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Machine Learning Software Engineer III (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/dudkaueA #AI #ML #AIJobs #AITalent #hiring #MachineLearning #SoftwareEngin...

Software architect careers are drastically shifting. With generative AI, AI's API economy, and low-code tools, most architects are open to new skills and opportunities–to help them ride the next wave of innovation. Recently, during our research in the space, we've seen 5 big trends regarding architect careers: 1️⃣Big Tech is Pushing the AI World Forward Microsoft CEO Satya Nadella believes AI will be as big as the internet. Google, AWS, IBM, Oracle, and others are all investing giant budgets and leading (or pushing) the rest of the global economy. This is a huge opportunity for architects and developers. AI is having a monstrous impact on architect and developer careers. 2️⃣Architects Now Have a Different Focus Low-code architects are much more focused on these three things versus the rest of their traditional job requirements: Productivity Cost savings Faster time to market. While these are not new, there is a drastic shift in focus–to achieve measurable business outcomes. 3️⃣The Emergence of The Prompt Architect The Prompt Engineer is the most hyped career in 2023–but what about the Prompt Architect? As automation and generative technologies grow, there will be an important role for architects who can design systems and frameworks around prompts and automations, so they work cohesively together. 4️⃣Automation is Moving up The Stack When AWS introduced S3 (IaaS)—to speed development, reduce bottlenecks, provide standardization, and offer a fully automated service—it catapulted a new category in the software industry. The same thing happened to the PaaS layer...prime examples of automation at the bottom of the stack. While UI frameworks and libraries have always offered more productivity, low-code platforms, like 8base, are automating app development 3X-5X-10X. What continues to be the hardest to automate is the ability to create business value via apps, UX, and CX. By focusing architect and developer time on business value creation, we move further up the stack. 5️⃣Tech Leaders are More Business Savvy Finally, we're seeing more and more technologists with strong business acumen. As automation expands, developers and architects may want to explore the context of their roles in wider business operations or even shift careers; there is a tremendous opportunity to do "micro versions" of what CIOs and CTOs do. What are you seeing in your career as a developer or architect? #techcareers #lowcode #software #architecture #AI #LLM

AWS recently announced the General Availability of Amazon Q, the ultimate AI-powered assistant designed to revolutionize software development and enterprise data management. Whether you're a developer or a business analyst, Amazon Q enhances productivity by automating coding, debugging, and data analysis tasks. 🔍 What Amazon Q Offers: - For Developers: Accelerate application development with capabilities like code generation, testing, debugging, and AWS resource optimization. - For Business Analysts: Leverage generative BI in Amazon QuickSight to create dashboards and visualizations using natural language, and gain AI-driven insights. - In Customer Service: Enhance interactions in Amazon Connect by providing agents with real-time recommendations and actionable insights. 🌐 Amazon Q seamlessly integrates into your enterprise systems, enabling employees to be more efficient, creative, and data-driven. Prepare for a new era of enterprise efficiency with Amazon Q. 🔗 Discover more about how Amazon Q can empower your team: https://aws.amazon.com/q/ #AmazonQ #AI #SoftwareDevelopment #BusinessIntelligence #CloudComputing #CloudyNow #AWS

🚀 Boost Your Business with Machine Learning! Looking to harness the power of machine learning? Discover the key to success in our latest blog post: https://lnkd.in/eK7yJwtm Learn: - Benefits of machine learning for your business. - What to look for in top developers. - Tips for successful hiring. Don't miss out on these insights! #MachineLearning #TechTalent #Innovation #BusinessGrowth #WeblineIndia

Will AI agents kill software engineering service companies? If to put it less dramatically, what will be the value of “traditional” software engineering service companies? How can they adapt to the future with AI agents? Let’s fantasize how it can evolve. Agents like Devin become ready for production use and can do the work of junior/middle engineers. AI agents are integrated into the teams. What companies will compete on? Some “traditional” software engineering service companies try to build their own agents, but specialized companies will likely be better at it. Moreover, these specialized companies will naturally try to grow into the providers of software engineering services through their agents. What value will “traditional” software engineering companies bring on top of agents? Initially it can be the knowledge and experience with organizing and managing engineering process as it’s much more than just writing code. Additionally, such companies have strong experts that are unlikely to be replaced by agents soon (it’s much easier to replace middle software engineer than enterprise architect). But what will happen when there are strong “agentic managers” and “agentic architects”? Why would clients want to go to “traditional” software engineering companies if “agents” companies provide the same service but cheaper? Or we may come to the situation when software engineering becomes real commodity with minimal differences between companies (some might argue that we are already there). And the only substantial competition will be in marketing and level of client service. Sure, it might not happen at all - e.g., it will turn out that we can’t create AGI yet, and there is not enough available data to train agents performing on par with senior engineers or experienced project managers. Anyway, it’s relevant question - what will be the value of software engineering service companies in the future? I personally don’t have clear answer yet. And you? #ai #ai_agents #ai_future #company

A Sr. Engg. Manager is doing Support + AI She talked about her Data Project…So I asked “What about knowledge?” All the “how-tos” that are locked In support articles, knowledgebase, And PDFs, internal docs & Confluence? She blanked out. All their conversations with their Vendor and Consultants, Were about Data. And knowledge was not a high priority. You know why? I suspect that there is a Data Hype, Far more innocuous looking than AI hype, And we are all thinking about data in isolation Because without the additional context And background of kb articles, How-tos, FAQs, Docs and Other unstructured Knowledge that Is floating Around AI and Data alone won’t make an impact We are led to believe. What do you think? #gptfy #data #ai #knowledge #salesforce

Machine Learning Operations extends DevOps functionality to address specific challenges associated with deploying and managing machine learning models. W𝗵𝘆 𝗠𝗟𝗢𝗽𝘀? Right from data preparation and model training to deployment and maintenance of models, #MLOps streamlines the end-to-end ML lifecycle. It accelerates time-to-market, ensuring consistency through version control and fostering team collaboration. Also, it offers high scalability through automated deployment, proactive maintenance, and continuous monitoring, leading to reduced costs and efficient resource utilization. Here are some of the top #MLOpsTools used to manage machine learning operations and ML model lifecycle: ✅ 𝗔W𝗦 𝗦𝗮𝗴𝗲𝗠𝗮𝗸𝗲𝗿 provides the right features for data labeling, model training, deployment, and monitoring, enabling developers to easily build, train, and deploy machine learning models at scale on AWS. ✅ 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗩𝗲𝗿𝘁𝗲𝘅 𝗔𝗜 simplifies machine learning workflows with good data preparation, model training and deployment features. ✅ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘇𝘂𝗿𝗲 𝗠𝗟 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 offers an enterprise-grade end-to-end ML lifecycle management service with a comprehensive set of tools for building, training, and deploying ML models. ✅ 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 provides a great platform for data scientists, data engineers, and ML engineers to seamlessly collaborate on large-scale data processing and machine learning tasks. ✅ 𝗠𝗟𝗳𝗹𝗼𝘄 allows developers to work with different ML libraries, offering handy tools for experiment tracking, deploying models on various environments, and packaging code into reproducible runs. Is there any other tool you like for managing MLOps? Feel free to share them below.  For an interesting comparison between MLOps and DevOps, click the link in the comments to read the full blog. #mlopsvsdevops #devops #devopstools #machinelearninganddevops #mlopsbestpractices

Salesforce Research Introduces INDICT: A Groundbreaking Framework Enhancing the Safety and Helpfulness of AI-Generated Code Across Diverse Programming Languages https://lnkd.in/dszjzW72 The Value of AI in Software Development AI has the potential to revolutionize software development by automating and speeding up the coding process. This can lead to faster and more efficient development. However, ensuring that the code produced is helpful and secure is a critical challenge. Risks and Mitigation There are risks that AI-generated code may have security vulnerabilities or could be used for harmful attacks. Mitigating these risks is crucial to fully harness the potential of AI in coding while maintaining safety. Current Methods and Limitations Current methods involve fine-tuning with safety-focused datasets and rule-based detectors. However, these approaches may not be enough to defend against sophisticated attacks and may leave vulnerabilities that can be exploited. INDICT Framework The INDICT framework enhances the safety and helpfulness of AI-generated code through a unique dual-critic system and external knowledge sources. This results in comprehensive feedback and iterative refinement of the output. Impact and Performance INDICT has shown significant improvements in safety and helpfulness metrics, achieving a 10% absolute improvement in code quality and up to 30% improvement in safety measures across diverse programming languages. Conclusion INDICT presents a groundbreaking framework for improving the safety and helpfulness of AI-generated code, setting new standards for responsible AI in coding. Explore AI Solutions Discover how AI can transform your work processes, sales, and customer engagement. Identify automation opportunities, define KPIs, select AI solutions, and implement gradually for measurable impacts on business outcomes. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #AIdevelopment #INDICTframework #AIsolutions #softwareautomation #codingsecurity #productmanagement #ai #ainews #llm #ml #startup #innovation #uxproduct #artificialintelligence #machinelearning #technology #ux #datascience #deeplearning #tech #robotics #aimarketing #bigdata #computerscience #aibusiness #automation #aitransformation

🚀 Streamline Your Machine Learning Workflow Building and deploying machine learning models is as crucial as traditional software development. Just as software undergoes updates, ML models require regular retraining and redeployment to stay effective. 🎯 Actionable Strategies for Success The goal? Near-complete automation to simplify retraining and redeploying models. 🤖 Tailored for Data Scientists and ML Engineers Designed for data scientists and ML engineers, this guide helps apply DevOps principles to ML projects, optimizing efficiency and reliability. 🔄 Understanding CI/CD Pipelines CI/CD pipelines automate the integration and delivery of code changes, ensuring they safely reach production. From unit testing to deployment, each step is meticulously managed. 🛠️ Shift to Automated Workflows Automated workflows replace manual steps, reducing the risk of human error and enabling faster, more frequent updates. 🤖 Why Use CI/CD for ML? ML models are prone to degradation and require regular updates. Automation streamlines this process, ensuring models remain effective and relevant. 🔍 Exploring the ML Lifecycle The ML lifecycle involves stages from experimentation to monitoring and retraining. Each phase plays a crucial role in maintaining model effectiveness. 🔧 Continuous Integration in ML CI in ML focuses on ensuring code quality and functionality, preparing models for subsequent stages in the ML lifecycle. 🔍 Tools for Continuous Integration GitHub Actions, CircleCI, and Jenkins are popular tools for implementing CI workflows, and simplifying code integration and testing processes. 📦 Model Code Packaging Models can be packaged as PyPI artifacts or container images, offering flexibility and reproducibility for deployment. 🔄 Continuous Training in ML Continuous training ensures models adapt to changing data, improving performance over time. Various tools and platforms facilitate this process, from self-managed solutions to dedicated ML platforms. The separation of model training and evaluation from the CI pipeline in ML is driven by several key reasons: Complexity and Resource Requirements Separation of Concerns Iterative Nature of ML Development 📊 Continuous Monitoring Continuous monitoring ensures models perform as expected in production, enabling timely responses to changes in data or model behavior. 🚀 Conclusion Navigating the complexities of ML system operation requires effective CI/CD pipelines and continuous monitoring. By embracing automation and best practices, organizations can ensure their ML models remain effective and relevant in dynamic environments. #MachineLearning #CI/CD #DevOps #Automation #DataScience #MLEngineering #BestPractices #ContinuousIntegration #ContinuousDeployment #GitHubActions #CircleCI #Jenkins #ModelTraining #ModelDeployment #Monitoring #MLLifecycle #SoftwareDevelopment #MLSystems

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗠𝗟𝗢𝗽𝘀? MLOps (Machine Learning Operations) is a set of practices that combines machine learning, DevOps, and data engineering to streamline the process of taking machine learning models from development to production. To understand MLOps, it is important to first understand the ML systems lifecycle which involves several key concepts: 1/ 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 Data sources refer to the origins from which data is collected for machine learning. These can include databases, APIs, data lakes, and external datasets (structured vs. unstructured). The choice of data sources is crucial as they directly impact the quality and relevance of the data used for training models. 2/ 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Model deployment is the process of integrating a trained machine learning model into a production environment where it can make predictions based on new data. This involves setting up the necessary infrastructure, such as APIs or web services, to serve the model to end-users or applications. 3/ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Feature engineering involves the creation and selection of relevant features (input variables) from raw data that enhance the performance of machine learning models. This process includes transforming data, handling missing values, and encoding categorical variables to ensure that the model can learn effectively from the data. 4/ 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Model development encompasses the entire process of designing (coding), training, and validating/evaluating machine learning models. This includes selecting algorithms, tuning hyperparameters, and evaluating model performance using metrics to ensure that the model meets the desired accuracy and reliability. 5/ 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 (𝗦𝗲𝗿𝘃𝗲 𝗮𝗻𝗱 𝗖𝗼𝗻𝘀𝘂𝗺𝗲) A data pipeline is a series of data processing steps that move data from its source to the machine learning model for training and inference. The "serve" aspect refers to how data is made available to the model for predictions, while "consume" refers to how the model receives and processes this data to generate outputs. Efficient data pipelines ensure that data flows seamlessly and is transformed appropriately for use in machine learning applications. These concepts are integral to MLOps, as they help streamline the lifecycle of machine learning models from development to deployment and ongoing management. #mlops #aiops #dataengineering #datapipeline #datasources Gif credit to @Deepak Bhardwaj