#Databricks Jobs

[rajaniesh]

Maximize Efficiency: New Monitoring and Alerting Tools in Databricks Workflows

Navigating complex data workflows can be tough, with uncertainties at every turn. Ensuring data accuracy, finding performance issues, and keeping pipelines reliable can be tough tasks.

As workflows become more complex, the need for effective monitoring and timely alerts grows. That’s where Databricks steps in, offering specialized Monitoring and Alerting features to tackle these challenges and ensure smooth data journeys. Navigating complex data workflows can be tough, with uncertainties at every turn. Ensuring data accuracy, finding performance issues, and keeping pipelines…

[xiaq]

If one more person at work asks me to do something today, I will cry.

I’ll do it.

But I will cry.

[dostoyevsky-official]

The Young, Inexperienced Engineers Aiding Elon Musk's Government Takeover

WIRED has identified six young men—all apparently between the ages of 19 and 24, according to public databases, their online presences, and other records—who have little to no government experience and are now playing critical roles in Musk’s so-called Department of Government Efficiency (DOGE) project, tasked by executive order with “modernizing Federal technology and software to maximize governmental efficiency and productivity.” The engineers all hold nebulous job titles within DOGE, and at least one appears to be working as a volunteer. The engineers are Akash Bobba, Edward Coristine, Luke Farritor, Gautier Cole Killian, Gavin Kliger, and Ethan Shaotran. None have responded to requests for comment from WIRED. Representatives from OPM, GSA, and DOGE did not respond to requests for comment. [...] Kliger, whose LinkedIn lists him as a special advisor to the director of OPM and who is listed in internal records reviewed by WIRED as a special advisor to the director for information technology, attended UC Berkeley until 2020; most recently, according to his LinkedIn, he worked for the AI company Databricks. His Substack includes a post titled “The Curious Case of Matt Gaetz: How the Deep State Destroys Its Enemies,” as well as another titled “Pete Hegseth as Secretary of Defense: The Warrior Washington Fears.”

these people are nazis orchestrating an illegal, unconstitutional takeover of government agencies and tapping into your personal data. they need to be arrested, charged with crimes, before that doxxed, harassed, etc.

[govindhtech]

Google Cloud’s BigQuery Autonomous Data To AI Platform

BigQuery automates data analysis, transformation, and insight generation using AI. AI and natural language interaction simplify difficult operations.

The fast-paced world needs data access and a real-time data activation flywheel. Artificial intelligence that integrates directly into the data environment and works with intelligent agents is emerging. These catalysts open doors and enable self-directed, rapid action, which is vital for success. This flywheel uses Google's Data & AI Cloud to activate data in real time. BigQuery has five times more organisations than the two leading cloud providers that just offer data science and data warehousing solutions due to this emphasis.

Examples of top companies:

With BigQuery, Radisson Hotel Group enhanced campaign productivity by 50% and revenue by over 20% by fine-tuning the Gemini model.

By connecting over 170 data sources with BigQuery, Gordon Food Service established a scalable, modern, AI-ready data architecture. This improved real-time response to critical business demands, enabled complete analytics, boosted client usage of their ordering systems, and offered staff rapid insights while cutting costs and boosting market share.

J.B. Hunt is revolutionising logistics for shippers and carriers by integrating Databricks into BigQuery.

General Mills saves over $100 million using BigQuery and Vertex AI to give workers secure access to LLMs for structured and unstructured data searches.

Google Cloud is unveiling many new features with its autonomous data to AI platform powered by BigQuery and Looker, a unified, trustworthy, and conversational BI platform:

New assistive and agentic experiences based on your trusted data and available through BigQuery and Looker will make data scientists, data engineers, analysts, and business users' jobs simpler and faster.

Advanced analytics and data science acceleration: Along with seamless integration with real-time and open-source technologies, BigQuery AI-assisted notebooks improve data science workflows and BigQuery AI Query Engine provides fresh insights.

Autonomous data foundation: BigQuery can collect, manage, and orchestrate any data with its new autonomous features, which include native support for unstructured data processing and open data formats like Iceberg.

Look at each change in detail.

User-specific agents

It believes everyone should have AI. BigQuery and Looker made AI-powered helpful experiences generally available, but Google Cloud now offers specialised agents for all data chores, such as:

Data engineering agents integrated with BigQuery pipelines help create data pipelines, convert and enhance data, discover anomalies, and automate metadata development. These agents provide trustworthy data and replace time-consuming and repetitive tasks, enhancing data team productivity. Data engineers traditionally spend hours cleaning, processing, and confirming data.

The data science agent in Google's Colab notebook enables model development at every step. Scalable training, intelligent model selection, automated feature engineering, and faster iteration are possible. This agent lets data science teams focus on complex methods rather than data and infrastructure.

Looker conversational analytics lets everyone utilise natural language with data. Expanded capabilities provided with DeepMind let all users understand the agent's actions and easily resolve misconceptions by undertaking advanced analysis and explaining its logic. Looker's semantic layer boosts accuracy by two-thirds. The agent understands business language like “revenue” and “segments” and can compute metrics in real time, ensuring trustworthy, accurate, and relevant results. An API for conversational analytics is also being introduced to help developers integrate it into processes and apps.

In the BigQuery autonomous data to AI platform, Google Cloud introduced the BigQuery knowledge engine to power assistive and agentic experiences. It models data associations, suggests business vocabulary words, and creates metadata instantaneously using Gemini's table descriptions, query histories, and schema connections. This knowledge engine grounds AI and agents in business context, enabling semantic search across BigQuery and AI-powered data insights.

All customers may access Gemini-powered agentic and assistive experiences in BigQuery and Looker without add-ons in the existing price model tiers!

Accelerating data science and advanced analytics

BigQuery autonomous data to AI platform is revolutionising data science and analytics by enabling new AI-driven data science experiences and engines to manage complex data and provide real-time analytics.

First, AI improves BigQuery notebooks. It adds intelligent SQL cells to your notebook that can merge data sources, comprehend data context, and make code-writing suggestions. It also uses native exploratory analysis and visualisation capabilities for data exploration and peer collaboration. Data scientists can also schedule analyses and update insights. Google Cloud also lets you construct laptop-driven, dynamic, user-friendly, interactive data apps to share insights across the organisation.

This enhanced notebook experience is complemented by the BigQuery AI query engine for AI-driven analytics. This engine lets data scientists easily manage organised and unstructured data and add real-world context—not simply retrieve it. BigQuery AI co-processes SQL and Gemini, adding runtime verbal comprehension, reasoning skills, and real-world knowledge. Their new engine processes unstructured photographs and matches them to your product catalogue. This engine supports several use cases, including model enhancement, sophisticated segmentation, and new insights.

Additionally, it provides users with the most cloud-optimized open-source environment. Google Cloud for Apache Kafka enables real-time data pipelines for event sourcing, model scoring, communications, and analytics in BigQuery for serverless Apache Spark execution. Customers have almost doubled their serverless Spark use in the last year, and Google Cloud has upgraded this engine to handle data 2.7 times faster.

BigQuery lets data scientists utilise SQL, Spark, or foundation models on Google's serverless and scalable architecture to innovate faster without the challenges of traditional infrastructure.

An independent data foundation throughout data lifetime

An independent data foundation created for modern data complexity supports its advanced analytics engines and specialised agents. BigQuery is transforming the environment by making unstructured data first-class citizens. New platform features, such as orchestration for a variety of data workloads, autonomous and invisible governance, and open formats for flexibility, ensure that your data is always ready for data science or artificial intelligence issues. It does this while giving the best cost and decreasing operational overhead.

For many companies, unstructured data is their biggest untapped potential. Even while structured data provides analytical avenues, unique ideas in text, audio, video, and photographs are often underutilised and discovered in siloed systems. BigQuery instantly tackles this issue by making unstructured data a first-class citizen using multimodal tables (preview), which integrate structured data with rich, complex data types for unified querying and storage.

Google Cloud's expanded BigQuery governance enables data stewards and professionals a single perspective to manage discovery, classification, curation, quality, usage, and sharing, including automatic cataloguing and metadata production, to efficiently manage this large data estate. BigQuery continuous queries use SQL to analyse and act on streaming data regardless of format, ensuring timely insights from all your data streams.

Customers utilise Google's AI models in BigQuery for multimodal analysis 16 times more than last year, driven by advanced support for structured and unstructured multimodal data. BigQuery with Vertex AI are 8–16 times cheaper than independent data warehouse and AI solutions.

Google Cloud maintains open ecology. BigQuery tables for Apache Iceberg combine BigQuery's performance and integrated capabilities with the flexibility of an open data lakehouse to link Iceberg data to SQL, Spark, AI, and third-party engines in an open and interoperable fashion. This service provides adaptive and autonomous table management, high-performance streaming, auto-AI-generated insights, practically infinite serverless scalability, and improved governance. Cloud storage enables fail-safe features and centralised fine-grained access control management in their managed solution.

Finaly, AI platform autonomous data optimises. Scaling resources, managing workloads, and ensuring cost-effectiveness are its competencies. The new BigQuery spend commit unifies spending throughout BigQuery platform and allows flexibility in shifting spend across streaming, governance, data processing engines, and more, making purchase easier.

Start your data and AI adventure with BigQuery data migration. Google Cloud wants to know how you innovate with data.

[wuedk]

🚀 𝐉𝐨𝐢𝐧 𝐃𝐚𝐭𝐚𝐏𝐡𝐢'𝐬 𝐇𝐚𝐜𝐤-𝐈𝐓-𝐎𝐔𝐓 𝐇𝐢𝐫𝐢𝐧𝐠 𝐇𝐚𝐜𝐤𝐚𝐭𝐡𝐨𝐧!🚀

𝐖𝐡𝐲 𝐏𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐞? 🌟 Showcase your skills in data engineering, data modeling, and advanced analytics. 💡 Innovate to transform retail services and enhance customer experiences.

📌𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰: https://whereuelevate.com/drills/dataphi-hack-it-out?w_ref=CWWXX9

🏆 𝐏𝐫𝐢𝐳𝐞 𝐌𝐨𝐧𝐞𝐲: Winner 1: INR 50,000 (Joining Bonus) + Job at DataPhi Winners 2-5: Job at DataPhi

🔍 𝐒𝐤𝐢𝐥𝐥𝐬 𝐖𝐞'𝐫𝐞 𝐋𝐨𝐨𝐤𝐢𝐧𝐠 𝐅𝐨𝐫: 🐍 Python,💾 MS Azure Data Factory / SSIS / AWS Glue,🔧 PySpark Coding,📊 SQL DB,☁️ Databricks Azure Functions,🖥️ MS Azure,🌐 AWS Engineering

👥 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐬 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞: Senior Consultant (3-5 years) Principal Consultant (5-8 years) Lead Consultant (8+ years)

📍 𝐋𝐨𝐜𝐚𝐭𝐢𝐨𝐧: 𝐏𝐮𝐧𝐞 💼 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: 𝟑-𝟏𝟎 𝐘𝐞𝐚𝐫𝐬 💸 𝐁𝐮𝐝𝐠𝐞𝐭: ₹𝟏𝟒 𝐋𝐏𝐀 - ₹𝟑𝟐 𝐋𝐏𝐀

ℹ 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐔𝐩𝐝𝐚𝐭𝐞𝐬: https://chat.whatsapp.com/Ga1Lc94BXFrD2WrJNWpqIa

Register now and be a part of the data revolution! For more details, visit DataPhi.

[caffeineandviolins]

PART TWO

The six men are one part of the broader project of Musk allies assuming key government positions. Already, Musk’s lackeys—including more senior staff from xAI, Tesla, and the Boring Company—have taken control of the Office of Personnel Management (OPM) and General Services Administration (GSA), and have gained access to the Treasury Department’s payment system, potentially allowing him access to a vast range of sensitive information about tens of millions of citizens, businesses, and more. On Sunday, CNN reported that DOGE personnel attempted to improperly access classified information and security systems at the US Agency for International Development and that top USAID security officials who thwarted the attempt were subsequently put on leave. The Associated Press reported that DOGE personnel had indeed accessed classified material.“What we're seeing is unprecedented in that you have these actors who are not really public officials gaining access to the most sensitive data in government,” says Don Moynihan, a professor of public policy at the University of Michigan. “We really have very little eyes on what's going on. Congress has no ability to really intervene and monitor what's happening because these aren't really accountable public officials. So this feels like a hostile takeover of the machinery of governments by the richest man in the world.”Bobba has attended UC Berkeley, where he was in the prestigious Management, Entrepreneurship, and Technology program. According to a copy of his now-deleted LinkedIn obtained by WIRED, Bobba was an investment engineering intern at the Bridgewater Associates hedge fund as of last spring and was previously an intern at both Meta and Palantir. He was a featured guest on a since-deleted podcast with Aman Manazir, an engineer who interviews engineers about how they landed their dream jobs, where he talked about those experiences last June.

Coristine, as WIRED previously reported, appears to have recently graduated from high school and to have been enrolled at Northeastern University. According to a copy of his résumé obtained by WIRED, he spent three months at Neuralink, Musk’s brain-computer interface company, last summer.Both Bobba and Coristine are listed in internal OPM records reviewed by WIRED as “experts” at OPM, reporting directly to Amanda Scales, its new chief of staff. Scales previously worked on talent for xAI, Musk’s artificial intelligence company, and as part of Uber’s talent acquisition team, per LinkedIn. Employees at GSA tell WIRED that Coristine has appeared on calls where workers were made to go over code they had written and justify their jobs. WIRED previously reported that Coristine was added to a call with GSA staff members using a nongovernment Gmail address. Employees were not given an explanation as to who he was or why he was on the calls.

Farritor, who per sources has a working GSA email address, is a former intern at SpaceX, Musk’s space company, and currently a Thiel Fellow after, according to his LinkedIn, dropping out of the University of Nebraska—Lincoln. While in school, he was part of an award-winning team that deciphered portions of an ancient Greek scroll.AdvertisementKliger, whose LinkedIn lists him as a special adviser to the director of OPM and who is listed in internal records reviewed by WIRED as a special adviser to the director for information technology, attended UC Berkeley until 2020; most recently, according to his LinkedIn, he worked for the AI company Databricks. His Substack includes a post titled “The Curious Case of Matt Gaetz: How the Deep State Destroys Its Enemies,” as well as another titled “Pete Hegseth as Secretary of Defense: The Warrior Washington Fears.”Killian, also known as Cole Killian, has a working email associated with DOGE, where he is currently listed as a volunteer, according to internal records reviewed by WIRED. According to a copy of his now-deleted résumé obtained by WIRED, he attended McGill University through at least 2021 and graduated high school in 2019. An archived copy of his now-deleted personal website indicates that he worked as an engineer at Jump Trading, which specializes in algorithmic and high-frequency financial trades.Shaotran told Business Insider in September that he was a senior at Harvard studying computer science and also the founder of an OpenAI-backed startup, Energize AI. Shaotran was the runner-up in a hackathon held by xAI, Musk’s AI company. In the Business Insider article, Shaotran says he received a $100,000 grant from OpenAI to build his scheduling assistant, Spark.

Are you a current or former employee with the Office of Personnel Management or another government agency impacted by Elon Musk? We’d like to hear from you. Using a nonwork phone or computer, contact Vittoria Elliott at vittoria_elliott@wired.com or securely at velliott88.18 on Signal.“To the extent these individuals are exercising what would otherwise be relatively significant managerial control over two very large agencies that deal with very complex topics,” says Nick Bednar, a professor at University of Minnesota’s school of law, “it is very unlikely they have the expertise to understand either the law or the administrative needs that surround these agencies.”Sources tell WIRED that Bobba, Coristine, Farritor, and Shaotran all currently have working GSA emails and A-suite level clearance at the GSA, which means that they work out of the agency’s top floor and have access to all physical spaces and IT systems, according a source with knowledge of the GSA’s clearance protocols. The source, who spoke to WIRED on the condition of anonymity because they fear retaliation, says they worry that the new teams could bypass the regular security clearance protocols to access the agency’s sensitive compartmented information facility, as the Trump administration has already granted temporary security clearances to unvetted people.This is in addition to Coristine and Bobba being listed as “experts” working at OPM. Bednar says that while staff can be loaned out between agencies for special projects or to work on issues that might cross agency lines, it’s not exactly common practice.“This is consistent with the pattern of a lot of tech executives who have taken certain roles of the administration,” says Bednar. “This raises concerns about regulatory capture and whether these individuals may have preferences that don’t serve the American public or the federal government.”

These men just stole the personal information of everyone in America AND control the Treasury. Link to article.

Akash Bobba

Edward Coristine

Luke Farritor

Gautier Cole Killian

Gavin Kliger

Ethan Shaotran

Spread their names!

[kamalkafir-blog]

Databricks Architect - L1

Job title: Databricks Architect – L1 Company: Wipro Job description: information, visit us at http://www.wipro.com. Databricks Architect · Should have minimum of 10+ years of experience… · Must have skills – DataBricks, Delta Lake, pyspark or scala spark, Unity Catalog · Good to have skills – Azure and/or AWS Cloud… Expected salary: Location: Bangalore, Karnataka Job date: Sun, 01 Jun 2025…

[korshubudemycoursesblog]

Master the Future: Become a Databricks Certified Generative AI Engineer

What if we told you that one certification could position you at the crossroads of AI innovation, high-paying job opportunities, and technical leadership?

That’s exactly what the Databricks Certified Generative AI Engineer certification does. As generative AI explodes across industries, skilled professionals who can bridge the gap between AI theory and real-world data solutions are in high demand. Databricks, a company at the forefront of data and AI, now offers a credential designed for those who want to lead the next wave of innovation.

If you're someone looking to validate your AI engineering skills with an in-demand, globally respected certification, keep reading. This blog will guide you through what the certification is, why it’s valuable, how to prepare effectively, and how it can launch or elevate your tech career.

Why the Databricks Certified Generative AI Engineer Certification Matters

Let’s start with the basics: why should you care about this certification?

Databricks has become synonymous with large-scale data processing, AI model deployment, and seamless ML integration across platforms. As AI continues to evolve into Generative AI, the need for professionals who can implement real-world solutions—using tools like Databricks Unity Catalog, MLflow, Apache Spark, and Lakehouse architecture—is only going to grow.

This certification tells employers that:

You can design and implement generative AI models.

You understand the complexities of data management in modern AI systems.

You know how to use Databricks tools to scale and deploy these models effectively.

For tech professionals, data scientists, ML engineers, and cloud developers, this isn't just a badge—it's a career accelerator.

Who Should Pursue This Certification?

The Databricks Certified Generative AI Engineer path is for:

Data Scientists & Machine Learning Engineers who want to shift into more cutting-edge roles.

Cloud Developers working with AI pipelines in enterprise environments.

AI Enthusiasts and Researchers ready to demonstrate their applied knowledge.

Professionals preparing for AI roles at companies using Databricks, Azure, AWS, or Google Cloud.

If you’re familiar with Python, machine learning fundamentals, and basic model deployment workflows, you’re ready to get started.

What You'll Learn: Core Skills Covered

The exam and its preparation cover a broad but practical set of topics:

🧠 1. Foundation of Generative AI

What is generative AI?

How do models like GPT, DALL·E, and Stable Diffusion actually work?

Introduction to transformer architectures and tokenization.

📊 2. Databricks Ecosystem

Using Databricks notebooks and workflows

Unity Catalog for data governance and model security

Integrating MLflow for reproducibility and experiment tracking

🔁 3. Model Training & Tuning

Fine-tuning foundation models on your data

Optimizing training with distributed computing

Managing costs and resource allocation

⚙️ 4. Deployment & Monitoring

Creating real-time endpoints

Model versioning and rollback strategies

Using MLflow’s model registry for lifecycle tracking

🔐 5. Responsible AI & Ethics

Bias detection and mitigation

Privacy-preserving machine learning

Explainability and fairness

Each of these topics is deeply embedded in the exam and reflects current best practices in the industry.

Why Databricks Is Leading the AI Charge

Databricks isn’t just a platform—it’s a movement. With its Lakehouse architecture, the company bridges the gap between data warehouses and data lakes, providing a unified platform to manage and deploy AI solutions.

Databricks is already trusted by organizations like:

Comcast

Shell

HSBC

Regeneron Pharmaceuticals

So, when you add a Databricks Certified Generative AI Engineer credential to your profile, you’re aligning yourself with the tools and platforms that Fortune 500 companies rely on.

What’s the Exam Format?

Here’s what to expect:

Multiple choice and scenario-based questions

90 minutes total

Around 60 questions

Online proctored format

You’ll be tested on:

Generative AI fundamentals

Databricks-specific tools

Model development, deployment, and monitoring

Data handling in an AI lifecycle

How to Prepare: Your Study Blueprint

Passing this certification isn’t about memorizing definitions. It’s about understanding workflows, being able to apply best practices, and showing proficiency in a Databricks-native AI environment.

Step 1: Enroll in a Solid Practice Course

The most effective way to prepare is to take mock tests and get hands-on experience. We recommend enrolling in the Databricks Certified Generative AI Engineer practice test course, which gives you access to realistic exam-style questions, explanations, and performance feedback.

Step 2: Set Up a Databricks Workspace

If you don’t already have one, create a free Databricks Community Edition workspace. Explore notebooks, work with data in Delta Lake, and train a simple model using MLflow.

Step 3: Focus on the Databricks Stack

Make sure you’re confident using:

Databricks Notebooks

MLflow

Unity Catalog

Model Serving

Feature Store

Step 4: Review Key AI Concepts

Brush up on:

Transformer models and attention mechanisms

Fine-tuning vs. prompt engineering

Transfer learning

Generative model evaluation metrics (BLEU, ROUGE, etc.)

What Makes This Certification Unique?

Unlike many AI certifications that stay theoretical, this one is deeply practical. You’ll not only learn what generative AI is but also how to build and manage it in production.

Here are three reasons this stands out:

✅ 1. Real-world Integration

You’ll learn deployment, version control, and monitoring—which is what companies care about most.

✅ 2. Based on Industry-Proven Tools

Everything is built on top of Databricks, Apache Spark, and MLflow, used by data teams globally.

✅ 3. Focus on Modern AI Workflows

This certification keeps pace with the rapid evolution of AI—especially around LLMs (Large Language Models), prompt engineering, and GenAI use cases.

How It Benefits Your Career

Once certified, you’ll be well-positioned to:

Land roles like AI Engineer, ML Engineer, or Data Scientist in leading tech firms.

Negotiate a higher salary thanks to your verified skills.

Work on cutting-edge projects in AI, including enterprise chatbots, text summarization, image generation, and more.

Stand out in competitive job markets with a Databricks-backed credential on your LinkedIn.

According to recent industry trends, professionals with AI certifications earn an average of 20-30% more than those without.

Use Cases You’ll Be Ready to Tackle

After completing the course and passing the exam, you’ll be able to confidently work on:

Enterprise chatbots using foundation models

Real-time content moderation

AI-driven customer service agents

Medical imaging enhancement

Financial fraud detection using pattern generation

The scope is broad—and the possibilities are endless.

Don’t Just Study—Practice

It’s tempting to dive into study guides or YouTube videos, but what really works is practice. The Databricks Certified Generative AI Engineer practice course offers exam-style challenges that simulate the pressure and format of the real exam.

You’ll learn by doing—and that makes all the difference.

Final Thoughts: The Time to Act Is Now

Generative AI isn’t the future anymore—it’s the present. Companies across every sector are racing to integrate it. The question is:

Will you be ready to lead that charge?

If your goal is to become an in-demand AI expert with practical, validated skills, earning the Databricks Certified Generative AI Engineer credential is the move to make.

Start today. Equip yourself with the skills the industry is hungry for. Stand out. Level up.

👉 Enroll in the Databricks Certified Generative AI Engineer practice course now and take control of your AI journey.

🔍 Keyword Optimiz

[ascendient-learning]

Master the Machines: Learn Machine Learning with Ascendient Learning 

Why Machine Learning Skills Are in High Demand 

Machine learning is at the core of nearly every innovation in technology today. From personalized product recommendations and fraud detection to predictive maintenance and self-driving cars, businesses rely on machine learning to gain insights, optimize performance, and make smarter decisions. As organizations generate more data than ever before, the demand for professionals who can design, train, and deploy machine learning models is rising rapidly across industries. 

Ascendient Learning: The Smartest Path to ML Expertise 

Ascendient Learning is a trusted provider of machine learning training, offering courses developed in partnership with top vendors like AWS, IBM, Microsoft, Google Cloud, NVIDIA, and Databricks. With access to official courseware, experienced instructors, and flexible learning formats, Ascendient equips individuals and teams with the skills needed to turn data into action. 

Courses are available in live virtual classrooms, in-person sessions, and self-paced formats. Learners benefit from hands-on labs, real-world case studies, and post-class support that reinforces what they’ve learned. Whether you’re a data scientist, software engineer, analyst, or IT manager, Ascendient has a training path that fits your role and future goals. 

Training That Matches Real-World Applications 

Ascendient Learning’s machine learning curriculum spans from foundational concepts to advanced implementation techniques. Beginners can start with introductory courses like Machine Learning on Google Cloud, Introduction to AI and ML, or Practical Data Science and Machine Learning with Python. These courses provide a strong base in algorithms, supervised and unsupervised learning, and model evaluation. 

For more advanced learners, courses such as Advanced Machine Learning, Generative AI Engineering with Databricks, and Machine Learning with Apache Spark offer in-depth training on building scalable ML solutions and integrating them into cloud environments. Students can explore technologies like TensorFlow, Scikit-learn, PyTorch, and tools such as Amazon SageMaker and IBM Watson Studio. 

Gain Skills That Translate into Real Impact 

Machine learning isn’t just a buzzword. It's transforming the way organizations work. With the right training, professionals can improve business forecasting, automate time-consuming processes, and uncover patterns that would be impossible to detect manually. 

In sectors like healthcare, ML helps identify treatment risks and recommend care paths. In retail, it powers dynamic pricing and customer segmentation. In manufacturing, it predicts equipment failure before it happens. Professionals who can harness machine learning contribute directly to innovation, efficiency, and growth. 

Certification Paths That Build Career Momentum 

Ascendient Learning’s machine learning training is also aligned with certification goals from AWS, IBM, Google Cloud, and Microsoft. Certifications such as AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate, and Google Cloud Certified – Professional ML Engineer validate your skills and demonstrate your readiness to lead AI initiatives.  

Certified professionals often enjoy increased job opportunities, higher salaries, and greater credibility within their organizations. Ascendient supports this journey by offering prep materials, expert guidance, and access to labs even after the course ends. 

Machine Learning with Ascendient 

Machine learning is shaping the future of work, and those with the skills to understand and apply it will lead the change. Ascendient Learning offers a clear, flexible, and expert-led path to help you develop those skills, earn certifications, and make an impact in your career and organization. 

Explore Ascendient Learning’s machine learning course catalog today. Discover the training that can turn your curiosity into capability and your ideas into innovation.

For more information visit: https://www.ascendientlearning.com/it-training/topics/ai-and-machine-learning

[siri0007]

Boost your career with AccentFuture's Databricks online training. Learn from industry experts, master real-time data analytics, and get hands-on experience with Databricks tools. Flexible learning, job-ready skills, and certification support included.

[hubertdudek]

Databricks: what’s new in April 2025? Updates & Features Explained! #databricks Databricks, What’s New in Databricks? April 2025 Updates & Features Explained! 📌 Key Highlights for This Month: - *00:04* PowerBI task - Refresh PowerBI from Databricks - *01:36* SQL task values - Pass SELECT result to workflow - *05:38* Cost-optimized jobs - Serverless standard mode - *06:34* Google Sheets - Query Databricks - *07:48* Git for dashboards - *08:38* Genie sampling - Genie can read data - *11:22* UC functions with PyPl libraries - *12:22* Anomaly detection - *15:02* PII scanner - Data classification - *16:13* Turn off Hive metastore - *17:17* AI builder - Extract data and more - *21:12* AI query with schema - *22:41* PyDABS - *23:28* ALTER statement - *24:03* TEMP VIEWS in DLT - *24:18* Apps on behalf of the user ============================= 📚 *Notebooks from the video:* 🔗 [GitHub Repository](https://ift.tt/S13qG0b) 🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬. https://www.youtube.com/@hubert_dudek/?sub_confirmation=1 🔗 Support Me Here! ☕Buy me a coffee: https://ift.tt/9qIpuET ✨ Explore Databricks AI insights and workflows—read more: https://ift.tt/1djZykN ============================= 🎬Suggested videos for you: ▶️ [What’s new in January 2025](https://www.youtube.com/watch?v=JJiwSplZmfk) ▶️ [What’s new in February 2025](https://www.youtube.com/watch?v=tuKI0sBNbmg) ▶️ [What’s new in March 2025](https://youtu.be/hJD7KoNq-uE) ============================= 📚 **New Articles for Further Reading:** - 📝 *More on Databricks into Google Sheets:* 🔗 [Read the full article](https://ift.tt/3cfjJLy) - 📝 *More on Anomaly Detection & Data Freshness:* 🔗 [Read the full article](https://ift.tt/5RB4bWM) - 📝 *More on Goodbye to Hive Metastore:* 🔗 [Read the full article](https://ift.tt/lxjpoRS) - 📝 *More on Databricks Refresh PowerBI Semantic Model:* 🔗 [Read the full article](https://ift.tt/8JAfSvZ) - 📝 *More on ResponseFormat in AI Batch Inference:* 🔗 [Read the full article](https://ift.tt/B07yqRT) ============================= 🔎 Related Phrases: #databricks #bigdata #dataengineering #machinelearning #sql #cloudcomputing #dataanalytics #ai #azure #googlecloud #aws #etl #python #data #database #datawarehouse via Hubert Dudek https://www.youtube.com/channel/UCR99H9eib5MOHEhapg4kkaQ April 22, 2025 at 02:17AM

[kamalkafir-blog]

Lead Software Engineer

Job title: Lead Software Engineer Company: Nike Job description: Software Engineer – Platforms with a passion for cloud-native development and platform ownership. You are someone who thrives… of AWS Cloud Services, Kubernetes, DevOps, Databricks, Python and other cloud-native platforms. You should be an excellent… Expected salary: Location: Karnataka Job date: Wed, 21 May 2025 03:13:56 GMT Apply…

[aitoolswhitehattoolbox]

Data Science Engineer

Job Description: We are looking for Senior Data Science Engineer for our Advanced Analytics and Big Data Team. The…’s in Computer Science, Data Engineering, or related field. Proficiency in Azure Databricks for data processing and pipeline… Apply Now

[ascendient-learning]

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