Gartner’s Magic Quadrant report on data science and machine learning (DSLM) platform companies assesses what it says are the top 20 vendors in this fast-growing industry segment.

Data scientists and other technical users rely on these platforms to source data, build models, and use machine learning at a time when building machine learning applications is increasingly becoming a way for companies to differentiate themselves.

Gartner says AI is still “overhyped” but notes that the COVID-19 pandemic has made investments in DSLM more practical. Companies should focus on developing new use cases and applications for DSML — the ones that are visible and deliver business value, Gartner said in the report released last week. Smart companies should build on successful early projects and scale them.

The report evaluates DSML platforms’ scope, revenue and growth, customer counts, market traction, and product capability scoring. Here are some of the notable findings:

• Responsible AI governance, transparency, and addressing model-based biases are the most valuable differentiators in this market, and every listed vendor is making progress in these areas.
• Google and Amazon are finally competing with Microsoft for supremacy in terms of DSML capabilities in the cloud. Amazon wasn’t even included in last year’s Magic Quadrant because it hadn’t shipped its core product by the November 2019 cutoff date. The longest-standing big names in this sector — IBM, MathWorks, and SAS — are still holding their ground and innovating with modern offerings and adaptive strategies.
• Numerous smaller, younger, and mid-size vendors are in sustained periods of hypergrowth. The growing size of the market feeds startups at all phases of the data science lifecycle. Gartner observes that growing at the rate of the market actually means growing slowly.
• Alibaba Cloud, Cloudera, and Samsung DDS are included in the Magic Quadrant for the first time.
• The DSML platform software market grew by 17.5% in 2019, generating $4 billion in revenue. It is the second-fastest-growing segment of the analytics and business intelligence (BI) software market behind modern BI platforms, which grew 17.9%. Its share of the overall analytics and BI market grew to 16.1% in 2019.
• The most innovative DSML vendors support various types of users collaborating on the same project: data engineers, expert data scientists, citizen data scientists, application developers, and machine learning specialists.

There remains a “glut of compelling innovations” and visionary roadmaps, Gartner says. This is an adolescent market, where vendors are heavily focused on innovation and differentiation, rather than pure execution. Gartner said key areas of differentiation include UI, augmented DSML (AutoML), MLOps, performance and scalability, hybrid and multicloud support, XAI, and cutting-edge use cases and techniques (such as deep learning, large-scale IoT, and reinforcement learning).

Data science and machine learning in 2021 and beyond

For most enterprises, the challenge is to keep up with the rapid pace of change in their industries, driven by how fast their competitors, suppliers, and channel partners are digitally transforming their businesses.

• CIOs and senior management teams want to understand the specifics of how data science and machine learning models work. A top priority for IT executives working with DSML technologies is understanding bias mitigation and how DSML technologies can control for biases on a per-model basis. Designing transparency should start with model and data repositories, providing greater visibility across an entire DSML platform.

• Enterprises continue to struggle with moving more AI models from pilot to production. According to the 2020 Gartner AI in Organizations Survey, just 53% of machine learning prototypes are eventually deployed to production. Yield rates from the initial model to production deployment show room for improvement. Look for DSML vendors to step up their efforts to deliver modeling apps and platforms that can accept smaller datasets and still deliver accurate results.
• Open source software (OSS) is a de facto standard with DSML vendors. OSS provides enterprises the opportunity to get DSML projects up and running with little upfront spending. OSS adoption has become so pervasive that most DSML vendors rely on OSS, starting with Python, the most commonly used language. DSML platform providers also help optimize and curate OSS distributions.
• For any enterprise to invest in a DSML platform, integration and connectivity are essential. DSML vendors are adopting components for their platform architectures because components are more extensible and can be tailored to an enterprise’s specific needs. Packaged models that integrate into a DSML platform using APIs help enterprises customize machine learning models for specific industry challenges they’re facing.
• Designing more intuitive interfaces and workflows reduces the learning curve for lines of business and data analysts. Improvements in augmented data science and ML help offload all the data science and modeling work from experienced data scientists to business analysts who prefer to iterate models on their own, often changing constraints based on market conditions.
• Organizations rely on free and low-cost open source, combined with public cloud providers to reduce costs while experimenting with DSML initiatives. They are then likely to adopt commercial software to tackle broader use cases and requirements for team collaboration and to move models into production.

Which vendors are leading — and why

Here are some company-specific insights included in this year’s Magic Quadrant:

• SAS Visual Data Mining and Machine Learning (VDMML) is the market leader, having dominated the Leader quadrant for years in this specific Magic Quadrant. Gartner gives SAS credit for its cloud-native architecture, automated feature engineering and modeling, and domain expertise reflected in its advanced prototyping and production refinement use cases. SAS is often seen as a legacy vendor that’s expensive to implement and support. The customer loyalty SAS has accrued in global enterprises and the priority its development teams place on DSML helps the company maintain dominance in this market.
• IBM’s Watson Studio ascended into the Leader quadrant this year, up from being considered a Challenger in 2020. Gartner believes the company’s completeness of vision (horizontal axis of the quadrant) has improved since last year, moving it into the Leader quadrant. This is mainly due to IBM Watson Studio’s multi-persona support, depth of responsible AI and governance, and component structure proving effective for decision modeling. Building on several years of reinventing itself, IBM can deliver an enterprise-class DSML that will successfully progress beyond the pilot or proof-of-concept phase. Gartner gives IBM credit for capitalizing on previous successes of SPSS, ILOG CPLEX Optimization Studio, earlier analytics products, and the continual stream of innovations from IBM Research.
• Alteryx’s strong momentum in the market isn’t reflected in its shift from the Leader quadrant to Challenger. Alteryx powered through last year’s uncertainty, reporting a 19% year-over-year increase in revenue for 2020, reaching $495.3 million. Annual recurring revenue grew 32% year over year to reach $492.6 million. Gartner gives Alteryx credit for supporting multiple personas, a proven go-to-market strategy, and delivering excellent customer service and support. Alteryx has proven to be innovative, despite having that attribute mentioned as a caution in the Magic Quadrant.
• Amazon SageMaker’s market momentum is formidable, further strengthened by its pace of innovation. In February, Amazon Web Services (AWS) announced it has designed and will produce its own machine learning training chip. AWS Trainium is designed to deliver the most teraflops of any machine learning training instance in the cloud. AWS also announced Trainium would support all major frameworks (including TensorFlow, PyTorch, and MXnet). Trainium will use the same Neuron SDK used by AWS Inferentia (an AWS-designed chip for machine learning inference acceleration), making it easy for customers to get started training quickly with AWS Trainium. AWS Trainium is coming to Amazon EC2 and Amazon SageMaker in the second half of 2021. Amazon SageMaker comprises 12 components: Studio, Autopilot, Ground Truth, JumpStart, Data Wrangler, Feature Store, Clarify, Debugger, Model Monitor, Distributed Training, Pipelines, and Edge Manager.
• Google will launch its unified AI Platform in the first quarter of 2021. This is after the cutoff date for evaluation in this Magic Quadrant. It will release key features like AutoML tables, XAI, AI platform pipelines, and other MLOps services.

The challenges for DSML platform vendors today begin with balancing the needs for greater transparency and bias mitigation while developing and delivering innovative new features at a predictable cadence. The Magic Quadrant reflects current market reality after updating with four new cloud vendors, one with an extensive ecosystem and proven market momentum.

One thing to consider after looking at the Magic Quadrant is that there will be some mergers or acquisitions on the horizon. Look for BI vendors to either acquire or merge with DSML platform providers as the BI market’s direction moves toward augmented analytics and away from visualization. Further fueling potential M&A activity is the fact that DSML platforms could use enhanced data transformation and discovery support at the model level, which is a long-standing strength of BI platforms.

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The post Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML appeared first on Artificial Intelligence.

Analyst house Gartner, Inc. has released its 2021 Magic Quadrant for Data Science and Machine Learning Platforms. The researcher defines a data science and machine learning platform as “a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner-sourced and open-source). Its primary users are data science professionals, including expert data scientists, citizen data scientists, data engineers, application developers and machine learning (ML) specialists.”

Data science and machine learning platforms can support tasks across the data science lifestyle like problem and business context understanding, data ingestion, data preparation, and data exploration. They can also include feature engineering, model creation and training, model testing, deployment, monitoring, maintenance, data and model governance, explainable AI, business value tracking, and collaboration tools. The expansion of capabilities among tools in this market makes it ‘more vibrant’ than ever according to Gartner.

Buyers should be aware that this market offers a broad array of products for different requirements and preferences, typically dependent on the user persona. This Magic Quadrant is aimed especially at expert data scientists, citizen data scientists, supporting roles, LOB data science teams, and corporate data science teams. While the eye may begin analyzing this report in the Leaders quadrant, Gartner notes that it is best to evaluate your specific needs when assessing vendors. They add “A vendor in the Leaders quadrant, for example, might not be the best choice for you. Equally, a Niche Player might be the perfect choice.”

Data science and machine learning vendors remain focused on innovation and product differentiation, and the market is made up of established and thought-leading providers as well as startups with emerging value propositions. The proliferation of augmented analytics has drawn this market to a near collision with the horizontal Analytics and Business Intelligence space, adding another layer. Gartner concludes “Traditionally these have been discrete markets with different buyers, but that situation is changing.”

While most vendors are aiming for a “sweet spot” with their platforms as it relates to adoption by expert and citizen data scientists, there are several key areas of differentiation. These include the user interface, augmented data science and machine learning, MLOps, performance and scalability, hybrid and multicloud support, and support for cutting-edge use cases and techniques.

Gartner adjusts its evaluation and inclusion criteria for Magic Quadrants as software markets evolve. As a result, Alibaba Cloud, Amazon Web Services (AWS), Cloudera, and Samsung SDS have been added to the 2021 report. No solution providers were removed from this edition.

In this Magic Quadrant, Gartner evaluates the strengths and weaknesses of 20 providers that it considers most significant in the marketplace, and provides readers with a graph (the Magic Quadrant) plotting the vendors based on their ability to execute and completeness of vision. The graph is divided into four quadrants: niche players, challengers, visionaries, and leaders. At Solutions Review, we read the report, available here, and pulled out the key takeaways.

SAS and IBM headline the Leaders quadrant for 2021. SAS remains the overall leader via its Visual Data Mining and Machine Learning product and thought leadership in composite AI, MLOps and decision intelligence. SAS touts an enterprise-grade solution and keen eye for market trends, as well as cloud-native architecture and integrations with popular open-source tools. IBM drastically improved its Completeness of Vision across Gartner’s horizontal axis to attain its 2021 position. IBM now offers a modern and complete product featuring multipersona support and a focus on responsible AI and governance.

Dataiku and TIBCO Software are clustered even closer together this year than last, though both remain major players. Dataiku is known to have an understanding of the citizen data scientist role, focuses on business value via performance metrics, and has what Gartner refers to as a “strong product roadmap” and vision. TIBCO Software offers a platform for Connected Intelligence and has a science and engineering focus. It has a leg up on the field when it comes to model deployment and production, and offers leading-edge functionality. TIBCO is also an excellent consideration for decentralized analytic professionals.

Databricks and MathWorks round out the Leaders quadrant for 2021, both maintaining positions in the bottom-third of the column. Databricks’ Unified Data Platform is available in multiple clouds and touts excellent scalability features. Its recent acquisition of Redash further expands the platform. Expert data scientists will find the most immediate value from the Databricks platform. MathWorks primarily services those in engineering and asset-centric organizations. The vendor offers advanced composite AI capabilities, deep domain expertise, and verifiable and reliable machine learning as well.

Alteryx is the only 2021 Challenger in this Magic Quadrant. The vendor is currently overhauling its focus, evident through the release of Alteryx Analytic Process Automation in May. It mainly serves clients in manufacturing, financial services, consumer packaged goods, retail, healthcare, and government. Alteryx touts ease of use for different user personas, a growing collection of partnerships, and strong customer sentiment regarding overall experience with the platform.

Microsoft, Google and AWS are competing for the top spot among cloud-based data science and machine learning vendors in the Visionaries column. Microsoft has the highest score for Ability to Execute in this class and offers capabilities for citizen and expert users. The mega-vendor also features strong MLOps functionality and security and governance for compute quota and cost management. Google currently offers its Cloud AI Platform but is soon set to release a unified product. Google touts thought leadership in ML research and responsible AI, and has recently made a major effort to reorganize its software release schedule.

Amazon’s data science and machine learning tools run through its SageMaker product line. AWS is natively integrated with many proprietary cloud data and analytics solutions as well. The mega-vendor touts perhaps the strongest performance and scalability of any product listed. Its data labeling and human-in-the-loop capabilities are also very popular. DataRobot retains its position between Microsoft and AWS just a stone’s throw away from the Leaders column. It features data science augmentation, prediction quality management via a Humble AI initiative, and high-touch customer service for providing business value.

KNIME, RapidMiner and H20.ai form a cluster on the right-hand side of the Visionaries quadrant to round it out. The open-source KNIME Analytics Platform and commercial KNIME Server products bridge the gap between development and production so data scientists and end-users can better collaborate. KNIME touts more than 4,000 nodes for connecting to data, as well as a visual workflow that can be broken down into individual components. RapoidMiner’s offerings reflect current trends like multipersona, collaboration, XAI, and model governance. New capabilities like FeatureMart and Feature Catalog are major value-adds.

H2O.ai earned the highest score for Completeness of Vision of any provider in this report. It is a thought leader in the automation and augmentation of data science and machine learning, especially for time-series analysis. H2O also offers several explainability capabilities for both modeling and feature engineering.

Alibaba Cloud, Cloudera, and Samsung SDS make their collective debuts as 2021 Niche Players. Alibaba is currently an Asia-only play for its retail, internet and data service customers. Cloudera Machine Learning is delivered as a service on top of the Cloudera Data Platform. The vendor mainly focuses on unifying machine learning workflows across data warehousing, data engineering, data science and machine learning, and operationalization. Cloudera’s strengths include native use of Spark on Kubernetes, complex data processing, and metadata management for DataOps and MLOps. Like Alibaba Cloud, Samsung SDS is currently concentrated in the Asian market.

Domino offers end-to-end capabilities on-prem or in the cloud. It released Domino Model Monitor last June which, according to Gartner, shows a “commitment to enterprise MLOps.” Domino is best-fit for code-first data science teams. Anaconda’s recent innovations around model governance and scalability are noteworthy, while Anaconda Enterprise remains a  trusted, flexible and recognized product. Anaconda enables the optimization of open-source tools as well. Altair is a strong consideration for service-centric industries, offers various simulation and high-performance computing tools, and touts excellent customer scores for deployment, service, and support.

The post What’s Changed: 2021 Gartner Magic Quadrant for Data Science and Machine Learning Platforms appeared first on Artificial Intelligence.