The Definitive Guide to AI Platforms for Open-Source Data Science and ML

Artificial intelligence (AI) is undeniably and rapidly transforming life as we know it. Leading tech companies are investing heavily, and Google CEO Sundar Pichai has compared the impact of AI to electricity and fire. Organizations that have adopted AI to improve products and services are seeing significant financial returns on their investments. We are just beginning to see the promise of AI come to life.

However, realizing business value with AI remains an elusive target for many organizations. Tools are ever-changing, use cases are disparate and scattered across the enterprise, and it’s incredibly complex to deploy useful AI into production. Data science and engineering teams that want to apply machine learning and deep learning to solve business problems face a confusing array of platforms, libraries, and software.

Introduction

In this guide, we will cover AI platforms for open-source data science and machine learning. We will define and consider the pros and cons of AI platforms that can facilitate your development and deployment of data science and machine learning algorithms, models, and systems. We will explore common use cases among enterprise organizations and some of the open-source tools available to you as you seek to harness the power of AI at scale. Finally, we will share best practices when considering an AI platform, including questions to ask potential technology providers.

AI Platforms, Data Science, and Open Source

Let’s begin with the basics, to inform those who are learning about AI platforms, machine learning, and deep learning—and also to set the stage for how we’ll be thinking about tools and techniques throughout this guide.

What is an AI platform?

An artificial intelligence (AI) platform is an integrated set of technologies that allow people to develop, test, deploy, and refresh machine learning (ML) and deep learning models. AI applies a combination of tools and techniques that make it possible for machines to analyze data, make predictions, and take action faster and more accurately than humans can do manually.

Enterprise AI platforms enable organizations to build and maintain AI applications at scale. An AI platform can:

1. Centralize data analysis and data science collaboration
2. Streamline ML development and production workflows, which is sometimes referred to as ML operations or MLOps
3. Facilitate collaboration among data science and engineering teams
4. Automate some of the tasks involved in the process of developing AI systems
5. Monitor AI models and systems in production

How can you use an AI platform?

There are many ways to prepare an AI platform for data science and engineering practitioners. Your approach will vary based on your organization’s financial resources, your team’s compute and data processing infrastructure, and your team’s expertise. In general, you have three options when it comes to creating an environment for teams to collaborate on building and deploying AI:

Choice 1: Buy an end-to-end AI platform

There are a few big businesses that dominate the end-to-end AI platform market. Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS) are all leaders in the space, offering end-to-end solutions and tools, such as AWS Sagemaker, for organizations looking to implement AI into their operations.

Benefits

• You can start fast and use tools that have enterprise-ready support. Tools and software are updated often for better reliability, performance, and security.

Challenges

• Tools may not be configured for your particular use cases, which will require planning for more engineering support at the start. As your project evolves and you recalibrate resources along the way, tools you chose before may not support new requirements.

Choice 2: Build your own AI platform

Many enterprise organizations choose to build their own AI platforms. The choice to build your own AI platform often comes down to whether your project leaders want elements of that AI technology stack to be built and owned by the organization.

Benefits

• This approach can give you and your team more control over your workflow, development process, and deployment strategies. It also allows you to respond quickly as business requirements change over time.

Challenges

• You will use more time and expense at the start to develop your own platform and supporting capabilities. You also will be responsible for the ongoing expenses of maintaining the system.

Choice 3: Open-source software

Open-source software is a great choice for building, deploying, and maintaining AI applications. It allows you to choose the best tools for your specific use case and leverage the power and innovation of the crowd of practitioners who are contributing to open-source tools, libraries, and frameworks.

Benefits

• This approach gives your practitioners access to the best tools available for your use case.

Challenges

• You will need an open-source mindset, which requires deep collaboration. You’ll also need a talented team with a clear understanding of the problem to be solved, with access to ongoing training and upskilling opportunities.

The Benefits of Open Source in Data Science and Machine Learning

Using free, open-source tools and libraries, you can quickly create powerful AI solutions with minimal effort. There are many benefits to using open-source software, including the ability to collaborate with others, to contribute to the open-source community, and to distribute the software with minimal restrictions, depending on the software license.

The benefits of open-source software for data science and machine learning include:

• Low barriers to entry: low- to no-cost access to some of the best tools available
• Reliability, security, and speed: the power of the community
• Access to open-source tools and talent: the community that uses the tools
• Libraries of data and models for data science and ML
• Education in open source for data science and ML

Why are there so many AI solutions?

The recent explosive growth of AI, as evidenced by advances in generative AI including stable diffusion and ChatGPT, is largely due to a form of machine learning called deep learning. Advances in self-driving cars and smart assistants, for example, are due to significant improvements in deep learning models.

Data scientists who attempt to deploy models find themselves wearing several hats: data scientist, software developer, IT administrator, security officer. They struggle to build and wrap their model, serve their model, route traffic and handle load-balancing, and ensure all of this is done securely. However, to achieve business value with AI, models must be deployed. And model deployment and model management are difficult tasks to complete, let alone automate.

The open-source community has only begun to create tooling to ease this challenge in model development, training, and deployment. Much of this work has been done by researchers at large companies and teams at startup-turned-enterprise companies like Uber and Netflix.

AI Use Cases By Industry

Explore concrete examples of where these advances are being applied in industry today.

AI adoption and investments are on the rise, and organizations applying AI are generating substantial returns, according to a McKinsey survey and report on The State of AI in 2022. Common AI use cases include services and product development, with other AI capabilities being added to the list all the time.

Banking

Here are common AI use cases in banking:

• Fraud detection: The most common application of machine learning is fraud detection.
• Credit scoring: Banks rely on AI to establish accurate credit scoring.
• Credit risk analysis: These predictive algorithms assess risk and predict market movement.

Ecommerce

Common AI use cases in ecommerce include:

• Product recommendations: Predictive analytics power advanced recommendation systems.
• Product-shipping models: Predictive analytics is used for anticipatory shipping models.
• Price optimization: AI adjusts prices based on product popularity.

Energy

Common AI use cases in energy include:

• Anomaly detection
• Demand forecasting
• Predictive maintenance

Finance

Common AI use cases in finance include:

• Advanced analytics: Hedge funds use machine learning to find new trading sources.
• Contract processing: NLP models parse contracts to reduce labor.
• Customer communications: NLP models categorize customer feedback and gauge sentiment.

Government

Common AI use cases in government include:

• Emergency response: Incorporating data for better service delivery.

Healthcare

Common AI use cases in healthcare include:

• Disease detection and diagnosis: Visual data processing for faster diagnoses.

Insurance

Common AI use cases in insurance include:

• Underwriting assessments: Deep learning enhances roof quality assessment.

Manufacturing

Common AI use cases in manufacturing include:

• Quality control: Machine learning detects faulty parts in production lines.

Retail

Common AI use cases in retail include:

• Customized services: AI personalizes shopping experiences.

7 Capabilities to Look for in an AI Platform

Success with AI comes down to an organization’s ability to build machine learning models at scale and rapidly deploy them. Here are key capabilities to look for in your AI platform:

1. Automation

Automation accelerates the data science lifecycle. Teams can automate proven processes and refresh models as conditions change.

2. IT Enablement and Governance

Maintain control over the ML pipeline to ensure compliance and track project lineage.

3. Scale

Centralized workflows should support collaboration and scalable model training and deployment.

4. Security

Finding and monitoring vulnerabilities in the open-source software supply chain is crucial for data scientists.

5. Support

Reliable support for onboarding and training is essential for teams working with open-source software.

6. Open-Source Tools

AI platforms should integrate easily with various open-source tools.

7. Open-Source Contributions

Organizations contributing to open-source projects enhance innovation and security.

Questions to Ask Your AI Platform Provider

1. What is required to integrate open-source orchestration tools?
2. How do you secure data to comply with GDPR and other regulations?
3. Describe how to create an environment for experimentation and model training.
4. What does access control look like with this platform?
5. How does your platform track logging and alerts?\n

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