Tag: ai
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Preparing for Machine Learning Readiness to Unlock its Full Potential
With the rapid advancement of artificial intelligence (AI) and machine learning (ML), companies need to understand and evaluate their readiness to adopt these technologies and drive material business outcomes.
To assess a company’s machine learning readiness, we must consider several key factors, including:
● Vision/strategy
● Data availability
● Expertise, infrastructure and resources
● MLOps and governanceIn this article, I will review these three key factors in assessing ML readiness and how they can help your organization prepare.
1. Vision/Strategy
A company’s vision for its AI/ML strategy is essential to consider when assessing its machine learning readiness. This includes ensuring that the expected AI transformation aligns with the company’s strategic goals and that it will ultimately be meaningful for the business. To evaluate this, organizations should consider their overall strategy for AI adoption and determine how it fits into their broader business objectives. Additionally, companies should identify specific use cases and applications of AI that support their goals and how those can be implemented to drive business outcomes. A company that has already woven an AI/ML strategy into its business planning is in a far better position to assess its ML readiness.
2. Data Availability
I believe data availability is another crucial factor in assessing ML readiness. Machine learning algorithms require vast amounts of data for training and validation to offer real value. It would be challenging for a company to successfully implement machine learning without access to sufficient data, or they’d risk a model that cannot be trained effectively and that would, in turn, perform poorly. Companies must consider the quality and quantity of data at their disposal, as well as any potential future data sources when assessing their readiness for this important element of machine learning. I also think organizations should consider the feasibility of collecting and integrating data from various sources, whether internal or external, to have the most impactful machine learning initiative.
3. Expertise, Infrastructure and Resources
It’s no secret machine learning requires a diverse set of skills, including data science, programming and statistics, and every organization must be sure they have all the necessary talent and resources when building that function. These resources include data preprocessing, model development, model validation, deployment, monitoring, maintenance and the ability to train and onboard new employees to oversee AI/ML programs. Companies should also enable collaboration across these different units and roles. If a company chooses to outsource machine learning projects to third parties, the skills needed internally may need to shift toward project management and collaboration with external teams, as well as a nuanced and sophisticated understanding of the business problem, industry and technical capabilities of the vendors, to ensure proper execution.
A company’s systems and technology stack are key to any operation. This includes selecting the right technologies and tools to enable the end-to-end creation and use of AI/ML-powered analytical applications. Before adopting ML, companies should evaluate their current stack to determine if it’s equipped to handle AI, including the availability of hardware, software and programming languages. Organizations should also consider the costs associated with selecting and implementing the right technology stack for AI/ML, including the costs of any necessary upgrades or new investments. Decision makers should also research and evaluate different technology vendors, their products and services, and find the one that best suits their needs in regards to cost, value and alignment with long-term goals.
4. MLOps and Governance
To be machine learning-ready, I find it important for companies to have machine learning ops (MLOps) in place. MLOps is a set of practices that allows companies to manage the end-to-end machine learning life cycle. It covers the entire machine learning process, from data preparation to model deployment and monitoring. Implementing MLOps includes continuous integration and delivery, model versioning and management and monitoring and alerting. Additionally, MLOps practices such as automated testing, model evaluation and deployment can help organizations improve the overall efficiency and effectiveness of their machine-learning efforts over time.
A company’s governance structure for AI/ML adoption is another factor to consider when assessing machine learning readiness. Companies should evaluate their existing governance structures and determine if they are equipped to handle the implementation of AI, including data governance, compliance and risk management. We must also consider how we will manage the deployment and scaling of AI, including the development of policies and procedures to govern the use of AI and the identification of internal stakeholders and decision-makers who will be responsible for its implementation.
Ultimately, I believe the key to machine learning readiness is to assess current capabilities, identify areas for improvement and develop strategies to address them. By taking the time to assess their machine learning readiness, companies can ensure they are in the best position to take full advantage of the benefits that machine learning can provide.
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Atlassian Embeds Generative AI Across Cloud App Portfolio
At its Team 23 conference today, Atlassian announced it is adding an artificial intelligence (AI) capability to the enterprise editions of its cloud portfolio to automate a wide range of routine tasks.
Sherif Mansour, distinguished product manager for Atlassian, said Atlassian Intelligence uses natural language to drive a virtual agent that resolves routine help desk issues from within Slack or Microsoft Teams. Atlassian Intelligence can also identify incidents that have been previously resolved along with issues that are related to one another.
In addition, IT teams can also leverage Atlassian Intelligence to create summaries for projects being tracked using the company’s Jira project management software, noted Mansour.
The overall goal is to streamline incident management processes while also making it simpler for application stakeholders to collaboratively participate in software development, he added.
Generative AI capabilities are likely to be infused into almost every imaginable application going forward, but Atlassian is clearly at the forefront of an effort to leverage AI to improve productivity. For example, instead of spending time handling routine issues that increase the amount of daily toil, IT teams should be able to spend more time resolving more complex issues, noted Mansour.
The cumulative effect of AI should result in a significant decrease in the backlog of tasks that many IT teams would otherwise have to manually perform, he noted.
It’s not clear just yet how much generative AI will impact the way software is developed and how IT is managed, but it’s already apparent that fundamental change is on the way. The Atlassian Intelligence approach is intended to augment the capabilities of IT teams so they can do more with the same number of or fewer personnel.
The challenge IT teams will face is identifying tasks they couldn’t previously accomplish simply because there wasn’t enough time available.
Fundamentally, generative AI changes the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. That makes it possible for anyone to make use of large language models used to create generative AI platforms to, for example, automate a routine task or, one day soon, create code.
At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of IT organizations, the ultimate impact should involve less drudgery as many of the tasks that make managing IT tedious are eliminated.
Hopefully, as IT continues to evolve, there will still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has been for the past four decades or more.
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AI Chatbots: Game Changers for DevOps Transformations
In today’s fast-paced digital world, organizations are constantly looking for ways to accelerate their software development processes and ensure the seamless integration of their development and operations teams. DevOps has emerged as a popular methodology to achieve this goal, but even with its adoption, many companies still face challenges in their transformation journey. AI chatbots can revolutionize DevOps transformations, making them more efficient and effective than ever before.
How AI ChatBots Improve DevOps Transformations
Enhance collaboration by automating routine tasks and facilitating communication between teams: AI chatbots can handle repetitive tasks, freeing up team members to focus on more critical issues while also providing a centralized communication hub for both development and operations teams.
Expedite incident management by providing real-time alerts and smart recommendations: AI chatbots can analyze logs and system metrics to quickly identify issues and suggest the most appropriate resolution steps, reducing downtime and improving overall system performance.
Streamline deployment and monitoring processes using AI-driven automation: AI chatbots can intelligently manage deployment pipelines, ensuring that the right code gets deployed to the right environments while also monitoring system health and performance in real-time.
Offer real-time analytics and insights into the development pipeline for informed decision-making: AI chatbots can gather and analyze data from various sources, providing valuable insights that help teams make better decisions related to code quality, release schedules and resource allocation.
Identify skill gaps and recommend targeted training resources for continuous improvement: AI chatbots can analyze team performance and individual skill sets, helping organizations identify areas for improvement and providing personalized learning resources to close those gaps.Roadmap to Implement AI Chatbots in DevOps Transformations
Evaluate your organization’s current DevOps maturity and identify areas for improvement: By assessing your existing processes and infrastructure, you can better understand the gaps where AI chatbots can make the most significant impact.
Select an AI chatbot platform that aligns with your organization’s technology stack and goals: Choose a platform that complements your existing tools and supports your specific DevOps objectives to ensure seamless integration and adoption.
Define and prioritize use cases for AI chatbot implementation in your DevOps processes: Identify the key areas where AI chatbots can provide the most value, such as incident management or deployment automation and prioritize their implementation accordingly.
Develop a phased implementation plan that includes integration, testing and deployment of AI chatbots: Create a step-by-step plan to integrate AI chatbots into your existing DevOps processes, ensuring proper testing and validation at each stage to minimize potential disruptions.
Train your teams on how to leverage AI chatbots effectively and continually refine their usage based on feedback and performance metrics: Provide education and training resources to help your team members understand how to use AI chatbots to their full potential and foster a culture of continuous improvement.Benefits of AI Chatbots in DevOps Transformations
AI chatbots offer several benefits that can significantly improve DevOps transformations, including:
Increased efficiency by automating manual tasks and streamlining processes: This leads to faster development cycles, quicker releases and a more agile response to changing business requirements.
Enhanced collaboration and communication among development and operations teams: AI chatbots can break down silos between teams, fostering a more collaborative and unified approach to software development and delivery.
Faster incident resolution and reduced downtime: With AI chatbots’ help, issues can be identified and resolved more quickly, minimizing the impact on end users and maintaining a high level of service quality.
Real-time insights into the development pipeline for more informed decision-making: AI chatbots enable teams to make data-driven decisions by providing real-time analytics and insights into various aspects of the development process, such as code quality, resource allocation and release timelines.
Continuous learning and improvement through AI-driven recommendations: By identifying skill gaps and suggesting targeted training resources, AI chatbots can help teams continuously improve their knowledge and expertise, ultimately leading to better performance and more successful DevOps transformations.Summary
In conclusion, AI chatbots are poised to revolutionize DevOps transformations by addressing common challenges, enhancing efficiency and fostering a culture of continuous improvement. By automating routine tasks, facilitating communication and providing valuable insights into the development pipeline, they can significantly improve the effectiveness of DevOps processes. As organizations continue to adopt and refine their DevOps strategies, incorporating AI chatbots into their workflows will become increasingly essential to remain competitive in the ever-evolving digital landscape.
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Pulumi Brings Advanced Analytics and AI to IaC Platform
Pulumi today launched a platform that leverages analytics and artificial intelligence (AI) to generate infrastructure-as-code (IaC) from natural language prompts in addition to automating infrastructure management.
Pulumi CEO Joe Duffy said Pulumi Insights will substantially improve the productivity of DevOps teams in addition to ultimately reducing the total cost of cloud computing.
At the core of Pulumi Insights is an analytics engine automation application programming interface (API) that enables DevOps teams to launch queries using a search interface or natural language prompts across more than 100 cloud computing platforms. Results can then be organized to highlight the most-used clouds and resources broken down by project and environment using built-in dashboards. There’s also a REST API that makes it possible to export that data to a data warehouse such as Snowflake, Amazon Redshift, Google BigQuery and Azure Synapse.
In addition, Pulumi has created a companion website and command line tool to make it possible to create code for provisioning infrastructure. That uses large language models like those at the core of generative AI platforms such as GitHub CoPilot or ChatGPT that promise to eliminate much of the toil associated with writing code.
Pulumi has been making a case for an alternative approach to IaC tools, such as Terraform, that gives enterprise IT organizations more control over how IT environments are provisioned. Too many developers with limited cybersecurity expertise are provisioning infrastructure themselves, resulting in misconfigurations that cybercriminals can then easily exploit.
The Pulumi approach also makes it simpler for IT teams to create platform engineering teams that can centrally apply guardrails that prevent those issues from arising without slowing down the rate at which applications are being built and deployed, noted Duffy.
There’s also a much greater focus on improving the productivity of DevOps teams during uncertain economic times as organizations look to either limit or reduce the number of full-time employees they hire or retain.
For one reason or another, DevOps teams are going to be required to find ways to become more productive as the number of applications being built and deployed continues to increase. AI clearly has a major role to play in enabling organizations to achieve that goal. However, before organizations can reliably benefit from those advancements, they will need to ensure the accuracy of the data those AI models use to surface recommendations and generate code. Otherwise, no matter how advanced these platforms are, the results will be suboptimal.
In the meantime, IT organizations will need to revisit how IaC tools are being employed in an era where the focus on securing software supply chains has dramatically increased after some high-profile breaches. The challenge is that, in addition to all the applications currently being developed, there are thousands more that were deployed by developers without the same emphasis on cybersecurity.
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Can ChatGPT Fix Bugs? ‘Wolverine’ Dev Says YES
Welcome to
The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Instead of AI aids for programming, what about for debugging? The pseudonymous “BioBootloader” says he’s persuaded GPT-4 to make his code self heal. (more…)
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Harnessing the Power of AI Chatbots for Continuous Testing
Artificial intelligence (AI) is revolutionizing the way we develop and test software, especially with the advent of AI chatbots. These intelligent agents have the potential to streamline and automate continuous testing while improving software quality. However, software and QA managers must understand the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes. This blog explains crucial aspects to help your team harness the full potential of AI-driven continuous testing.
The Urgency of Integrating AI Chatbots Into Continuous Testing
• Increasing complexity of software applications: Modern software applications are becoming increasingly complex, which makes manual testing time-consuming, error-prone and costly. AI chatbots can analyze vast amounts of data in real-time, identify patterns and learn from the testing process, making them invaluable for continuous testing.
• Fast-paced development and market demands: Today’s competitive market demands faster release cycles and shorter time-to-market. AI chatbots can help you accelerate testing and development cycles, allowing your team to focus on delivering high-quality software promptly.
• The growing importance of customer experience: AI chatbots can simulate user interactions and predict user behavior, helping to improve user experiences by identifying potential issues before they become real problems.Opportunities for AI Chatbots in Continuous Testing
• Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy.
• Enhanced defect detection: AI chatbots can identify defects early in the development process, reducing the overall cost and time spent fixing them.
• Efficient resource allocation: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively, leading to increased productivity.Pitfalls to Avoid When Implementing AI Chatbots
• Over-reliance on AI: AI chatbots are powerful tools, but they should not replace human testers completely. Human intuition, creativity and critical thinking are essential for effective testing.
• Insufficient training data: To work effectively, AI chatbots require quality training data. Ensure that your chatbot is trained with diverse and representative datasets to minimize the risk of biased or inaccurate results.
• Integration challenges: Integrating AI chatbots with existing systems and processes can be challenging. Ensure that your team has a clear understanding of the integration requirements and potential issues that may arise.Best Practices for Applying AI Chatbots to Continuous Testing
• Define clear objectives: Establish clear goals and expectations for your AI chatbot to ensure that it aligns with your team’s overall testing strategy.
• Choose the right AI chatbot platform: Consider factors like cost, scalability, ease of integration and support for programming languages when selecting an AI chatbot platform.
• Continuously monitor and refine: Continuously monitor your AI chatbot’s performance and make necessary adjustments to improve its effectiveness and efficiency.
• Foster collaboration: Encourage collaboration between human testers and AI chatbots to leverage the strengths of both.Benefits of Applying AI Chatbots to Continuous Testing
• Improved test coverage and accuracy: AI chatbots can execute a large number of tests simultaneously, leading to better test coverage and increased accuracy. By automating repetitive and complex tasks, AI chatbots can effectively handle edge cases and efficiently test various scenarios, thereby reducing the likelihood of undetected issues in the software.
• Enhanced defect detection and early issue identification: AI chatbots can quickly identify defects and potential issues early in the development process, reducing the overall cost and time spent on fixing them. By leveraging machine learning and pattern recognition, AI chatbots can predict potential problems and bring them to the attention of developers, allowing for proactive resolution and improved software quality.
• Efficient resource allocation and increased productivity: By automating repetitive tasks and reducing human intervention, AI chatbots enable teams to allocate resources more effectively. Human testers can focus on higher-level tasks that require creativity, critical thinking, and domain expertise. This leads to increased productivity, as the team can concentrate on delivering high-quality software and improving user experiences.Conclusion
AI chatbots are transforming the landscape of continuous testing, offering unprecedented opportunities to improve software quality and speed up development cycles. By understanding the urgency, opportunities, pitfalls and best practices associated with integrating AI chatbots into continuous testing processes, software managers can effectively harness the potential of these intelligent agents and drive their teams toward success.
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Automation Relation
One thing that is absolutely true: This latest iteration of IT improvement is about automation. Whether making DevOps more automated, working on any of the various “X-as-code” movements or implementing AI, the idea is to create what one vendor portrayed as a “human-free zone.” A space where automation does everything that needs doing.
And the one question we all need to be asking now is simple: Where do we want that human-free zone in our organization?
The answer is inevitably, “In more places than you think,” for a wide variety of reasons. Let’s cover what you should be thinking about when answering this question – because this change is already upon us and we’re doing a lot more without so much as an approval.
- Anywhere that compliance is on the line and the action doesn’t risk crashing systems.
- Anywhere the process being performed is so rote that any member of the team can do it and everyone thinks of that process as a distraction.
- Anywhere that time is critical. Think of incident response to a DDoS or zero-day attack.
This is a very basic starting point. From here, we want to move into “anywhere we have a static list of steps to perform.” So, the above points would include things like, “If a new employee is in the marketing department, they belong in the Marketing security group. If they are further working on marketing for new products, they belong in the Marketing for product development group.” But eventually, you want to get to, “This app communicates on port 12345, so that port must be accessible, run the script to open that port.” The decision about “Should this port be open?” can then be shifted left to have more involved discussions about options and necessity. Our current world already does some of that; automation will make it standard.
And that’s what we’re getting. We’re in a world where some standardization is enabling automation, and automation is demanding that we figure out more standardization. And it is all good. There is a lot we do in IT that a trained monkey could do, and we need to let a trained system do it. The more time we have, the more problems we can solve … So let’s keep freeing up that time.
And keep rocking it. Your time is valuable precisely because you keep the organization running. Freeing up that valuable time just makes you a greater asset to the organization and makes your job less boring as a bonus.
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New Relic Bets on AI to Advance Observability
New Relic is preparing to extend the capabilities of its observability platform by making use of both additional machine learning algorithms and the ChatGPT generative artificial intelligence (AI) platform.
Peter Pezaris, senior vice president for strategy and experience at New Relic, said during an interview on an episode of TechStrong TV that a prototype of an AI model created using machine learning algorithms will automatically notify developers when code in a production environment is not performing well—right within their integrated development environment (IDE). The notification will then take developers to the precise line of code identified as the root cause of the issue, he said.
DevOps teams will need to add two lines of code to applications running in production environments to take advantage of this capability. New Relic also plans to use a similar approach to integrate with generative AI platforms such as ChatGPT to create suggestions for fixing code, he added. ChatGPT now makes it possible to create entire programs in ways that output HTML and JavaScript code, so DevOps teams should expect to be able to use the platform to create natural language queries that invoke the proprietary query language New Relic makes available for its observability platform, he noted. Conversely, ChatGPT will make it possible to explain how a piece of code functions in plain language, Pezaris added.
Previously, New Relic employed a similar approach to integrate its observability platform with machine learning operations (MLOps) platforms used to build and deploy AI models. This was part of a larger effort to integrate MLOps and DevOps workflows, added Pezaris.
These AI extensions to the New Relic platform are part of a larger effort to extend the reach of the New Relic One observability platform further left toward developers. Most recently, New Relic added a free CodeStream module to its observability platform to give developers access to metrics and telemetry data that will enable them to write higher-quality code faster.
That approach eliminates the need to wait for IT operations teams to surface issues that usually don’t manifest until long after the code in question was originally developed. The overall goal is to provide development teams with frictionless access to observability data at every stage of the software development life cycle to reduce mean-time-to-detection (MTTD) and mean-time-to-resolution (MTTR) of issues.
Organizations should also be able to reduce technical debt faster by making it easier to identify issues that, for example, adversely impact application performance.
In general, most organizations are still in the early stages of achieving full-stack observability. A recent New Relic survey found only 27% of respondents have achieved full-stack observability, and only 5% claimed they have a mature observability practice in place. A third (33%) of respondents also said they still primarily detect outages manually or based on complaints, the survey found.
On the plus side, the survey also found nearly three-quarters of respondents said C-suite executives in their organization are advocates of observability, and more than three-quarters of respondents (78%) saw observability as a key enabler for achieving core business goals. However, more than half (52%) of respondents said they experienced high-business-impact outages once per week or more and 29% said they take more than an hour to resolve those outages.
The hope is, of course, that by augmenting DevOps teams with AI capabilities, the number of those outages will be dramatically reduced in the months and years ahead.
