Top AI and machine learning development platform providers

It is like magic to experience intelligent features on our phones, devices and apps. But developing them can be anything but! Developers face too many sequential and interconnected challenges along the way to achieving “magic” machine learning solutions.

How they address and solve these challenges under the pressure of building the best machine learning pipeline faster in a given time frame, determines the accuracy and performance of the machine learning solution. Higher accuracy often delays or even preclude pursuing other projects.

Therefore, they sometimes need to settle for less-than-optimal accuracy in models, which can later lead to loopholes for fraud, malicious activities, and other problems. But what if a developer could access an automated machine learning platform that can help them identify an appropriate end-to-end machine learning pipeline for any situation and achieve higher accuracy while spending far less of their time?

That’s where the role of AI and machine learning development platforms comes in. These platforms allow the businesses to run a significantly larger number of experiments, resulting in faster iteration towards production-ready intelligent experiences.

Today’s post will look at the top five AI and machine learning development platform providers, recommended by independent analyst firm Omdia (formerly Ovum).

The platform providers in our list offer a wealth of AI and ML capabilities that will be essential as enterprises engage in the digital transformation of their businesses. Catering to AI and ML workloads, they can automate large-scale tasks that manual efforts can’t cope with effectively or manage at all. They accelerate the creation of AI- and ML-driven business outcomes through pre-built software as well as augmented and automated development tasks.

1. is a leading innovator in enterprise AI software for accelerating digital transformation. It offers a comprehensive AI platform using a flexible modular approach known as MDA for developing, deploying, and operating large-scale AI, predictive analytics, and IoT applications. has a revolutionary, model-driven AI architecture that dramatically enhances data science and application development.

It enables an abstraction layer to exist where new components inherit the rich attributes of the layer, with pre-built connections to infrastructure, data sources, and other features. can extend and evolve custom solutions speedily and reliably and at scale. This approach makes it easier for ML developers and data scientists to focus their work on core functional requirements.

What sets apart is the ability to tackle the complete ML lifecycle, including development, deployment, performance, scalability, and monitoring, security, and integration. created a cross-vertical ML development platform capable of affording large-scale, real-time business execution, which can be performed by experts and non-experts.

2. Dataiku

Dataiku is the world’s most advanced and centralized enterprise AI and machine learning platform. It democratizes the use of data science, machine learning, and AI in the enterprise. The catalyst for data-powered companies, Dataiku uniquely empowers businesses to move along their data journey, from data preparation to analytics, at scale.

By providing access to thousands of teams and users like technical coders, non-coders, data scientists and business analysts, the platform opens up a common ground, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment for data experts and explorers.

Data democratization through collaboration among people with different skillsets, has been at the core of Dataiku since its inception. The self-service analytics capabilities in the platform help break siloed department walls and provided a single point for accessing data, integrating with legacy data sources across hybrid infrastructure, and reducing the time wasted accessing data.

Notably, Dataiku was named a Leader in the Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms. It marked the fourth consecutive year of Dataiku’s inclusion in the report and the first year in the Leaders quadrant. Today, over 300 customers across retail, e-commerce, health care, finance, transportation, the public sector, manufacturing, and pharmaceuticals use Dataiku to massively scale AI efforts.

3. IBM

Since its inception in 2011, IBM Watson has been recognized as a pioneer in the Artificial Intelligence (AI) space. It now includes a vast suite of products, helping technology innovators in several industries adopt AI.

In 2018, IBM introduced OpenScale AI to offer a complete end-to-end build-deploy-validate-monitor-govern lifecycle for ML applications, suitable for use by a range of technical and business roles. It is a scalable and open platform that makes it easy to use AI in a vendor-agnostic way.

IBM Watson OpenScale lets enterprises operate and automate AI at scale, regardless of their technologies and environments. It provides a genuine understanding of how AI apps make decisions, bridging the AI skills gap, and solving complexities around a multi-vendor AI environment.

Watson OpenScale AI tracks and measures outcomes from AI across its lifecycle and adapts and governs AI to changing business situations — for models built and running anywhere. IBM has the best of open source ML algorithms available curated under the hood, including all the most popular frameworks, such as TensorFlow and PyTorch.

4. Microsoft

Microsoft’s Azure Machine Learning (AML) platform helps for both Microsoft product teams and customers scale ML development projects and build intelligent features that can plug into business processes.

Microsoft’s Automated ML fulfills this task by releasing data scientists to focus on the data input and output and deploying the application. It provides automated pipelines that take large amounts of data stored in Azure Data Lake, merges and pre-process the raw data, and feeds them into distributed training running in parallel across multiple VMs and GPUs.

The machine learning version of the automated deployment common in DevOps is known as MLOps. Office machine-learning models are often built using frameworks like PyTorch or TensorFlow; the PowerPoint team uses a lot of Python and Jupiter notebooks.

5. SAS

SAS is a leader in analytics with a long history as a statistician’s tool of choice. It has been well-positioned to tackle the rise of ML the role of data scientist, putting the company into a central role within the enterprise, building on the strength of its platform.

The updated SAS Platform is enhancing its easy-to-use artificial intelligence (AI) solutions to help organizations improve efficiency and quickly realize value with automation. It comes with new functionalities, including automated data management, automated machine learning, and cutting-edge interpretability features, making AI more accessible for all.

The SAS Viya in the SAS Platform offers new AI and advanced analytics techniques for both data scientists and business users to automate most of the manual and complex steps required for data transformations and the development of machine learning models. It automates the analytics lifecycle, from data wrangling to feature engineering and algorithm selection, in a single click.

Adding a layer of transparency, the platform produces a visual pipeline to eliminate the black box that can accompany automation. Once a model is complete, it can be deployed with a single click. The automated modeling process uses a REST API, enabling the developers to customize business applications while using SAS Analytics. Additionally, users can easily embed open source code and augment their analysis with SAS, providing a truly open experience.