and agile way.
In this article, we will look at the market opportunity, current trends, and potential use cases of MLOps.
Iterative raises $20M for its MLOps platform
MLOps, or ML Operations, is a rapidly growing Artificial Intelligence (AI) and Machine Learning (ML) segment. It is focused on automating the process of building, testing, deploying and monitoring ML models. MLOps provides data scientists with efficient tools to build and manage complex ML pipelines without requiring in-depth knowledge of software engineering principles. By automating the end-to-end lifecycle of developing an AI/ML model from conception to deployment, MLOps enables enterprises to increase their speed-to-market for AI/ML products.
The emergence of MLOps has created an opportunity for companies to create solutions tailored to helping organisations deploy complex AI/ML systems quickly and efficiently. Iterative Solutions is one such company that recently secured USD20 million in Series B funding for its innovative cloud-based MLOps platform. The platform provides a comprehensive suite of tools which supports fully automated software delivery pipelines from data collection and cleaning to model optimization, validation and deployment – enabling enterprises to optimise their machine learning life cycles while improving team productivity.
Market Opportunity for MLOps
MLOps, or Machine Learning Operations, is quickly becoming a key technology for enterprise organisations looking to leverage the power of Artificial Intelligence (AI). It is a set of processes and tools that help organisations manage their machine learning workflow, from ideation to production. In addition, MLOps provides an environment where ML experiments can be integrated into production systems and monitored.
The promise of MLOps is significant – it enables organisations to quickly rotate through machine learning model iterations to maximise their investments in AI. This has resulted in venture capital firms seeing the large potential demand for MLOps solutions, with Iterative raising $20M in 2020 for its MLOps platform.
This indicates that market opportunity for MLOps remains strong, particularly given that many companies are still trying to make strides towards fully implementing machine learning capabilities into their production systems. Furthermore, with more investment flowing into promising companies such as Iterative, there is no doubt that further technology development will continue rapidly shortly.
Iterative’s MLOps Platform
Iterative, a MLOps software company, recently raised $20M to further develop its MLOps platform. This round of funding comes as many companies increasingly turn to MLOps to streamline their machine learning operations and allow for faster development cycles.
In this article, we will look at the market opportunity for MLOps and how Iterative is positioning itself to take advantage of that.
Overview of Iterative’s MLOps Platform
Iterative, a startup specialising in Machine Learning (ML) operations (MLOps), recently announced it had secured $20 million in Series A funding. This investment will enable Iterative to further develop its platform, which helps engineers and data scientists deploy machine learning models faster and with more control.
Moreover, Iterative’s MLOps platform simplifies model training and deployment while providing increased visibility into key metrics used to monitor and evaluate model performance. By providing these benefits, Iterative can uniquely meet the MLOps needs of organisations that have embraced ML/AI technology by facilitating the creation of data products.
Iterative’s platform optimises the experience for developers working with advanced AI tools – like TensorFlow, PyTorch, Scikit-learn, and more – by automating the development process from data ingestion and annotation to model optimization and deployment. It also includes a range of performance improvements that address safety-critical issues such as latency, throughput delays, memory usage spikes, debugging efforts for broken pipelines, etc. It also features collaborative features that enable team-wide collaboration for building reusable machine learning pipelines across development cycles and feature engineering workflows for continual optimization efforts.
The platform provides an intuitive graphical user interface (GUI) which enables enterprises to easily build complex ML/AI models without extensive programming knowledge or costly trial & error experimentation of different algorithms. By allowing developers of all levels access to its platform through self-service controls organisations can now save precious time & resources while taking advantage of a greater range of functionalities – from version control & feature engineering built from bulk annotation APIs to real-time insights on model performance & full audit trail availability at any given period for better compliance capabilities for their teams requirements when deploying predictive models into production environments across multiple languages & platforms or tiers within their architecture.
Benefits of Iterative’s MLOps Platform
Iterative’s MLOps platform aims to enable companies across various industries to easily and rapidly deploy machine learning systems in production environments. By adopting this cutting-edge technology, organisations can leverage AI and analytics to increase efficiency, reduce costs, and stay ahead of their competition.
The Iterative platform empowers teams to quickly bring AI models from development into production. Additionally, it provides the ability for continuous improvement with real-time feedback that allows for rapid iterations on model architectures, datasets and algorithms. This real-time data collection results in better understanding each set of variables’ impact on system performance. With this information, teams are empowered with insights to keep models updated with the latest industry standards.
MLOps from Iterative also streamlines the processes around deployment times and automated fine tuning for analytics pipelines, improving accuracy. Automated deployments mean fewer manual deployments, resulting in simpler rollbacks should issues arise so organisations can keep their systems running safe and unstoppably. In addition, by deploying MLOPS, teams have an easier time quickly optimising development cycles while simultaneously reducing time to market by leveraging powerful automation tools.
At its core, Iterative’s MLOps platform offers organisations numerous benefits including scalability through cloud deployment support; cross-language compatibility; Open API services which allow access from different programming languages; used case specific libraries which simplify model creation process; automated data wrangling which accelerates testing; and security auditing protocols designed per industry regulations such as GDPR which helps maintain compliance regulatory agencies like PCI compliance policies among many others. These capabilities help ensure that enterprise applications can run securely at scale while meeting their privacy requirements cost effectively and promptly over wide geographic areas or platforms, saving businesses money and labour costs.
Market Analysis
The recent investment of $20M by Iterative into its MLOps platform demonstrates the potential of this growing space. With its ability to automate the machine learning (ML) lifecycle, MLOps enables organisations to benefit from an agile and cost-effective approach to ML.
In this article we’ll explore the market opportunity for MLOps and its potential impact on businesses.
Market Size and Growth
The market opportunity for MLOps is one of immense size and rapid growth. There is estimated to be a 300% increase in demand for MLOps tools through 2024, with the potential to grow even more significantly in the years ahead. Market Size engineers suggest that businesses can make at least $2 billion from its adoption.
MLOps has shown tremendous promise in helping companies make their data-driven decisions faster and more efficiently, increasing ROI and helping firms remain competitive within their respective markets. The market opportunity for MLOps lies in the need of businesses to move away from manual DevOps processes, where coding and feature implementations require time and resources to deploy successfully.
MLOps helps automate these processes by reducing the steps required to spin up an operational machine learning environment, thereby streamlining the workflow with minimal downtime during deployments. The Iterative’s latest round of funding also reinforces this market opportunity further. With $20 million secured by the startup, investors are betting on MLOps becoming an integral part of tomorrow’s competitive corporate landscape. As such, it would bode ill for companies not taking advantage of this massive potential soon enough.
Market Segmentation
Market segmentation is dividing a market into distinct groups of buyers with different needs, characteristics, or behaviours, and who might require separate products or marketing mixes. Segmenting a market can provide opportunities to target customers more effectively and increase your exposure in the market.
When considering the potential of MLOps, it is important to understand the breakdown and specific needs of companies interested in leveraging MLOps. Market segmentation can help identify potential users to better target their strategies.
The most common market segment criteria are: industry/sector, company size, geography, job role/title and data science maturity score. These segments can provide insight into which verticals are likely to experience accelerated growth due to MLOps adoption, enabling companies to develop an effective go-to-market strategy that aligns with customer’s needs and requirements from MLOps applications.
At its core, Iterative’s $20M fundraising enables the company’s mission to improve on existing issues around automation for enterprises by providing key operational elements such as model management and retraining. Additionally, with this substantial influx of capital Iterative can build a comprehensive suite of tools for data scientists ensuring successful deployments throughout various industries potentially experiencing disruption due to automation technology such as financial services, healthcare or insurance.
Market Trends
There has been a rising demand for MLOps in the market in recent years. MLOps, or machine learning operations, is an emerging practice which focuses on streamlining and automating the delivery of machine learning models from development to production. Companies are increasingly investing in MLOps solutions to reduce errors due to manual tasks and fast-track their time-to-market with newer developments.
According to market intelligence firm Mordor Intelligence, the global MLOps market will grow at a compound annual growth rate (CAGR) above 50% between 2020 and 2025. This surge in demand has led venture capital firms to increase their investments in MLOps startups and platforms. For instance, Iterative raised $20M for its platform for companies to deploy ML models faster without sacrificing accuracy, reliability or security.
The expanding base of cloud service and software providers is anticipated to drive the growth of the MLOps market;. In contrast, features such as scalability, cost efficiency and flexibility offered by cloud services will act as key driving forces in the forecast period. Furthermore, industry verticals such as healthcare & life sciences, defence & government agencies have significantly embraced digital transformation initiatives involving adopting cloud-enabled technologies such as Machine Learning Operations, resulting in very large development budgets by automated enterprises worldwide.
Investment in MLOps
Recent investments in MLOps have increased, with Iterative raising $20M for its MLOps platform. This funding shows the growing potential for MLOps in the market.
By leveraging MLOps to streamline machine learning pipelines across the different stages of deploying machine learning models, companies can increase their agility in creating and updating their machine learning models.
In this article, we will explore the market opportunity for MLOps and discuss how companies can capitalise on this trend.
Overview of Iterative’s $20M Investment
Iterative, the MLOps AI platform provider, recently announced a $20 million Series A funding round led by Battery Ventures. Iterative’s AI-driven automation platform is designed to enable enterprises to manage their machine learning models in an automated and optimised way.
The platform’s rapid growth reflects the need for more efficient and automated solutions for managing models as data and AI technologies continue to rise in popularity. According to battery Ventures partner Dharmesh Thakker, MLOps platforms like Iterative have become critical for enterprises “as they look to scale up AI initiatives across their organisations with automation-focused tools that better connect data science initiatives with full-fledged technology infrastructures.”
The $20 million investment will help Iterative grow its MLOps offering further and foster deep connections across teams with advanced solutions for model experimentation management, monitoring, automation and optimization. It will also allow Iterative to invest in new technologies such as natural language processing (NLP) capabilities that support entirely new applications of enterprise machine learning models.
Implications of Iterative’s Investment
The investment from Iterative, a start up offering an MLOps platform, comes when more companies turn to Machine Learning (ML) and Artificial Intelligence (AI) as part of their strategies. This influx of capital signals that investors recognize the market potential for MLOps and its ability to reduce friction in developing, deploying, and managing ML-based applications.
With machine learning becoming increasingly popular across industries – from retail to healthcare – there is a growing need for technology to streamline moving models from development to production. By automating underlying operational processes, developers will have more time to innovate without sacrificing scalability or performance. Additionally, MLOps promises cost savings by allowing organisations to focus on optimising their models rather than spending resources on infrastructure and management tasks.
By leveraging Iterative’s platform, companies can accelerate development by drastically reducing time spent managing underlying infrastructure. This can free engineers to focus on what matters most – optimising models for production deployment.
In addition to streamlining operations, new investments will allow Iterative to extend its capabilities beyond version control to improve the end-to-end delivery process of machine learning projects. With workflow automation, automated testing, continuous integration and continuous delivery functions, this single platform offers huge opportunities for boosting efficiency in all aspects of developing machine learning-based applications .
Overall it appears Iterative’s investment round is further evidence that the market value of MLOps will only continue to grow in coming years as businesses realise its full potential for improving their operations and overall bottom line.
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