Suitable solutions should provide data scientists with toolkits that include integrated AI libraries and that support all the end-to-end data pipeline functions.
Imagine that you’ve got a top-notch finance team, but the only tools they have are pen and paper. Thinking along these lines, imagine your sales team works from Rolodexes or marketing only has dial-up internet. Now think about how your data science team works.
For your data science team to accomplish great things, businesses need tools and systems that cover the entire range of operations when it comes to turning data into insights. What’s needed are tools that manage everything from data ingestion, preparation, visualization, and analysis. The tools need to support and, most importantly, facilitate the data science process. However, for many companies, the tools themselves are a huge barrier. Here’s what you need to know.
Where data bottlenecks occur
Businesses may be searching for those real-time insights, but their teams are bogged down with legacy systems, searching for appropriate data, and figuring out how to access it. Since a data scientist’s time is the most expensive part of the pipeline, companies must implement more efficient processes.
Enterprise data systems are highly complex, and data scientists are spending great amounts of time finding and exploring data. Some data warehouse solutions only make the problem worse by being too simplistic, adding an awkward layer of abstraction when trying to get different data sources to work together, failing to resolve the problem of data silos.
These issues can prevent businesses from reaping the benefits of AI. One way to address these issues is to use an integrated system that helps data scientists at all stages of an AI data pipeline.
So, what’s the issue?
There are several significant obstacles to creating enterprise solutions for universal data access.
Data silos. One major issue with genuine, real-time insights is that those preparing data for ingestion have no idea what it will be used for. For example, there could be data lake issues where labeling and mismatched or missing data created huge gaps, or it could be that departments don’t communicate.
Regardless of what’s happening, tools should always help break down silos and encourage full communication between parties. At the beginning of an AI project, most often, it is not known which datasets and models will be used. It’s essential to choose tech solutions that support a wide range of use cases and ones with great scalability to include any relevant datasets in the future.
Scalability. Another common problem is that many analytics solutions are designed to run on a single compute node and run best when all of the data can be placed in memory. Unfortunately, there are many cases where the algorithm and data exceed the capacity of a system. To avoid future scalability issues, developers should think ahead and select a tool that addresses this problem from the start. An excellent example is the Modin platform, a solution that scales analytics workflows to multiple machines. From the data scientist’s perspective, such tools give them the flexibility to move workloads from single to multi-node clusters.
Forcing one-size-fits-all solutions. Companies often attempt to force one-size-fits-all solutions to their unique use case instead of finding an enterprise solution with everything they need.
While one-size-fits-all solutions provide temporary fixes, they do not address the full issue. Such solutions are not forward-thinking and do not provide growth opportunities for deployment. Furthermore, they don’t allow a universal governance strategy that brings in all data owners from all departments.
Another issue with one-size-fits-all solutions is that there is a loss of flexibility. Mandating the use of specific data science packages or versions prevent teams from experimenting and innovating with new approaches.
The solution? A universal, agile data practice
An integrated set of data analysis and AI development tools is essential for improving data scientists’ productivity. Moving the needle on the data pipeline could also increase the success rate of AI deployment for enterprises.
Agile practices across the board help break down silos and facilitate iterations for continuous insight. Speeding up and making processes lean helps build and enhance data pipelines for maximum efficiency.
Suitable solutions should provide data scientists with toolkits that include integrated AI libraries and that support all the end-to-end data pipeline functions. They should also offer multiple installation solutions (e.g., Docker or Conda) to meet the business needs across the board.
The solution must be end-to-end and built for the enterprise
A fully end-to-end AI model must fit in the data analytics pipeline for production deployment. Teams must be able to assess each stage of the pipeline, including that stage’s performance and total cost of ownership (TCO).
One particularly irksome area that often arises is dealing with legacy systems. If muddling through legacy systems to access and prepare data takes longer than the machine learning (ML) training itself, there must be a different solution. Frustration with legacy systems doesn’t mean killing that system altogether, however. Tools that make the integration of legacy systems and their data easier are essential to smoothly running AI data pipelines and workflows.
Another area that needs consideration is data security. It is essential since the data set for ML training or AI algorithm execution may contain sensitive and personal information. The data analysis tools should be fully integrated into the security systems deployed in the enterprise.
Addressing such issues needed in real production deployments means companies cannot get stuck on special-purpose systems that don’t advance real-time insight or segment data into harder-to-find silos.
So, what is the ideal end-to-end solution for working with and enabling efficient and secure AI data pipelines? There are many open-source tools and platforms available to choose from. The problem is that most data scientists do not have the time to explore the ever-growing number of new tools and integrate them into existing pipelines and workflows.
A real solution is here
Intel and Anaconda are partnering to provide bundled toolsets for data science that are easy to use and optimized for performance for different hardware architectures. These toolsets allow businesses to harness big data’s true power while streamlining their data science pipeline. The toolsets help data scientists focus on delivering insightful visualizations and building high-impact models.
The toolkits offered by Anaconda and Intel put security first, offering universal governance that breaks down silos. By providing the needed security across the enterprise, departments will be on board with high-touch data projects, and data scientists will be able to facilitate real, continuous insight.
Read the other blogs in this series:
- How to Efficiently Build Today’s Modern Data-Driven Applications
- “Enterprise-Class Open-Source Data Tools” Is Not an Oxymoron
- The Need for the Pervasive Use of Data and Analysis
- How to Deliver on Your Data Strategy
