Daily Archives: July 2, 2021

Accelerate efficiency gains with optimization and AI

Today, machine learning (ML), artificial intelligence (AI) and decision optimization (DO) are not just buzzwords you read in the press, but urgent requirements for any company that fears disruption and wants to do pragmatic analysis in order to make better decisions with their data. Data is the next natural resource but as with any resource, it’s not worth much until it helps you show results. Investment in AI can help accelerate and enhance those results.

A recent technical validation report by the Enterprise Strategy Group (ESG) mentioned that “improving operational efficiency” is the overarching theme driving interest in and adoption of AI/ML. And that’s no wonder, as organizations using optimization technology to make critical business decisions have seen millions of dollars in cost savings and other benefits such as improved customer service and lower inventory.1

Many IBM clients are focused on accelerating data science projects and seeking ways to automate the AI lifecycle. They are also infusing prediction and optimization capabilities into decision-making and deploying enterprise AI across any cloud. For enterprises pursuing these goals, it’s essential to choose a future-proof architecture. That’s where IBM technology can make a real difference. For example, IBM Cloud Pak® for Data is an open data and AI platform, designed as an integrated, fully governed multicloud environment where organizations can keep data secure at its source and add preferred data and analytics microservices.

Furthermore, with IBM Watson® Studio, organizations can capitalize on the power of prescriptive analytics using IBM Decision Optimization. This unique offering enables organizations to apply AI techniques to simplify the optimization modeling process, thereby reducing decision-making time. It’s also the rare enterprise AI offering that enables organizations to With it, data science teams can solve complex problems using optimization technology and machine learning within a unified environment.

Watson Studio helps data science teams choose between visual modeling tools — such as SPSS Modeler — and open source tools such as Python, R and Scala. Based on their available skill sets, businesses can use either intuitive design methods to build their algorithms or models, go with open source tools, or choose a mix of both.

What makes the IBM approach unique is not only its mix of open source languages and intuitive graphical tools, but also the APIs to other available IBM AI services such as SpeechToText, ToneAnalyzer and Visual Recognition. This ecosystem empowers intuitive interactions with human users and access to all kinds of unstructured data, including speech, videos, photos and geospatial data.

With Watson Studio for IBM Cloud Pak for Data, IBM delivers a comprehensive answer for the needs of enterprises that want to drive innovation, improve operational efficiency and maximize growth. With it, data scientists are empowered to experiment and build minimum viable products (MVP) and proofs of concept (POC) in the public cloud through Watson Studio within IBM Cloud Pak for Data as a Service. And then when they are successful, they can move that success story to the internal private cloud on IBM Cloud Pak for Data — getting the same tool and same reliability with cloud-based elasticity, open source and Spark, all within the enterprise network.

To learn more, read this analyst report and see how Watson Studio can help predict and optimize your business outcomes.

1 Source: 2018 INFORMS Franz Edelman Award finalists selected from leading analytics teams around the world

The post Accelerate efficiency gains with optimization and AI appeared first on Journey to AI Blog.

Governed ModelOps with Anaconda and IBM Cloud Pak® for Data

Using open source packages and libraries during the development stage for artificial intelligence and machine learning (AI/ML) models can enable data scientists to capitalize on the latest innovations. But these packages and libraries also pose security and governance challenges for enterprises.

Given the excitement and growth in data science and AI, companies around the world have been developing AI/ML models on a large scale over the past few years. However, the reality is that many of these models never make it to production. Ultimately, this is because of difficult requirements, such as legal compliance, that are needed before models can be put into production.

Unmanaged Open Source technology comes with risk

According to Anaconda’s 2020 State of Data Science report, developers and system administrators cite IT security standards as their biggest blocker to getting models into production. And a concerning 30% of respondents who have knowledge of their company’s security practices stated their organization does not have any mechanism in place to secure open source data science. Given the prevalence of Open Source software in production workflows, this creates risks that can deliver far-reaching negative impacts.

Often, admins need to ensure that their developers and data scientists use only approved packages in enterprise projects. In addition, enterprises may have their own proprietary packages that also need to be made available to data scientists. How can enterprises operationalize machine learning models for production use and align with necessary compliance and internal requirements?

Supporting enterprise data science

IBM and Anaconda have partnered to integrate Anaconda Team Edition with IBM Cloud Pak® for Data to address these challenges. Adding the benefits of Anaconda to your ModelOps strategy allows admins to block, exclude, and include packages according to enterprise standards. You can also keep vulnerabilities and unreliable software out of your data science and machine learning pipeline.

A model is more than just a set of weights — it includes the code, libraries, and packages used to build and execute the inference operations of the model. Because of this, it’s essential to ensure that the specific set of libraries and versions of those libraries are consistent between the model developer’s environment, the model validator’s environment, and the final deployment platform. This is a genuine challenge of reproducibility that the Anaconda package ecosystem attempts to address effectively.

With Anaconda Repository for IBM Cloud Pak for Data, admins can govern access to open source packages based upon users, groups, and roles. To ensure developers and data scientists only use approved packages, customers may block access to packages on the internet from the IBM Cloud Pak for Data environment, forcing all package loading to go through Anaconda Repository for IBM. Anaconda Repository for IBM caches packages originating from the internet and allows admins to upload a customer’s proprietary packages alongside them. Packages are served up securely and with consistent performance.

As visualized in the picture below, custom runtime environments can be defined to load packages from Conda channels served by Anaconda Repository for IBM Cloud Pak for Data, to run Notebooks and Scripts using these packages. Alternatively, code in Notebooks or Scripts can load approved packages via Conda.

Joint customers using IBM Anaconda Repository for IBM Cloud Pak for Data have already begun securely managing and governing open source packages and libraries for production models. Examples of these use cases are opioid abuse tracking, COVID case analysis, and data analytics for education programs. The possibilities for AI and ML models are endless, but ultimately make for a quicker way to take advantage of open source innovation without data or security risks.

Next steps

Join this upcoming webinar on July 14 to learn more about governed ModelOps and how IBM Anaconda Repository for IBM Cloud Pak for Data can help you manage and secure open source data science in the enterprise. Register now.

The post Governed ModelOps with Anaconda and IBM Cloud Pak® for Data appeared first on Journey to AI Blog.

The Rise and Fall of the Traditional Data Enterprise

In this contributed article, David Richards, Co-founder of WANdisco, believe we are seeing the clear signs of the death of the data enterprise as we have known it. When we look back at the death knells for Dell, EMC, HP, Cisco and IBM – it is hard not to read a similar future in the tea leaves of companies like Snowflake and Palantir after their wildly successful IPOs, and of Databricks with its highly-anticipated public offering.