Latest news with #Copilot
Yahoo
6 hours ago
- Business
- Yahoo
Microsoft Stock Just Hit a New All-Time High. Should You Buy MSFT Here?
Microsoft (MSFT) rose to an all-time high on June 26, following multiple analyst price target upgrades. Shares initially hit an all-time high of $494.56 on Wednesday, June 25, closing down slightly at $492.27. Shares then picked back up the rally on Thursday, hitting a new high of $496.96. This has largely been the result of Microsoft's potential in fruitfully monetizing its artificial intelligence (AI) offerings. The company stands on the brink of an adoption wave of Copilot and Azure monetization, which could propel the stock even higher than its historic highs. Tesla's Robotaxis Reportedly Sped and Veered Into the Wrong Lanes. Does This Crush the Bull Case for TSLA Stock? 1 Dividend Stock to Buy Yielding Over 7% Ditch Big Tech and Buy These 3 Popular Stocks in 2025 Instead Our exclusive Barchart Brief newsletter is your FREE midday guide to what's moving stocks, sectors, and investor sentiment - delivered right when you need the info most. Subscribe today! While tech titans are not having an easy time on Wall Street due to fears of tariffs, Microsoft is soaring on the prospect of its lofty AI ambitions. So, should you buy Microsoft here? Major tech supergiant Microsoft (MSFT) is globally known for its Windows operating system, Office software, and Azure cloud services. Beyond its core competencies, Microsoft has also taken significant strides in AI, cybersecurity, and other enterprise solutions. The company has a market cap of $3.66 trillion. In particular, Microsoft has made significant investments in AI. Through products like Azure AI services, the company has established a significant presence in the fields of machine learning, data analysis, and automation. Microsoft has also started incorporating AI-based smart features in its 365 suite of products. Overall, the company is dedicated to making AI more accessible for everyone. Based on the company's AI prowess, Microsoft's stock has been scaling to new highs recently. Over the past 52 weeks, Microsoft's shares have gained 9.5%. More impressively, they are up nearly 27% in the last three months. By its standards, Microsoft does not have a lofty valuation. Its price sits at 37.88 times trailing earnings, which, although overvalued compared to the industry average, does not seem to be overly stretched. On April 30, Microsoft reported its third quarter results for fiscal 2025 (the quarter that ended on March 31). The company's quarterly revenue grew 13% from the prior year's period to $70.1 billion. This was better than the $68.44 billion figure that analysts were expecting. At the center of this growth was Microsoft's cloud segment. CEO Satya Nadella said that cloud and AI are 'essential inputs' to expand 'output, reduce costs, and accelerate growth' for businesses. The company's intelligent cloud segment's revenue was $26.8 billion, representing a 21% year-over-year increase and surpassing the analyst estimate of $26.17 billion. This surge was driven by 33% revenue growth for Azure and other cloud services. Microsoft's productivity and business processes segment reported revenue of $29.9 billion, up 10% year-over-year. The top-line growth was also reflected in Microsoft's bottom-line financials. The company's adjusted net income climbed by 18% annually to $25.8 billion or $3.46 per share, which was higher than the Wall Street analyst estimate of $3.22 per share. Microsoft manages to post robust growth, even though big tech companies bore the brunt of the uncertainties surrounding tariffs. This is because the company continually adjusts its investments and implements efficiency improvements to meet the demand it faces from customers. For the fourth quarter, Microsoft expects its productivity and business processes revenue to be in the range of $32.05 billion to $32.35 billion, implying 12% to 13% year-over-year growth. Its intelligent cloud revenue is anticipated to be in the range of $28.75 billion to $29.05 billion, reflecting 21% to 22% year-over-year growth. This optimism is echoed by Wall Street analysts. They expect Microsoft's EPS to climb by 13.6% year-over-year to $3.35 for Q4 FY2025. For FY2025, EPS is projected to surge 13.2% to $13.36, followed by a 12.1% growth to $14.97 in FY2026. Wall Street analysts anticipate significant growth prospects for this tech giant, particularly in monetizing its AI capabilities. As already stated, the company's stock has reached new heights due to analysts' bullishness. Wedbush analysts, led by Dan Ives, raised the price target on the stock from $515 to $600, while maintaining an 'Outperform' rating. The firm sees significant potential in the momentum surrounding Microsoft Copilot at present, as well as in Azure monetization. Ives believes this period could be Microsoft's 'shining moment.' The next fiscal year could be a 'true inflection year' for it as AI functionality expands. Citing the same reason and seeing the same hefty prospects in Microsoft's AI operations, analysts at Wells Fargo also raised the price target from $565 to $585, while keeping an 'Overweight' rating. Microsoft is earning high praise on Wall Street, with analysts giving it a consensus 'Strong Buy' rating overall. Of the 46 analysts rating the stock, a majority of 37 analysts have rated it a 'Strong Buy,' five suggest a 'Moderate Buy,' and four analysts are playing it safe with a 'Hold' rating. The consensus price target of $518.98 represents 4.4% upside from current levels. However, the Street-high price target of $626 indicates 26% upside. Microsoft might have a record-breaking year in fiscal 2026 if AI monetization gains momentum. This has the potential to propel the stock beyond the $600 mark. In line with its brand, the company continually introduces new products and services. Recently, it launched its Mu small language model, an AI tool designed to run on Neural Processing Units (NPUs) on Copilot+ PCs. With the strides in AI continuing, Microsoft may be a solid investment now. On the date of publication, Anushka Mukherji did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes. This article was originally published on Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Business Insider
11 hours ago
- Business
- Business Insider
Microsoft pushes staff to use internal AI tools more, and may consider this in reviews. 'Using AI is no longer optional.'
Microsoft is asking some managers to evaluate employees based on how much they use AI internally, and the software giant is considering adding a metric related to this in its review process, Business Insider has learned. Julia Liuson, president of the Microsoft division responsible for developer tools such as AI coding service GitHub Copilot, recently sent an email instructing managers to evaluate employee performance based on their use of internal AI tools like this. "AI is now a fundamental part of how we work," Liuson wrote. "Just like collaboration, data-driven thinking, and effective communication, using AI is no longer optional — it's core to every role and every level." Liuson told managers that AI "should be part of your holistic reflections on an individual's performance and impact." Microsoft's performance requirements vary from team to team, and some are considering including a more formal metric about the use of internal AI tools in performance reviews for its next fiscal year, according to a person familiar with the situation. This person asked not to be identified discussing private matters. These changes are meant to address what Microsoft sees as lagging internal adoption of its Copilot AI services, according to another two people with knowledge of the plans. The company wants to increase usage broadly, but also wants the employees building these products have a better understanding of the tools. In Liuson's organization, GitHub Copilot is facing increasing competition from AI coding services including Cursor. Microsoft lets employees use some external AI tools that meet certain security requirements. Staff are currently allowed to use coding assistant Replit, for example, one of the people said. A recent note from Barclays cited data suggesting that Cursor recently surpassed GitHub Copilot in a key part of the developer market. Competition among coding tools is even becoming a sticking point in Microsoft's renegotiation of its most important partnership with OpenAI. OpenAI is considering acquiring Cursor competitor Windsurf, but Microsoft's current deal with OpenAI would give it access to Windsurf's intellectual property and neither Windsurf nor OpenAI wants that, a person with knowledge of the talks said.
Yahoo
13 hours ago
- Business
- Yahoo
The biggest AI companies you should know
AI continues to be the hottest trend in tech, and it doesn't appear to be going away anytime soon. Microsoft (MSFT), Google (GOOG, GOOGL), Meta (META), and Amazon (AMZN) continue to debut new AI-powered software capabilities while leaders from other AI firms split off to form their own startups. But the furious pace of change also makes it difficult to keep track of the various players in the AI space. With that in mind, we're breaking down what you need to know about the biggest names in AI and what they do. From OpenAI ( to Perplexity ( these are the AI companies you should be following. Microsoft-backed OpenAI helped put generative AI technology on the map. The company's ChatGPT bot, released in late 2022, quickly became one of the most downloaded apps in the world. Since then, the company has launched its own search engine, 4o image generator, a video generator, and a file uploader that allows you to ask the bot to summarize the content of your documents, as well as access to specialized first- and third-party GPT bots. Microsoft uses OpenAI's various large language models (LLM) in its Copilot and other services. Apple (AAPL) also offers access to ChatGPT as part of its Apple Intelligence and Visual Intelligence services. But there's drama behind the scenes. OpenAI is working to restructure its business into a public benefit corporation overseen by its nonprofit arm, which will allow it to raise more capital. To do that, it needs Microsoft's sign-off, but the two sides are at loggerheads over the details of the plan and what it means for each company. In the meantime, both OpenAI and Microsoft are reportedly working on products that will compete with each other's existing offerings. Microsoft offers its own AI models, and OpenAI is developing a productivity service, according to The Information. Still, the pairing has been lucrative for both tech firms. During its most recent quarterly earnings call, Microsoft said AI revenue was above expectations and contributed 16 percentage points of growth for the company's Azure cloud business. OpenAI, meanwhile, saw its annualized revenue run rate balloon to $10 billion as of June, according to Reuters. That's up from $5.5 billion in Dec. 2024. OpenAI offers a limited free version of its ChatGPT bot, as well as ChatGPT Plus, which costs $20 per month, and enterprise versions of the app. Google's Gemini offers search functionality using the company's Gemini 2.5 family of AI models. You can choose between using Gemini Flash for quick searches or Gemini Pro, which is meant for deep research and coding. Gemini doesn't just power Google's Gemini app. It's pervasive across Google's litany of services. Checking your email or prepping an outline in Docs, Gemini is there. Get an AI Overviews result when using standard Google Search? That's Gemini too. Google Maps? That also takes advantage of Gemini. Chrome, YouTube, Google Flights, Google Hotels — you name it, it's using Gemini. But Google's Gemini, previously known as Bard, got off to a rough start. When Google debuted its Gemini-powered AI Overviews in May 2024, it began offering up wild statements like recommending users put glue on their pizza to help make the cheese stick. But during its I/O developer conference in May, Google showed off a number of impressive new developments for Gemini, including its updated video-generation software Veo 3 and Gemini running on prototype smart glasses. A limited version of Gemini is available to use for free. A paid tier that costs $19.99 per month gives you access to advanced AI models and integration with Google's productivity suite. A $249 subscription lets you use Google's most advanced Gemini models and 30TB of storage via Google Drive, Photos, and Gmail. Mark Zuckerberg's Meta has gone through a number of transformations over the years, from desktops to mobile to short-form video to an ill-advised detour into the metaverse. Now the company is leaning heavily into AI with the goal of dominating the space so it doesn't have to rely on technologies from rivals like Apple and Google, like it did during the smartphone wars. It helps that Meta has a massive $70 billion in cash and marketable securities on hand that it can deploy at a moment's notice and data from billions of users to train its models. Unlike most competitors, Meta is offering its Llama family of AI models as open-weights software, which means companies and researchers can adjust the models as they see fit, though they don't get access to the original training data. More people developing apps and tools that use Llama means Meta effectively gets to see how its software can evolve without having to do extra work. But Llama 4 Behemoth, the company's massive LLM, has been delayed by months, according to the Wall Street Journal. To seemingly offset similar delays moving forward, Meta is scooping up AI talent left and right. The company invested $14.3 billion in Scale AI and hired its CEO, Alexandr Wang. Meta also grabbed Safe Superintelligence CEO Daniel Gross and former GitHub CEO Nat Friedman. Meta's AI, like Google's, runs across its various platforms, including Facebook, Instagram, and WhatsApp, as well as its smart glasses. Founded in 2021 by siblings and ex-OpenAI researchers Dario and Daniela Amodei, Anthropic ( is an AI company focused on safety and trust. The duo split off from OpenAI over disagreements related to AI safety and the company's general direction. Like OpenAI, Anthropic has accumulated some deep-pocketed backers, including Amazon and Google, which have already poured billions into the company. The company's Claude models are available across various cloud services. Its Anthropic chat interface offers a host of capabilities, including web searches, coding, as well as writing and drafting documents. Anthropic also allows users to build what it calls artifacts, which are documents, games, lists, and other bite-sized pieces of content you can share online. In June, a federal judge sided with Anthropic in a case in which the company was accused of breaking copyright law by training its models on copyrighted books. But Anthropic allegedly downloaded pirated versions of some books and will now face trial over the charge. Elon Musk's xAI, a separate company from X Corp, which owns X (formerly Twitter), offers its own Grok chatbot and Grok AI models. Users can access Grok through a website, app, and X. Like other AI services, it allows users to search for information via the web, generate text and images, and write code. The company trains Grok on its Colossus supercomputer, which xAI said will eventually include 1 million GPUs. According to Musk, Grok was meant to have an edgy flair, though like other chatbots, it has been caught spreading misinformation. Musk previously co-founded OpenAI with Sam Altman but left the company after disagreements over its future and leadership positions. In 2024, Musk filed a lawsuit against OpenAI and Sam Altman over the AI company's effort to restructure itself as a for-profit organization. Musk says OpenAI has abandoned its original mission statement to build AI to benefit humanity and instead is working to enrich itself and Microsoft. Perplexity takes a real-time web search approach to AI chatbots, serving as a true threat to the likes of Google and its own search engine. Headed by CEO Aravind Srinivas, who previously worked as a research scientist at OpenAI, Perplexity allows users to choose from a number of different AI models, including OpenAI's GPT-4.1, Anthropic's Claude 4.0 Sonnet, Google's Gemini 2.5 Pro, xAI's Grok 3, and the company's own Sonoar. Perplexity also provides users with Discover pages for topics like finance, sports, and more, with stories curated by both the Perplexity team and outside contractors. As with other AI companies, Perplexity has been criticized by media organizations for allegedly using their content without permission. Dow Jones is suing the company over the practice. Email Daniel Howley at dhowley@ Follow him on X/Twitter at @DanielHowley. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Business Insider
21 hours ago
- Business
- Business Insider
From podcasts to fatherhood, here's how CEOs are using AI
Microsoft's Satya Nadella Microsoft has invested heavily in AI, including introducing its Copilot assistant in 2023, inking a $13 billion partnership with OpenAI in 2024, and creating teams dedicated to developing the tech. CEO Satya Nadella, who took charge of the company in 2014, previously discussed how recent developments in AI will change workflows and humans' cognitive labor. For Nadella, AI has become a necessary part of his life, both in and out of the office, according to Bloomberg. During an interview published in May, Nadella said he enjoys podcasts but doesn't listen to them. Instead, he uploads the transcripts of podcasts to the Copilot app on his phone so he can discuss the content with a voice assistant during his commute. When he reaches Microsoft's headquarters in Washington State, Nadella uses Copilot to summarize his Outlook and Teams messages. He utilizes at least 10 custom agents from Copilot Studio to help with meeting prep and research. OpenAI's Sam Altman Sam Altman, the CEO of OpenAI, has become one of Silicon Valley's most prominent tech giants thanks to OpenAI 's premier product, ChatGPT. The company launched a chatbot demo in 2022, and it quickly went viral on social media as people inquired about everything from diets to recipes. Over the last three years, OpenAI has shared more advanced GPT programs with users and is working to expand its global reach despite competition from Chinese tech companies like DeepSeek. This January, President Donald Trump announced a $500 billion private-sector investment in AI infrastructure called Stargate. OpenAI was among the companies asked to help with that project. So, it's unsurprising that Altman uses AI to streamline tasks his his personal life. Altman appeared on Adam Grant's "ReThinking" podcast this January, saying, "Honestly, I use it in the boring ways." Altman said the AI bots help him process emails or summarize documents. The tech has also helped him with fatherhood. During an OpenAI podcast interview published this month, Altman said he used AI "constantly" after welcoming his first child in February. "Clearly, people have been able to take care of babies without ChatGPT for a long time," Altman said. "I don't know how I would have done that." Nvidia's Jensen Huang Another major player on the global tech scene is Jensen Huang, Nvidia's CEO. The California-based company is one of the most valuable in the world, with a market value of over $3 trillion, according to Google Finance. The company is focused on designing and manufacturing hardware, including chips and graphical processing units to assist AI. During the 28th annual Milken Institute Global Conference in May, Huang told the audience he uses AI programs to learn new concepts. "I use it as a tutor every day," Huang said. "In areas that are fairly new to me, I might say, 'Start by explaining it to me like I'm a 12-year-old,' and then work your way up into a doctorate-level over time." AI's ability to rapidly collect, analyze, and communicate information could close the tech gap, according to Huang. "In this room, it's very unlikely that more than a handful of people know how to program with C++," Huang said. "Yet 100% of you know how to program an AI, and the reason for that is because the AI will speak whatever language you wanted to speak." In a 2024 interview with Wired, Huang said he uses Perplexity and ChatGPT "almost every day" for research. "For example, computer-aided drug discovery. Maybe you would like to know about the recent advancements in computer-aided drug discovery," Huanng said. "And so you want to frame the overall topic so that you could have a framework, and from that framework, you could ask more and more specific questions. I really love that about these large language models." Apple's Tim Cook Apple is navigating the global AI market under CEO Tim Cook, who announced Apple Intelligence — a generative AI system — at the company's Worldwide Developers Conference in 2024. He also unveiled a slew of other AI-based features at the time, including the Image Playground and the ability to remove unwanted background details from photos. Cook, who became CEO in 2011, publicly spoke about how he uses AI day-to-day in a 2024 interview with The Wall Street Journal. He said Apple Intelligence helps him summarize long emails. "If I can save time here and there, it adds up to something significant across a day, a week, a month," Cook told the outlet. "It's changed my life," he says. "It really has." One year earlier, Cook appeared on "Good Morning America" and said he was "excited" about developments in AI. "I think there's some unique applications for it and you can bet that it's something that we're looking at closely," Cook said. Zillow's Jeremy Wacksman Real estate tech companies like Zillow are also leaning into AI. The company announced in 2023 that it implemented an "AI-powered natural-language search" to help users navigate the website. CEO Jeremy Wacksman, like the other executives, has begun using AI to be more efficient. "I spend a lot of time either catching up on meetings I've missed or on asynchronous documentation," Wacksman told The New York Times Dealbook. "You can tell ChatGPT, 'Treat me like my role. Here's all this data — summarize it for me the way I would need to know going forward,' and you can get a personalized summary. That's just — that's far more valuable to me than to try to read a transcript at one-and-a-half speed or watch a video at one-and-a-half speed." Wacksman added that he wants Zillow staffers to experiment with the technology. "We've had what we call 'AI days,' where we showcase work and celebrate examples," Wacksman said. "We've also started weaving it into our bigger meetings, like product reviews: When a product manager-design-engineering team is prototyping, oftentimes, they're now using an AI tool called Replit. They're prototyping really quickly to get something in front of a user."
Hans India
a day ago
- Business
- Hans India
AI Tools & Skills Every Data Engineer Should Know in 2025
The lines between data engineering and artificial intelligence are increasingly blurred. As enterprises pivot towards intelligent automation, data engineers are increasingly expected to work alongside AI models, integrate machine learning systems, and build scalable pipelines that support real-time, AI-driven decision-making. Whether you're enrolled in a data engineer online course or exploring the intersection of data engineering for machine learning, the future is AI-centric, and it's happening now. In this guide, we explore the core concepts, essential skills, and advanced tools every modern AI engineer or data engineer should master to remain competitive in this evolving landscape. Foundational AI Concepts in Data Engineering Before diving into tools and frameworks, it's crucial to understand the foundational AI and ML concepts shaping the modern data engineer online course. AI isn't just about smart algorithms—it's about building systems that can learn, predict, and improve over time. That's where data engineers play a central role: preparing clean, structured, and scalable data systems that fuel AI. To support AI and machine learning, engineers must understand: Supervised and unsupervised learning models Feature engineering and data labeling Data pipelines that serve AI in real-time ETL/ELT frameworks tailored for model training Courses like an AI and Machine Learning Course or a machine learning engineer course can help engineers bridge their current skills with AI expertise. As a result, many professionals are now pursuing AI and ML certification to validate their cross-functional capabilities. One key trend? Engineers are building pipelines not just for reporting, but to feed AI models dynamically, especially in applications like recommendation engines, anomaly detection, and real-time personalization. Top AI Tools Every Data Engineer Needs to Know Staying ahead of the rapidly changing data engineering world means having the right tools that speed up your workflows, make them smarter, and more efficient. Here is a carefully curated list of some of the most effective AI-powered tools specifically built to complement and boost data engineering work, from coding and improving code to constructing machine learning pipelines at scale. 1. DeepCode AI DeepCode AI is like a turbocharged code reviewer. It reviews your codebase and indicates bugs, potential security flaws, and performance bottlenecks in real-time. Why it's helpful: It assists data engineers with keeping clean, safe code in big-scale projects. Pros: Works in real-time, supports multiple languages, and integrates well with popular IDEs. Cons: Its performance is highly dependent on the quality of the training data. Best For: Developers aiming to increase code dependability and uphold secure data streams. 2. GitHub Copilot Created by GitHub and OpenAI, Copilot acts like a clever coding buddy. It predicts lines or chunks of code as you type and assists you in writing and discovering code more efficiently. Why it's helpful: Saves time and lessens mental burden, particularly when coding in unknown codebases. Pros: Minimally supported languages and frameworks; can even suggest whole functions. Cons: Suggestions aren't perfect—code review still required. Best For: Data engineers who jump back and forth between languages or work with complex scripts. 3. Tabnine Tabnine provides context-aware intelligent code completion. It picks up on your current code habits and suggests completions that align with your style. Why it's useful: Accelerates repetitive coding tasks while ensuring consistency. Pros: Lightweight, easy to install, supports many IDEs and languages. Cons: Occasionally can propose irrelevant or too generic completions. Best For: Engineers who desire to speed up their coding with little resistance. 4. Apache MXNet MXNet is a deep learning framework capable of symbolic and imperative programming. It's scalable, fast, and versatile. Why it's useful: It's very effective when dealing with big, complicated deep learning models. Pros: Support for multiple languages, effective GPU use, and scalability. Cons: Smaller community compared to TensorFlow or PyTorch, hence less learning materials. Best For: Engineers preferring flexibility in developing deep learning systems in various languages. 5. TensorFlow TensorFlow continues to be a force to be reckoned with for machine learning and deep learning. From Google, it's an engineer's preferred choice for model training, deployment, and big data science. Why it's useful: Provides unparalleled flexibility when it comes to developing tailor-made ML models. Pros: Massive ecosystem, robust community, production-ready. Cons: Steep learning curve for beginners. Best For: Data engineers and scientists working with advanced ML pipelines. 6. TensorFlow Extended (TFX) TFX is an extension of TensorFlow that provides a full-stack ML platform for data ingestion, model training, validation, and deployment. Why it's useful: Automates many parts of the ML lifecycle, including data validation and deployment. Key Features: Distributed training, pipeline orchestration, and built-in data quality checks. Best For: Engineers who operate end-to-end ML pipelines in production environments. 7. Kubeflow Kubeflow leverages the power of Kubernetes for machine learning. It enables teams to develop, deploy, and manage ML workflows at scale. Why it's useful: Makes the deployment of sophisticated ML models easier in containerized environments. Key Features: Automates model training and deployment, native integration with Kubernetes. Best For: Teams who are already operating in a Kubernetes ecosystem and want to integrate AI seamlessly. 8. Paxata Paxata is an AI-powered data prep platform that streamlines data transformation and cleaning. It's particularly useful when dealing with big, dirty datasets. How it's useful: Automates tedious hours of data preparation with intelligent automation. Major Features: Recommends transformations, facilitates collaboration, and integrates real-time workflows. Ideal For: Data engineers who want to prepare data for analytics or ML. 9. Dataiku Dataiku is a full-stack AI and data science platform. You can visually create data pipelines and has AI optimization suggestions. Why it's useful: Simplifies managing the complexity of ML workflows and facilitates collaboration. Key Features: Visual pipeline builder, AI-based data cleaning, big data integration. Best For: Big teams dealing with complex, scalable data operations. 10. Fivetran Fivetran is an enterprise-managed data integration platform. With enhanced AI capabilities in 2024, it automatically scales sync procedures and manages schema changes with minimal human intervention. Why it's useful: Automates time-consuming ETL/ELT processes and makes data pipelines operate efficiently. Key Features: Intelligent scheduling, AI-driven error handling, and support for schema evolution. Best For: Engineers running multi-source data pipelines for warehousing or BI. These tools aren't fashionable – they're revolutionizing the way data engineering is done. Whether you're reading code, creating scalable ML pipelines, or handling large data workflows, there's a tool here that can Best suited for data engineers and ML scientists working on large-scale machine learning pipelines, especially those involving complex deep learning models. Feature / Tool DeepCode AI GitHub Copilot Tabnine Apache MXNet TensorFlow Primary Use Code Review Code Assistance Code Completion Deep Learning Machine Learning Language Support Multiple Multiple Multiple Multiple Multiple Ideal for Code Quality Coding Efficiency Coding Speed Large-Scale Models Advanced ML Models Real-Time Assistance Yes Yes Yes No No Integration Various IDEs Various IDEs Various IDEs Flexible Flexible Learning Curve Moderate Moderate Easy Steep Steep Hands-On AI Skills Every Data Engineer Should Develop Being AI-aware is no longer enough. Companies are seeking data engineers who can also prototype and support ML pipelines. Below are essential hands-on skills to master: 1. Programming Proficiency in Python and SQL Python remains the primary language for AI and ML. Libraries like Pandas, NumPy, and Scikit-learn are foundational. Additionally, strong SQL skills are still vital for querying and aggregating large datasets from warehouses like Snowflake, BigQuery, or Redshift. 2. Frameworks & Tools Learn how to integrate popular AI/ML tools into your stack: TensorFlow and PyTorch for building and training models and for building and training models MLflow for managing the ML lifecycle for managing the ML lifecycle Airflow or Dagster for orchestrating AI pipelines or for orchestrating AI pipelines Docker and Kubernetes for containerization and model deployment These tools are often highlighted in structured data engineering courses focused on production-grade AI implementation. 3. Model Serving & APIs Understand how to serve trained AI models using REST APIs or tools like FastAPI, Flask, or TensorFlow Serving. This allows models to be accessed by applications or business intelligence tools in real time. 4. Version Control for Data and Models AI projects require versioning not only of code but also of data and models. Tools like DVC (Data Version Control) are increasingly being adopted by engineers working with ML teams. If you're serious about excelling in this space, enrolling in a specialized data engineer training or data engineer online course that covers AI integration is a strategic move. Integrating Generative AI & LLMs into Modern Data Engineering The advent of Generative AI and Large Language Models (LLMs) like GPT and BERT has redefined what's possible in AI-powered data pipelines. For data engineers, this means learning how to integrate LLMs for tasks such as: Data summarization and text classification and Anomaly detection in unstructured logs or customer data in unstructured logs or customer data Metadata enrichment using AI-powered tagging using AI-powered tagging Chatbot and voice assistant data pipelines To support these complex models, engineers need to create low-latency, high-throughput pipelines and use vector databases (like Pinecone or Weaviate) for embedding storage and retrieval. Additionally, understanding transformer architectures and prompt engineering—even at a basic level—empowers data engineers to collaborate more effectively with AI and machine learning teams. If you're a Microsoft Fabric Data Engineer, it's worth noting that tools like Microsoft Synapse and Azure OpenAI are offering native support for LLM-driven insights, making it easier than ever to build generative AI use cases within unified data platforms. Want to sharpen your cloud integration skills too? Consider upskilling with niche courses like cloud engineer courses or AWS data engineer courses to broaden your toolset. Creating an AI-Centric Data Engineering Portfolio In a competitive job market, it's not just about what you know—it's about what you've built. As a data engineer aiming to specialize in AI, your portfolio must reflect real-world experience and proficiency. What to Include: End-to-end ML pipeline : From data ingestion to model serving : From data ingestion to model serving AI model integration : Real-time dashboards powered by predictive analytics : Real-time dashboards powered by predictive analytics LLM-based project : Chatbot, intelligent document parsing, or content recommendation : Chatbot, intelligent document parsing, or content recommendation Data quality and observability: Showcase how you monitor and improve AI pipelines Your GitHub should be as well-maintained as your résumé. If you've taken a data engineering certification online or completed an AI ML Course, be sure to back it up with publicly available, working code. Remember: Recruiters are increasingly valuing hybrid profiles. Those who combine data engineering for machine learning with AI deployment skills are poised for the most in-demand roles of the future. Pro tip: Complement your technical portfolio with a capstone project from a top-rated Data Analysis Course to demonstrate your ability to derive insights from model outputs. Conclusion AI is not a separate domain anymore—it's embedded in the very core of modern data engineering. As a data engineer, your role is expanding into new territory that blends system design, ML integration, and real-time decision-making. To thrive in this future, embrace continuous learning through AI and Machine Learning Courses, seek certifications like AI ML certification, and explore hands-on data engineering courses tailored for AI integration. Whether you're starting out or upskilling, taking a solid data engineer online course with an AI focus is your ticket to relevance. Platforms like Prepzee make it easier by offering curated, industry-relevant programs designed to help you stay ahead of the curve. The fusion of AI tools and data engineering isn't just a trend—it's the new standard. So gear up, build smart, and lead the future of intelligent data systems with confidence and clarity.
