What do we mean when we talk about AI tools? In their most basic form, machine learning (ML) and AI tools or platforms are tools that:
- Enable people with AI wherever and whoever they are.
- Support a spectrum of AI use cases, from self-service analytics to operationalized models in production.
- Build sustainable AI systems that are governable and open to changing needs and technologies in the future.
Ultimately, ML and AI tools are about time. That means time savings in all parts of the processes (from connecting to data to building ML models to deployment), of course. But it’s also about easing the burden of getting started in AI and allowing businesses to dive in and get started now before it’s too late (especially now given the rise in Generative AI).
Starting the AI journey might be intimidating, but ML and AI tools and platforms can ease that burden and provide a framework that allows companies to learn as they go. In our latest “In Plain English” blog series, we unpack key elements to look for in an AI tool and concrete reasons to invest in them.
A Framework for AI Tools: What to Look For
ML and AI tools and platforms provide both the flexibility and control required to scale AI initiatives because they are a framework for:
Data Access, Exploration, Prep, and Transformation:
To support self-serve analytics initiatives, people across an organization — whether proficient in code or not — need a simple way to access and interface with data in a way that’s both efficient and transparent.
💡See data prep in action in Dataiku here.
Machine Learning:
Of course, machine learning is in the name and is table stakes for a data science, ML, and AI platform. What’s important is that platform’s ability to facilitate rapid iteration plus to connect ML to the rest of the AI project lifecycle (including data prep but also, for example, operationalization and MLops or governance of the model once it’s in production). After all, Google showed that ML code itself only contributes 5% to high-level technical debt, where the remaining 95% is tied up in "Glue Code” (i.e., dependencies holding it all together).
💡See ML in action in Dataiku here.
Collaboration:
A way for people — whether data scientists, analysts, engineers, or business experts — to work together on data projects despite having different skills and expertise. This includes horizontal collaboration, or people working together with others who have roughly the same skills, toolsets, training, and day-to-day responsibilities, as well as vertical collaboration, or people working across different teams.
💡See collaboration in action in Dataiku here.
Governance:
Traditional data governance includes data security, reference and master data management, data quality, data architecture, and metadata management. Additional considerations with ML include model management (MLOps) and Responsible AI.
💡See AI Governance in action in Dataiku here.
Reuse and Capitalization:
Reuse is the simple concept of avoiding rework in AI projects, from small details (like code snippets that can be shared to speed up data preparation) to the macro level (like ensuring two data scientists from different parts of the company aren’t working on the same project). Capitalization in Everyday AI takes reuse to another level — it’s about sharing the cost incurred from an initial AI project (most commonly the cost of finding, cleaning, and preparing data) across other projects, resulting in many use cases for the price of one, so to speak.
💡Learn about reuse and how to reduce the cost of AI in this quick read.
Automation:
Critical to the scaling of AI in an organization, it includes everything from operationalization and automating entire CI/CD pipelines to performing automated checks and building datasets or training models.
💡Discover 7 critical automations for a machine learning platform here.
Architecture:
The furious pace of innovation and sheer number of technologies in AI is overwhelming, and organizations need to have the agility to adopt (and drop) tech when it makes sense while still providing a consistent experience to end users.
💡Learn more about architecture in Dataiku here.
Why Invest in AI Tools?
1. Ad-Hoc Methodology Is Unsustainable for Large Teams
Small teams can potentially sustain themselves to a certain point by working on data, ML, or larger AI projects in an ad-hoc fashion, meaning team members store their work locally and not centrally and don’t have any reproducible processes or workflows, figuring things out along the way.
But with more than just a few team members and more than one project, this becomes unruly quickly. Any business with any hope of getting return on investment (ROI) from AI at scale needs a central place where everyone involved with data can do all of their work, from accessing data to deploying a model into a production environment. Allowing people — whether directly on the data team or not — to work ad hoc without a central tool from which to work is like a construction team trying to build a skyscraper without a central set of blueprints.
💡In "Upskilling: How to Win the Battle for Data + AI Talent,” discover why having a centralized platform for analytics and AI projects is so important.
2. MLOps at Scale Is Complex
At its core, MLOps is the standardization and streamlining of ML lifecycle management. But taking a step back, why does the ML lifecycle need to be streamlined? Didn’t we already figure this out with DevOps? Indeed, the two have quite a bit in common: For example, they both center around:
- Robust automation and trust between teams.
- The idea of collaboration and increased communication between teams.
- The end-to-end service lifecycle (build-test-release).
- Prioritizing continuous delivery as well as high quality.
Yet there is one critical difference between MLOps and DevOps that makes the latter not immediately transferable to data science teams: deploying software code in production is fundamentally different from deploying ML models into production. While software code is relatively static, data is always changing.
Teams are increasingly looking for ways to formalize a multi-stage, multi-discipline, multi-phase process with a heterogeneous environment and a framework for MLOps best practices, which is no small task. Data science, ML, and AI tools like Dataiku that offer these capabilities can help.
💡Check out MLOps in action in Dataiku here.
3. Not Practicing Responsible AI Is Getting Riskier
Recent excitement around Generative AI — in particular Large Language Models — means organizations are pushing forward with the use of AI at an unprecedented pace. There has arguably never been a more pivotal time in the history of AI.
At the same time, it’s important to stay grounded. The truth is that flaws within AI systems and the data they are built on can present — and have presented, even before the rise of Generative AI — real risks. More than ever before, organizations need to think about building AI systems in a responsible and governed manner.
💡For more on this topic, check out the Dataiku ebook “Build Responsible Generative AI Applications: Introducing the RAFT Framework.”
4. Governance Is Getting Trickier
With the amount of data being collected today, data security (especially in certain industries like finance) is critical. Without a central place to access and work with data that has proper user controls, data could be stored across different individuals’ laptops. And if an employee or contractor leaves the company, the risks increase not only because they could still have access to sensitive data, but because they could take their work with them and leave the team to start from scratch, unsure of what the person was working on.
On top of these issues, today’s enterprise is plagued by shadow IT; that is, the idea that for years, different departments have invested in all kinds of different technologies and are accessing and using data in their own ways. So much so that even IT teams today don’t have a centralized view of who is using what, how. It’s an issue that becomes dangerously magnified as AI efforts scale and points to the need for governance at a wider and more fundamental level across all lines of business in the enterprise.
💡For more on this topic, check out the Dataiku ebook "How to Safely Scale AI With Oversight."
5. It's Impossible to See ROI From AI by Simply Scaling Use Cases
Data science, ML, and AI projects have obvious, tangible costs (like that of tools and technology), which should certainly be managed in order to successfully scale. But there are costs like data cleaning, pushing to production, and model monitoring that take time and resources as well.
Getting to the tenth or twentieth AI project or use case usually still has a positive impact on the balance sheet, but eventually, the marginal value of the next use case is lower than the marginal costs. It is, therefore, economically impossible to scale use cases, and it's a big mistake to think that the business will be able to easily generalize Everyday AI everywhere by simply taking on increasingly more AI projects throughout the company.
Ultimately, to continue seeing ROI in AI projects at scale, taking on exponentially more use cases, companies must find ways to decrease both the marginal costs and incremental maintenance costs of AI. ML and AI tools are a good way to do this.
💡For more on this topic, check out the Dataiku ebook "The Economics of AI."
Bringing It All Together: How Is Dataiku Different From Other AI Tools?
As you can see, ML and AI tools and platforms are the underlying framework that allow companies to scale and be more productive when it comes to data initiatives. They should allow for easy (but controlled) access to data necessary to complete complex data projects and initiatives, keep all work centralized (and thus reproducible), and facilitate critical collaboration not only among similar profiles but between them (data scientist, business/data analyst, IT, etc.).
Dataiku is for everyone, from IT to data scientists, data engineers and software engineers, business people, managers, analysts, and more. The platform for Everyday AI handles both vertical and horizontal collaboration with ease with a complete suite of features that enable communication and allow both technical and non-technical staff work with data their way.
Many tools and platforms today say they are end-to-end, but they actually only handle one or two parts of the data process, so inevitably, the business needs to purchase other tools to round out the Enterprise AI strategy (not to mention find a way to cobble these tools together and make the data workflow seamless between them). Dataiku is one tool for everything from data wrangling to machine learning to deployment plus a framework for governance, MLOps, and more.
Perhaps most importantly, ML and AI tools and platforms open up the door to true data innovation when teams don’t have to spend precious time on administrative, organizational, or repeated tasks. The reality is, in the age of AI, businesses of any size can’t afford to work without a data science platform that enables and elevates not just their data science team, but the entire company to the highest level of data competence for the greatest possible impact.