Dominick Battiato’s Post

AI might replace or significantly impact a few fun ones (take this with a pinch of salt): 1. Jira 2. Scrum 3. Software estimates 4. Stack Overflow 5. Accounting and taxes 6. Expense and bill tracking 7. Meetings about meetings 8. "We need to talk" emails 9. Most aspects of Agile methodology 10. Design Systems 11. Writing code 12. Websites (at least in their current form) 13. Programming languages 14. Adobe products 15. Salesforce and most CRM systems 16. Email 17. Cloud engineering roles 18. AWS, GCP, Azure certifications 19. Checkpoints, retros, PRDs 20. KPIs 21. Some aspects of business operations 22. Certain types of digital labor 23. Empathetic chatbots for businesses 24. Consumer AI products (e.g., Microsoft's Copilot AI chatbot) 25. Aspects of search engine functionality 26. Some roles in consumer marketing and product development Anything I missed? #ai #jobs #business #future

People often ask me, 'How should one decide which AI tools to pick for their product development lifecycle, especially in mid-sized to large B2B/SaaS enterprise companies?' Based on my observations, here’s an effective approach: 1. When exploring AI tools for product development teams, integration is crucial. Tools like GitHub Copilot and Microsoft Azure DevOps stand out because they fit seamlessly into existing workflows, minimizing disruptions, and ensuring continuity. ◘Important KPIs: Integration time, User adoption rate, Workflow disruption incidents. 2. Security is another top priority. Companies like Equifax and Target have faced significant challenges due to a lack of robust security, leading to data breaches and financial losses. This is why tools like AWS CodeGuru, with its high threat detection rate, are highly recommended for maintaining data safety and compliance. ◘Important KPIs: Threat detection rate, Compliance adherence rate, Security incident frequency. 3. Scalability also plays a vital role. As projects grow, the tools must keep pace. TensorFlow and Google Cloud AI are excellent examples, handling increased workloads efficiently, which ensures smooth operations regardless of project size. ◘Important KPIs: Performance under load, Scalability metrics (e.g., horizontal/vertical scaling efficiency), System availability. 4. Finally, the importance of ROI cannot be overstated. The right AI tools can significantly enhance efficiency and reduce costs. For example, using Google Cloud has been reported by some companies to increase development speed and reduce defect-related costs, demonstrating a clear return on investment. ◘Important KPIs: Development speed improvement (% increase in time to deploy rate), defect-related cost reduction (% reduction in defects), Overall cost savings (release by release comparisions) The ultimate North Star metric for selecting AI tools for product development teams should be Time to Market (TTM). This metric encapsulates the overall effectiveness of the AI tools in accelerating development cycles, improving productivity, and ensuring timely delivery of high-quality products. By focusing on these key criteria—seamless integration, robust security, scalability, and measurable ROI—and keeping the North Star metric in mind, product teams can make informed decisions that enhance their development lifecycle and drive overall success. #Leadership #AIStrategy #ROI #TimeToMarket #ProductDevelopment #ProductManagement

With all the buzz about AI in the news, its natural for everyone to aspire to have a career in this field. The good news is that there is space for everyone. How do you transition from your existing role? Here are some tips.. 🕴️ Business Professional -> Power User Skill up with prompt engineering to use AI in your business, either for automation or better content. 📊 Business Analyst -> Citizen DataScientist AI use cases can benefit tremendously from knowledge of business. Business analysts can easily skill up to use AutoML to extend analytics with predictive capabilities. 👩🏾💻 Programmer -> Data Scientist/Data Engineer Programmers just need to learn data science techniques and skill up to translate business problems to data science problems or gain knowledge of transforming datasets. 🕸️ IT Infrastructure/DevOps -> ML Engineer (MLOPS) Models developed by data scientists need to be deployed on cloud infra securely. Learn technologies such as MLflow or ZenML in addition to your existing skills in CI/CD development. 🗓️ Project Manager -> AI Success Manager Successful AI projects needs co-ordination between diverse teams as well as business stakeholders. Learn basics of AI and also gain understanding of the workflow from problem definition to deployment of models.

Machine Learning Deployment with DevOps Using machine learning with DevOps makes it easy. You can move models from development to production. It ensures they are scalable, reliable, and efficient. By adding DevOps to the machine learning lifecycle, organizations can automate processes. These include model training, testing, and deployment. This makes for faster time-to-market and fewer manual errors. DevOps principles include continuous integration and continuous deployment (CI/CD). They make it easy to add machine learning models to existing software. Automation tools ensure consistent deployment across environments. This approach fosters collaboration between data scientists, developers, and operations teams. It leads to stronger and easier-to-keep-up machine learning apps. These apps can adapt to changing business needs. For more info read : https://lnkd.in/gw2XQrBP #nixontechnologiesllc #nixon #nixontechnologysystems #ITservice #nixontech #enterprisesintegration #dataanalytics #cloudcomputing #ITconsultantinindia #USAITconsultant #bestitconsultant #usitconsultant #CRM #dataengineering #softwarejobsinusa #itjobsinusa #usaconsultant #itjobsconsultant #ITInfrastructure2024 #SecOps #SecurityManagement #dataSecurity #CyberSecurity #datascience #cloud #logistics #devops #automachine #frontend #ML 

AI is everywhere! From personalized recommendations on your phone to smart homes, it's transforming our daily lives. Is your skillset keeping up? Data Analysis (SQL, Python, Spark): Unlocks insights from the massive data AI generates. Cloud Platforms (GCP, AWS, Azure): Build and deploy AI applications at scale. Full-Stack Development (Java, MEAN): Creates user interfaces for AI-powered tools. Data Integration (ETL): Prepares data for AI analysis. Project Management (PMP, Scrum): Leads AI development projects efficiently. Digital Marketing (SEO, PPC): Reaches customers in the AI-powered digital landscape. HR Skills: Manages the changing workforce with AI automation. Are you ready for the AI era?

𝐌𝐋𝐎𝐩𝐬 𝐯𝐬. 𝐃𝐞𝐯𝐎𝐩𝐬: 𝑵𝒂𝒗𝒊𝒈𝒂𝒕𝒊𝒏𝒈 𝑨𝑰 𝒖𝒔𝒆 𝒄𝒂𝒔𝒆𝒔 𝒊𝒏 𝑯𝒆𝒂𝒍𝒕𝒉𝒄𝒂𝒓𝒆 In the fast-evolving landscape of healthcare AI, the convergence of MLOps and DevOps is creating waves of innovation. While both have their own strengths, the real magic lies in their harmonious collaboration! 🏥 Let's unravel the synergies and differences through a compelling healthcare use case. 🌐 ⏩𝐃𝐞𝐯𝐎𝐩𝐬 𝐛𝐮𝐢𝐥𝐝𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 #DevOps (Development and Operations) are the concepts that combine the working of software development and operations, aiming to provide a collaborative approach to performing development and IT operations team tasks together. ⏩𝐌𝐋𝐎𝐩𝐬 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐬 𝐭𝐡𝐞 𝐀𝐈 𝐦𝐚𝐠𝐢𝐜 #MLOps ( Machine Learning Operations) is a set of practices that facilitates the process of machine learning into production, combining the practices of machine learning development and efficient deployment & maintenance of machine learning models ➡️𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬🤖 ▶️ #𝐏ersonalized #𝐓reatment #𝐏lans 👩⚕️ Imagine a scenario where AI is revolutionizing patient care by tailoring treatment plans based on individual health profiles. #MLOps ensures seamless integration of machine learning models into the healthcare ecosystem, while #DevOps streamlines the development and deployment pipelines. ▶️ #𝐑apidResponse to #𝐇ealthcare #𝐄mergencies🚑 In critical situations, time is of the essence. #DevOps expedites the development and deployment of healthcare applications, while #MLOps ensures that AI-driven insights are readily available, aiding healthcare professionals in making swift, informed decisions during emergencies. ➡️ 𝐊𝐞𝐲 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬🚦 ▶️ #lifecycle #devops : Primarily centered on development and IT operations. #mlops : Extends DevOps principles, adding a layer specific to machine learning model development and deployment. ▶️ #Automation & #Monitoring #devops : Automates infrastructure and software delivery & focus on system performance. #mlops : Automating machine learning workflows and model deployment. Focuses on metrics of model performance & data quality. ▶️ #Collaboration: #devops : Promotes collaboration between development and operations teams. #mlops : Adds data scientists and ML engineers to the collaboration mix, fostering a multidisciplinary approach. As we navigate the future of healthcare AI, the synergy between MLOps and DevOps emerges as a game-changer. 🤔 𝑯𝒐𝒘 𝒅𝒐 𝒚𝒐𝒖 𝒔𝒆𝒆 𝒕𝒉𝒆 𝒊𝒏𝒕𝒆𝒓𝒔𝒆𝒄𝒕𝒊𝒐𝒏 𝒐𝒇 𝑴𝑳𝑶𝒑𝒔 𝒂𝒏𝒅 𝑫𝒆𝒗𝑶𝒑𝒔 𝒔𝒉𝒂𝒑𝒊𝒏𝒈 𝒕𝒉𝒆 𝒇𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝑨𝑰 𝒊𝒏 𝒕𝒉𝒆 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚?  💬Share your thoughts in the comments below! 👇 📢𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞" to "𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐈𝐓 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬" to stay up to date on the latest 𝐇ealthcare 𝐈𝐓 𝐀dvancement𝐬 👉 https://lnkd.in/gEsYXMr4 #AIinHealthcare Image sources (cdn.analyticsvidhya.com,,https://lnkd.in/gK_Tf2MK)

MLOps vs Devops Vs AIOPS MLOps (Machine Learning Operations): Focus: MLOps focuses specifically on the lifecycle management of machine learning models, from development and training to deployment and monitoring in production environments. Skills and Knowledge: Requires expertise in machine learning frameworks (e.g., TensorFlow, PyTorch), model deployment techniques (e.g., Docker, Kubernetes), version control for models and datasets, and monitoring tools for tracking model performance. Career Opportunities: Ideal for professionals interested in AI and machine learning, particularly those involved in deploying and maintaining ML models in production. Roles include ML engineers, data scientists specializing in deployment, and MLOps engineers. DevOps (Development Operations): Focus: DevOps aims to improve collaboration between development (Dev) and operations (Ops) teams to streamline software development, testing, and deployment processes. Skills and Knowledge: Involves knowledge of CI/CD pipelines, infrastructure as code (IaC), configuration management tools (e.g., Ansible, Chef), containerization (e.g., Docker), and cloud platforms (e.g., AWS, Azure). Career Opportunities: Widely applicable across software development and IT operations roles. DevOps engineers focus on automating and optimizing software delivery pipelines, ensuring faster and more reliable deployments. AIOps (Artificial Intelligence for IT Operations): Focus: AIOps integrates artificial intelligence and machine learning techniques into IT operations to enhance monitoring, incident management, and decision-making processes. Skills and Knowledge: Involves skills in data analytics, anomaly detection, predictive modeling, and integrating AI/ML with IT operations tools (e.g., monitoring systems, ticketing platforms). Career Opportunities: Suited for professionals in IT operations, network operations, and system administration roles who are interested in leveraging AI to improve operational efficiency, automate repetitive tasks, and proactively manage IT infrastructure. #jmrinfotech #AI #ML #DEVOPS #AWS #CLOUD

Machine Learning Software Engineer II (Remote) Agiloft Agiloft was named a Leader in the 2022 Gartner Magic Quadrant for Contract Life Cycle Management for the third year in a row. Contract Lifecycle Management (CLM) is one of the fastest-growing areas of enterprise sales, with a TAM projected to climb from $2B to $7B in the next 5 years. The Agiloft Contract Lifecycle Management platform has won dozens of awards, including the Editor's Choice award from PC Mag, for the past five years in a row. Agiloft has a highly differentiated value proposition which is uniquely appealing to enterprises: pre-built applications with a deeply configurable, no-code platform for integrated Business Process Management throughout an organization. Agiloft is pioneering the applied use of Artificial Intelligence to enable next-generation business commerce at organizations ranging from small enterprises to U.S. government agencies and Fortune 100 companies. Additionally, 99% of employees who commented on Glassdoor would recommend Agiloft to a friend. P... See the full job description on illbeback: https://lnkd.in/dixn272u #AI #ML #AIJobs #AITalent #hiring #MachineLearning #SoftwareEngineer...