These are heady days in data science and machine learning (DSML) according to Gartner, which identified a “glut” of innovation occurring in the market for DSML platforms. From established companies chasing AutoML or model governance to startups focusing on MLops or explainable AI, a plethora of vendors are simultaneously moving in all directions with their products as they seek to differentiate themselves amid a very diverse audience.

“The DSML market is simultaneously more vibrant and messier than ever,” a gaggle of Gartner analysts led by Peter Krensky wrote in the Magic Quadrant for DSML Platforms, which was published earlier this month. “The definitions and parameters of data science and data scientists continue to evolve, and the market is dramatically different from how it was in 2014, when we published the first Magic Quadrant on it.”

The 2021 Magic Quadrant for DSML is heavily represented by companies to the right of the axis, which anybody who’s familiar with Gartner’s quadrant-based assessment method knows represents the “completeness of vision.” No fewer than 13 of the 20 vendors to make the quadrant’s cut landed on the right side, which indicates active innovation.

Generating new DSML features and exploring new DSML methods is the name of the game in this fast-moving business, Gartner says. “There remains a glut of compelling innovations and visionary roadmaps,” the analysts wrote. “…[V]endors are heavily focused on innovation and differentiation, rather than pure execution. Innovation remains key to survival and relevance.”

The Connecticut-based analyst firm did not sound surprised to conclude that the cloud biggies have moved strongly into the space. “The long-expected gigantic presence in this market of Google and Amazon is now easily felt as they compete with Microsoft for supremacy in terms of DSML capabilities in the cloud,” the analysts write.

However, that does not mean that they are sucking all the air out of the room, as smaller companies have found success in the market, with a few achieving what Gartner termed “hypergrowth.” A few well-established leaders from the previous generation of statistical tools, like SAS, MathWorks, and IBM (SPSS) are also doing well, Gartner notes. In fact, those three vendors are collectively doing better than AWS, Google, and Microsoft when it comes to ability to execute.

The DSML market is young and vibrant, and there is ample revenue and funding opportunities for companies that differentiate themselves on the product side, Gartner says. There is just a “moderate” level of M&A activity at this time, which indicates a growing market. With that said, the vendors who made Gartner’s cut had to prove themselves by meeting certain customer-count and financial performance criteria. And of course, they have to have a product that meets the definition of an DSML platform.

Which begs the question: Just what is an DSML platform? Gartner defines it as a place “to source data, build models and operationalize machine learning,” either by certified, card-carrying data scientists or people who are doing data science work, i.e. citizen data scientists, data engineer, or ML specialists.

Beyond that broad definition, Gartner identified 13 other capabilities that may (or may not) exist in a given DSML platform, including: data ingestion; data preparation; data exploration; feature engineering; model creation and training; model testing; deployment; monitoring; maintenance; data and model governance; explainable artificial intelligence (XAI); business value tracking; and collaboration.

Here’s a brief description of the pros and cons provided for each of the vendors listed in the Magic Quadrant, courtesy of Gartner:

Leaders Quadrant

Databricks Unified Data Platform

Pros: Scalable multi-cloud support; empowerment of data scientists; execution and expansion.

Cons: Lack of support for citizen data scientists; need for governance and responsible AI; growing cloud competition.

Dataiku Data Science Studio

Pros: Support for citizen data scientists; focus on business value; market traction.

Cons: Heavy use of extensions and plugins; emerging story around “XOps” (i.e. unified management of data, ML, models, and platforms); pricing for smaller teams.

IBM Watson Studio on IBM Cloud Pak for Data

Pros: support for multiple personas; composite AI vision; responsible AI and governance.

Cons: scope of auto AI features; doubts about Watson brand; lack of clarity in product-bundling.

MathWorks MATLAB

Pros: Robust composite AI capabilities; integrated domain knowledge; verifiable and reliable ML.

Cons: Interface lacks usability among non-engineers and non-scientists; interpretability of ML models; lack of augmented DSML capabilities.

SAS Viya

Pros: Market understanding and presence; cloud-native architecture and open source integration; automated feature engineering and modeling.

Cons: Perceived high cost; product bundling; marketing strategy.

TIBCO Software (various products)

Pros: Leading edge DSML capabilities; integration of DS and BI/analytics; support for collaboration and applied analytics.

Cons: Limited ModelOps capabilities; lack of support for citizen data science capabilities; financial growth in 2020.

Visionaries Quadrant

AWS (various products)

Pros: Breadth and depth of cloud platform; performance and scalability; data labeling and human-in-the-loop capabilities

Cons: Lack of attention on citizen data scientist; rapid rollout of products and maturity; maturity of on-prem, hybrid, and multi-cloud support

DataRobot Enterprise AI Platform

Pros: Sales strategy and execution; high-touch customer service; successful acquisitions.

Cons: Complexity of product portfolio; resource-heavy onboarding; capability gaps.

Google Cloud AI Platform

Pros: Responsible AI vision and capabilities; research contributions; cohesion and simplification of consolidated products.

Cons: Rapid pace of change; steep learning curve; lack of capabilities for on-prem, hybrid, and multi-cloud deployments.

KNIME Analytics Platform

Pros: Breadth and depth of DSML capabilities; commitment to open source; visual workflow coherence.

Cons: Limitations in enterprise deployments; responsible AI vision; low market traction.

Microsoft Azure Machine Learning

Pros: Strong support for enterprise DS; support for multiple personas; openness and partnerships.

Cons: Requirement of use of other Azure services; immaturity of on-prem, hybrid, and multi-cloud capabilities; lack of support for augmented DSML capabilities.

RapidMiner (various products)

Pros: Support for multiple personas; “clear vision and delivery of aligned features”; expandability and governance.

Cons: Growth rate; average advanced analytics capabilities; academic perception of product.

H2O.ai (various products)

Pros: Vision for value creation; extensive automation; rich AI explainability features (XAI).

Cons: Lack of some data access and data prep features; OEM partner strategy; collaboration and cohesion.

Challengers Quadrant

Alteryx Analytics Process Automation

Pros: Support for multiple personas; product packaging and go-to-market strategy; customer support.

Cons: Changing product portfolio; high cost; lack of innovation.

Niche Players Quadrant

Alibaba Cloud’s Platform for AI (PAI) Studio and Data Science Workshop

Pros: Strong community in China; advanced use-case modeling; and seamless integration.

Cons: Focus on Asia; lack of product vision; narrow usage and focus on professional data scientists.

Altair Knowledge Studio and Knowledge Works

Pros: Ease of use; support for data pipelines; customer satisfaction

Cons: Functional gaps in lineup; limited rollouts in some industries; relatively slow growth.

Anaconda Enterprise

Pros: Trusted and flexible platform; based on open source; culture of collaboration.

Cons: Focus on technical audience; lack of model operationalization functions; runtime stability.

Cloudera Data Platform

Pros: Native Spark on Kubernetes; support for complex data workloads; metadata support for DataOps and MLOps.

Cons: No GUI for development; lack of coherence of products; domain-specific solutions.

Domino Data Lab Data Science Platform

Pros: Support for large, expert teams; mature MLOps capabilities; support for on-prem, hybrid, and multi-cloud.

Cons: Support for small, immature DS teams; low market visibility; open source vision;

Samsung SDS Brightics AI

Pros: Comprehensive ecosystem vision; data access, prep, and visualization; ease of use and collaboration.

Cons: Limited adoption outside of Asia; gaps in product vision; limited capabilities in ModelOps and explainability.

This is indeed a great time to be in the data science and machine learning business. Whether you’re a user of these tools or helping to develop them, the rapid pace of innovation is not only exciting but good for business as a whole.

The post A ‘Glut’ of Innovation Spotted in Data Science and ML Platforms appeared first on Artificial Intelligence.

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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