How I Became A Machine Learning Engineer (No CS Degree, No Bootcamp)

Stay Ahead, Stay ONMINE

How I Became A Machine Learning Engineer (No CS Degree, No Bootcamp)

Machine learning and AI are among the most popular topics nowadays, especially within the tech space. I am fortunate enough to work and develop with these technologies every day as a machine learning engineer! In this article, I will walk you through my journey to becoming a machine learning engineer, shedding some light and advice […]

In this article, I will walk you through my journey to becoming a machine learning engineer, shedding some light and advice on how you can become one yourself!

My Background

In one of my previous articles, I extensively wrote about my journey from school to securing my first Data Science job. I recommend you check out that article, but I will summarise the key timeline here.

Pretty much everyone in my family studied some sort of STEM subject. My great-grandad was an engineer, both my grandparents studied physics, and my mum is a maths teacher.

So, my path was always paved for me.

I chose to study physics at university after watching The Big Bang Theory at age 12; it’s fair to say everyone was very proud!

At school, I wasn’t dumb by any means. I was actually relatively bright, but I didn’t fully apply myself. I got decent grades, but definitely not what I was fully capable of.

I was very arrogant and thought I would do well with zero work.

I applied to top universities like Oxford and Imperial College, but given my work ethic, I was delusional thinking I had a chance. On results day, I ended up in clearing as I missed my offers. This was probably one of the saddest days of my life.

Clearing in the UK is where universities offer places to students on certain courses where they have space. It’s mainly for students who don’t have a university offer.

I was lucky enough to be offered a chance to study physics at the University of Surrey, and I went on to earn a first-class master’s degree in physics!

There is genuinely no substitute for hard work. It is a cringy cliche, but it is true!

My original plan was to do a PhD and be a full-time researcher or professor, but during my degree, I did a research year, and I just felt a career in research was not for me. Everything moved so slowly, and it didn’t seem there was much opportunity in the space.

During this time, DeepMind released their AlphaGo — The Movie documentary on YouTube, which popped up on my home feed.

From the video, I started to understand how AI worked and learn about neural networks, reinforcement learning, and deep learning. To be honest, to this day I am still not an expert in these areas.

Naturally, I dug deeper and found that a data scientist uses AI and machine learning algorithms to solve problems. I immediately wanted in and started applying for data science graduate roles.

I spent countless hours coding, taking courses, and working on projects. I applied to 300+ jobs and eventually landed my first data science graduate scheme in September 2021.

You can hear more about my journey from a podcast.

Data Science Journey

I started my career in an insurance company, where I built various supervised learning models, mainly using gradient boosted tree packages like CatBoost, XGBoost, and generalised linear models (GLMs).

I built models to predict:

Fraud — Did someone fraudulently make a claim to profit.
Risk Prices — What’s the premium we should give someone.
Number of Claims — How many claims will someone have.
Average Cost of Claim — What’s the average claim value someone will have.

I made around six models spanning the regression and classification space. I learned so much here, especially in statistics, as I worked very closely with Actuaries, so my maths knowledge was excellent.

However, due to the company’s structure and setup, it was difficult for my models to advance past the PoC stage, so I felt I lacked the “tech” side of my toolkit and understanding of how companies use machine learning in production.

After a year, my previous employer reached out to me asking if I wanted to apply to a junior data scientist role that specialises in time series forecasting and optimisation problems. I really liked the company, and after a few interviews, I was offered the job!

I worked at this company for about 2.5 years, where I became an expert in forecasting and combinatorial optimisation problems.

I developed many algorithms and deployed my models to production through AWS using software engineering best practices, such as unit testing, lower environment, shadow system, CI/CD pipelines, and much more.

Fair to say I learned a lot.

I worked very closely with software engineers, so I picked up a lot of engineering knowledge and continued self-studying machine learning and statistics on the side.

I even earned a promotion from junior to mid-level in that time!

Transitioning To MLE

Over time, I realised the actual value of data science is using it to make live decisions. There is a good quote by Pau Labarta Bajo

ML models inside Jupyter notebooks have a business value of $0

There is no point in building a really complex and sophisticated model if it will not produce results. Seeking out that extra 0.1% accuracy by staking multiple models is often not worth it.

You are better off building something simple that you can deploy, and that will bring real financial benefit to the company.

With this in mind, I started thinking about the future of data science. In my head, there are two avenues:

Analytics -> You work primarily to gain insight into what the business should be doing and what it should be looking into to boost its performance.
Engineering -> You ship solutions (models, decision algorithms, etc.) that bring business value.

I feel the data scientist who analyses and builds PoC models will become extinct in the next few years because, as we said above, they don’t provide tangible value to a business.

That’s not to say they are entirely useless; you have to think of it from the business perspective of their return on investment. Ideally, the value you bring in should be more than your salary.

You want to say that you did “X that produced Y”, which the above two avenues allow you to do.

The engineering side was the most interesting and enjoyable for me. I genuinely enjoy coding and building stuff that benefits people, and that they can use, so naturally, that’s where I gravitated towards.

To move to the ML engineering side, I asked my line manager if I could deploy the algorithms and ML models I was building myself. I would get help from software engineers, but I would write all the production code, do my own system design, and set up the deployment process independently.

And that’s exactly what I did.

I basically became a Machine Learning Engineer. I was developing my algorithms and then shipping them to production.

I also took NeetCode’s data structures and algorithms course to improve my fundamentals of computer science and started blogging about software engineering concepts.

Coincidentally, my current employer contacted me around this time and asked if I wanted to apply for a machine learning engineer role that specialises in general ML and optimisation at their company!

Call it luck, but clearly, the universe was telling me something. After several interview rounds, I was offered the role, and I am now a fully fledged machine learning engineer!

Fortunately, a role kind of “fell to me,” but I created my own luck through up-skilling and documenting my learning. That is why I always tell people to show their work — you don’t know what may come from it.

My Advice

I want to share the main bits of advice that helped me transition from a machine learning engineer to a data scientist.

Experience — A machine learning engineer is not an entry-level position in my opinion. You need to be well-versed in data science, machine learning, software engineering, etc. You don’t need to be an expert in all of them, but have good fundamentals across the board. That’s why I recommend having a couple of years of experience as either a software engineer or data scientist and self-study other areas.
Production Code — If you are from data science, you must learn to write good, well-tested production code. You must know things like typing, linting, unit tests, formatting, mocking and CI/CD. It’s not too difficult, but it just requires some practice. I recommend asking your current company to work with software engineers to gain this knowledge, it worked for me!
Cloud Systems — Most companies nowadays deploy many of their architecture and systems on the cloud, and machine learning models are no exception. So, it’s best to get practice with these tools and understand how they enable models to go live. I learned most of this on the job, to be honest, but there are courses you can take.
Command Line — I am sure most of you know this already, but every tech professional should be proficient in the command line. You will use it extensively when deploying and writing production code. I have a basic guide you can checkout here.
Data Structures & Algorithms — Understanding the fundamental algorithms in computer science are very useful for MLE roles. Mainly because you will likely be asked about it in interviews. It’s not too hard to learn compared to machine learning; it just takes time. Any course will do the trick.
Git & GitHub — Again, most tech professionals should know Git, but as an MLE, it is essential. How to squash commits, do code reviews, and write outstanding pull requests are musts.
Specialise — Many MLE roles I saw required you to have some specialisation in a particular area. I specialise in time series forecasting, optimisation, and general ML based on my previous experience. This helps you stand out in the market, and most companies are looking for specialists nowadays.

The main theme here is that I basically up-skilled my software engineering abilities. This makes sense as I already had all the math, stats, and machine learning knowledge from being a data scientist.

If I were a software engineer, the transition would likely be the reverse. This is why securing a machine learning engineer role can be quite challenging, as it requires proficiency across a wide range of skills.

Summary & Further Thoughts

I have a free newsletter, Dishing the Data, where I share weekly tips and advice as a practising data scientist. Plus, when you subscribe, you will get my FREE data science resume and short PDF version of my AI roadmap!

Connect With Me

Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

AWS launches ‘Capabilities by Region’ to simplify planning for cloud deployments

While Microsoft Azure’s Product Availability by Region portal lists services by geography, it lacks forward-looking timelines and the unified API comparability that AWS’s Capabilities by Region delivers, Jain noted. Region Picker, which is a similar offering from Google, too, falls short on granular, future-facing service or API roadmaps and focuses

Google Cloud aims for more cost-effective Arm computing with Axion N4A

It’s not alone: AWS introduced its own Arm-based chip, Graviton, in 2018 to reduce the cost of running internal cloud workloads such as Amazon retail IT, and now 50% of new AWS instances run on it. Microsoft, too, recently developed an Arm chip, Cobalt, to run Microsoft 365 and to

Perplexity’s open-source tool to run trillion-parameter models without costly upgrades

The obvious answer would be Nvidia’s new GB200 systems, essentially one giant 72-GPU server. But those cost millions, face extreme supply shortages, and aren’t available everywhere, the researchers noted. Meanwhile, H100 and H200 systems are plentiful and relatively cheap. The catch: running large models across multiple older systems has traditionally

Arista shares Q3 financials, touts ‘golden era in networking’

“I think a lot of these designs will materialize as the standards for Ethernet are getting stronger and stronger. We now have a UEC [Ultra Ethernet Consortium] spec. You heard me talk about the Scale-Up Ethernet spec for ESUN where we can bring different work streams onto the same Ethernet

Oil Rises on Shutdown Hopes

Oil rose as a push to end the US government shutdown buoyed wider markets, with crude traders also looking toward a data-heavy week that will yield insights into whether a long-awaited global surplus is forming. West Texas Intermediate rose around 0.6% to settle above $60 a barrel after two weekly declines, while Brent closed around $64. In the US, the White House expressed support for a bipartisan deal to reopen the US government after its longest-ever shutdown. Markets took the progress as a breakthrough, with tech shares driving the equities rally. Crude has dropped in five of the past six weeks as jitters over surplus supply gained greater traction. The Organization of the Petroleum Exporting Countries and its allies have been loosening output curbs in an apparent effort to gain market share, while drillers from outside the alliance, including the US, have also been adding barrels. OPEC is due to release its monthly analysis on Wednesday, with the International Energy Agency issuing an annual energy outlook the same day, followed by its regular monthly snapshot on Thursday. US sanctions also remain in focus after the Trump administration last month targeted Russia’s Rosneft PJSC and Lukoil PJSC in a bid to raise pressure on the Kremlin to end its war in Ukraine. Governments across Europe and the Middle East are rushing to ensure Lukoil’s sprawling oil operations can keep running after the US sanctions and a quashed bid by energy merchant Gunvor Group for its assets last week. Iraq is said to have transferred operations at Lukoil’s West Qurna 2 field to two state firms in an effort to ensure production continues. Earlier in the day Lukoil declared force majeure, allowing it to exercise the right to skip contractual obligations on the field, according to a person familiar with the matter.

After rate case, Con Edison Q3 electric revenues up 10.6% on flat sales

By the numbers: Consolidated Edison Q3 2025 -1.5% Consolidated Edison Co. of New York sold 15,692 million kWh in the third quarter of 2025, down slightly from 15,923 million kWh in the same period of 2024. After adjusting for weather and other variations, the utility said delivery volumes increased 0.3%. +10.6% CECONY’s sales of electricity reached $3.73 billion in the third quarter of 2025, up from $3.38 billion in the same period last year. $688 million Utility holding company Consolidated Edison saw its third quarter net income reach $688 million compared with $588 million in the same period of 2024. Rate case drives revenues Consolidated Edison Co. of New York, the electric utility serving New York City, warned customers heading into the summer that their bills would be going up. Now those sales are showing up on the utility parent company’s bottom line. Parent company Consolidated Edison also owns Orange and Rockland Utilities and Rockland Electric Co., as well as transmission and clean energy development businesses. “Third quarter 2025 results reflect [an] increase in electric rate base at CECONY,” the full name of the New York City distribution utility, the company said in its third quarter earnings presentation. Residential sales were flat but third quarter electric sale revenues at CECONY rose more than 5%, year over year. Commercial sales rose 3% and revenues jumped 13.9%. Retrieved from Consolidated Edison. And the utility has proposed spending almost $17 billion in New York City and Westchester County to build out its gas and electric systems from 2026 to 2028. Electric system spending is about $12 billion of the total. If regulators approve, the spending plan “will fund critical infrastructure investments while keeping affordability and reliability front and center,” Chairman and CEO Tim Cawley said in a statement. “At the same time, the settlement

USA Natural Gas Price Pulls Back Before Skyrocketing

In an EBW Analytics Group report sent to Rigzone by the EBW team on Monday, Eli Rubin, an energy analyst at the company, noted that the December natural gas contract “pulled back to $4.268 [per million British thermal units (MMBtu)] intraday Friday before skyrocketing to test $4.509 [per MMBtu] this morning”. Rubin highlighted in the report that “a cold spell lifted weekend Henry Hub prices to a seven-month high of $3.76 [per MMBtu], LNG exports pushed a step-change higher to a record 18.1 Bcf/d [billion cubic feet per day], and weekend weather forecasts added eight HDDs [heating degree days] since Friday”. The EBW energy analyst went on to note in the report, however, that gas production set a record high on Sunday as Marcellus producers raised output into the first cold spell of the season. “Further, current cold weather may end mid-week, with daily weather-driven demand to slide 12.7 Bcf/d by Thursday,” Rubin added. “U.S. natural gas storage may reach Thanksgiving only slightly below 3,900 Bcf [billion cubic feet],” Rubin continued. Rubin went on to warn in the report that technical momentum may fizzle after having achieved the $4.50 per MMBtu target. “Chances for a cold December lie beyond the 1-15 day forecast window,” Rubin pointed out. “While another test of support is probable within the next 7-10 days, the bullish long-term structural outlook has limited both the duration and magnitude of any pullbacks – and may continue to offer support,” he added. In an exclusive interview with Rigzone on Monday morning, Art Hogan, Chief Market Strategist at B. Riley Wealth, said the fundamental backdrop continues to move natural gas prices higher. “Futures rose nearly three percent to around $4.45 per MMBtu, the highest since March and close to levels last seen in December 2022, lifted by strong export demand

EOG Completes $5.7B Purchase of Encino Acquisition Partners

EOG Resources Inc has consummated its takeover of Encino Acquisition Partners (EAP) from the Canada Pension Plan (CPP) Investment Board and Encino Energy for $5.7 billion subject to post-closing adjustments. “In the Utica, the integration of the Encino assets is proceeding exceptionally well, with continued incremental efficiency gains”, EOG chair and chief executive Ezra Yacob said in the company’s quarterly report. The transaction involved the purchase of CPP’s 98 percent stake and Encino Energy’s two percent stake in EAP, which the two formed 2017, CPP said May 30 announcing the deal. The acquisition grows EOG’s Utica shale position by 675,000 net acres to 1.1 million net acres with over two billion net barrels of oil equivalent undeveloped resources, Houston, Texas-based EOG said in a separate statement May 30. “Pro forma production totals 275,000 barrels of oil equivalent per day creating a leading producer in the Utica shale play”, EOG said then. “The acquisition expands EOG’s core acreage in the volatile oil window, which averages 65 percent liquids production, by 235,000 net acres for a combined contiguous position of 485,000 net acres”, EOG said at the time. “In the natural gas window, the acquisition adds 330,000 net acres along with existing natural gas production with firm transportation exposed to premium end markets. “In the northern acreage, where the company has delivered outstanding well results, EOG increases its existing average working interest by more than 20 percent”. EOG raised its regular dividend by five percent to $1.02 per share in light of the transaction. “EOG expects to generate more than $150 million of synergies in the first year driven by lower capital, operating and debt financing costs”, the May statement said. Yacob said then, “This acquisition combines large, premier acreage positions in the Utica, creating a third foundational play for EOG alongside our Delaware Basin and Eagle Ford assets”. The acquisition

JP Morgan Analysts Examine Global Oil Demand Growth

Year to date through November 4, global oil demand growth is tracking an expansion of 0.85 million barrels per day. That’s what J.P. Morgan analysts said in a report sent to Rigzone by the JPM Commodities research team recently, adding that the expansion figure is 50,000 barrels per day below the company’s estimate of 0.90 million barrels per day. Global oil demand averaged 105.8 million barrels per day in October, the analysts noted in the report, pointing out that demand “consistently trail[ed]…” their estimates by 50,000 barrels per day throughout the month. The analysts said in the report that, for the first four days of November, they estimate global demand averaged 105 million barrels per day. They noted that this was an increase of 450,000 barrels per day compared with the same period last year but 30,000 barrels per day below their forecast monthly growth of 480,000 barrels per day. “High-frequency indicators suggest that U.S. oil consumption remains subdued, with travel activity declining as daily flights dropped seven percent week over week for the period ending November 3, largely due to the ongoing shutdown,” the analysts said in the report. “Additionally, U.S. container arrivals for November point to an eight percent decrease relative to last year, marking the third consecutive month of annual decline,” they added. “In East Asia, petrochemical feedstock demand continues to be softer than usual, while industrial activity in Germany has extended its downturn into a third consecutive month,” they continued. The analysts also stated in the report, which was sent to Rigzone on November 5, that global total liquids stocks “rose by 29 million barrels last week, bringing the year to date increase to 385 million barrels”. “Regionally, OECD liquid stocks have grown by 83 million barrels, while stocks in China have increased by 88 million

How network diversity protects utility operations in an evolving landscape

From redundancy to resilience: The next level of network diversity Despite billions invested in grid modernization and widespread adoption of dual-carrier approaches, many utilities still find their communications infrastructure vulnerable to disruption. It’s a paradox: utilities have engineered redundancy across power generation and distribution, yet communications—the digital backbone of the modern grid—often lacks the comprehensive diversity needed to truly eliminate single points of failure. True grid resilience now depends on comprehensive network diversity that enables consistent quality of service, operational flexibility and long-term adaptability. The cost of connectivity blind spots As utilities expand advanced metering infrastructure (AMI), SCADA systems, distributed energy resource (DER) management and connected field operations, the strain on communications networks continues to grow. Relying on a single technology exposes utilities to significant risk—from natural disasters and carrier outages to rural coverage gaps and network congestion. The result: delayed outage restoration, compromised grid visibility and operational inefficiencies that undermine reliability and customer trust. The versatility imperative: Why utilities can’t just “upgrade” Utility communications can’t follow the consumer tech cycle. Equipment often remains in service for 20 to 30 years, even as wireless standards evolve from private radio networks to LTE, 4G, 5G and emerging spectrum such as Anterix Band 106. Each shift brings new performance expectations—but utilities can’t afford to rip and replace infrastructure every few years. Instead, they need adaptable, field-proven solutions that bridge technologies and spectrum generations, ensuring consistent, secure connectivity today while accommodating tomorrow’s innovations without interrupting critical operations. Three pillars of network diversity While many utilities have already adopted dual-carrier strategies, the expanding complexity of modern grid operations demands a renewed look at comprehensive network diversity. True resilience comes from a multi-path approach—one that enables seamless communication under any condition. Three key strategies define this new standard of reliability. A. Public-to-public redundancy Dual-radio routers

Microsoft’s In-Chip Microfluidics Technology Resets the Limits of AI Cooling

Raising the Thermal Ceiling for AI Hardware As Microsoft positions it, the significance of in-chip microfluidics goes well beyond a novel way to cool silicon. By removing heat at its point of generation, the technology raises the thermal ceiling that constrains today’s most power-dense compute devices. That shift could redefine how next-generation accelerators are designed, packaged, and deployed across hyperscale environments. Impact of this cooling change: Higher-TDP accelerators and tighter packing. Where thermal density has been the limiting factor, in-chip microfluidics could enable denser server sleds—such as NVL- or NVL-like trays—or allow higher per-GPU power budgets without throttling. 3D-stacked and HBM-heavy silicon. Microsoft’s documentation explicitly ties microfluidic cooling to future 3D-stacked and high-bandwidth-memory (HBM) architectures, which would otherwise be heat-limited. By extracting heat inside the package, the approach could unlock new levels of performance and packaging density for advanced AI accelerators. Implications for the AI Data Center If microfluidics can be scaled from prototype to production, its influence will ripple through every layer of the data center, from the silicon package to the white space and plant. The technology touches not only chip design but also rack architecture, thermal planning, and long-term cost models for AI infrastructure. Rack densities, white space topology, and facility thermals Raising thermal efficiency at the chip level has a cascading effect on system design: GPU TDP trajectory. Press materials and analysis around Microsoft’s collaboration with Corintis suggest the feasibility of far higher thermal design power (TDP) envelopes than today’s roughly 1–2 kW per device. Corintis executives have publicly referenced dissipation targets in the 4 kW to 10 kW range, highlighting how in-chip cooling could sustain next-generation GPU power levels without throttling. Rack, ring, and row design. By removing much of the heat directly within the package, microfluidics could reduce secondary heat spread into boards and

Designing the AI Century: 7×24 Exchange Fall ’25 Charts the New Data Center Industrial Stack

SMRs and the AI Power Gap: Steve Fairfax Separates Promise from Physics If NVIDIA’s Sean Young made the case for AI factories, Steve Fairfax offered a sobering counterweight: even the smartest factories can’t run without power—and not just any power, but constant, high-availability, clean generation at a scale utilities are increasingly struggling to deliver. In his keynote “Small Modular Reactors for Data Centers,” Fairfax, president of Oresme and one of the data center industry’s most seasoned voices on reliability, walked through the long arc from nuclear fusion research to today’s resurgent interest in fission at modular scale. His presentation blended nuclear engineering history with pragmatic counsel for AI-era infrastructure leaders: SMRs are promising, but their road to reality is paved with physics, fuel, and policy—not PowerPoint. From Fusion Research to Data Center Reliability Fairfax began with his own story—a career that bridges nuclear reliability and data center engineering. As a young physicist and electrical engineer at MIT, he helped build the Alcator C-MOD fusion reactor, a 400-megawatt research facility that heated plasma to 100 million degrees with 3 million amps of current. The magnet system alone drew 265,000 amps at 1,400 volts, producing forces measured in millions of pounds. It was an extreme experiment in controlled power, and one that shaped his later philosophy: design for failure, test for truth, and assume nothing lasts forever. When the U.S. cooled on fusion power in the 1990s, Fairfax applied nuclear reliability methods to data center systems—quantifying uptime and redundancy with the same math used for reactor safety. By 1994, he was consulting for hyperscale pioneers still calling 10 MW “monstrous.” Today’s 400 MW campuses, he noted, are beginning to look a lot more like reactors in their energy intensity—and increasingly, in their regulatory scrutiny. Defining the Small Modular Reactor Fairfax defined SMRs

Top network and data center events 2025 & 2026

Denise Dubie is a senior editor at Network World with nearly 30 years of experience writing about the tech industry. Her coverage areas include AIOps, cybersecurity, networking careers, network management, observability, SASE, SD-WAN, and how AI transforms enterprise IT. A seasoned journalist and content creator, Denise writes breaking news and in-depth features, and she delivers practical advice for IT professionals while making complex technology accessible to all. Before returning to journalism, she held senior content marketing roles at CA Technologies, Berkshire Grey, and Cisco. Denise is a trusted voice in the world of enterprise IT and networking.

Google’s cheaper, faster TPUs are here, while users of other AI processors face a supply crunch

Opportunities for the AI industry LLM vendors such as OpenAI and Anthropic, which still have relatively young code bases and are continuously evolving them, also have much to gain from the arrival of Ironwood for training their models, said Forrester vice president and principal analyst Charlie Dai. In fact, Anthropic has already agreed to procure 1 million TPUs for training and its models and using them for inferencing. Other, smaller vendors using Google’s TPUs for training models include Lightricks and Essential AI. Google has seen a steady increase in demand for its TPUs (which it also uses to run interna services), and is expected to buy $9.8 billion worth of TPUs from Broadcom this year, compared to $6.2 billion and $2.04 billion in 2024 and 2023 respectively, according to Harrowell. “This makes them the second-biggest AI chip program for cloud and enterprise data centers, just tailing Nvidia, with approximately 5% of the market. Nvidia owns about 78% of the market,” Harrowell said. The legacy problem While some analysts were optimistic about the prospects for TPUs in the enterprise, IDC research director Brandon Hoff said enterprises will most likely to stay away from Ironwood or TPUs in general because of their existing code base written for other platforms. “For enterprise customers who are writing their own inferencing, they will be tied into Nvidia’s software platform,” Hoff said, referring to CUDA, the software platform that runs on Nvidia GPUs. CUDA was released to the public in 2007, while the first version of TensorFlow has only been around since 2015.

Cisco launches AI infrastructure, AI practitioner certifications

“This new certification focuses on artificial intelligence and machine learning workloads, helping technical professionals become AI-ready and successfully embed AI into their workflows,” said Pat Merat, vice president at Learn with Cisco, in a blog detailing the new AI Infrastructure Specialist certification. “The certification validates a candidate’s comprehensive knowledge in designing, implementing, operating, and troubleshooting AI solutions across Cisco infrastructure.” Separately, the AITECH certification is part of the Cisco AI Infrastructure track, which complements its existing networking, data center, and security certifications. Cisco says the AITECH cert training is intended for network engineers, system administrators, solution architects, and other IT professionals who want to learn how AI impacts enterprise infrastructure. The training curriculum covers topics such as: Utilizing AI for code generation, refactoring, and using modern AI-assisted coding workflows. Using generative AI for exploratory data analysis, data cleaning, transformation, and generating actionable insights. Designing and implementing multi-step AI-assisted workflows and understanding complex agentic systems for automation. Learning AI-powered requirements, evaluating customization approaches, considering deployment strategies, and designing robust AI workflows. Evaluating, fine-tuning, and deploying pre-trained AI models, and implementing Retrieval Augmented Generation (RAG) systems. Monitoring, maintaining, and optimizing AI-powered workflows, ensuring data integrity and security. AITECH certification candidates will learn how to use AI to enhance productivity, automate routine tasks, and support the development of new applications. The training program includes hands-on labs and simulations to demonstrate practical use cases for AI within Cisco and multi-vendor environments.

Chip-to-Grid Gets Bought: Eaton, Vertiv, and Daikin Deals Imply a New Thermal Capital Cycle

This week delivered three telling acquisitions that mark a turning point for the global data center supply chain; and more specifically, for the high-density liquid cooling mega-play now unfolding across the power-thermal continuum. Eaton is acquiring Boyd Thermal for $9.5 billion from Goldman Sachs Asset Management. Vertiv is buying PurgeRite for about $1 billion from Milton Street Capital. And Daikin Applied has moved to acquire Chilldyne, one of the most proven negative-pressure direct-to-chip pioneers. On paper, they’re three distinct transactions. In reality, they’re chapters in the same story: the acceleration of strategic vertical integration around thermal infrastructure for AI-class compute. The Equity Layer: Private Capital Builds, Strategics Buy From an equity standpoint, these are classic handoff moments between private-equity construction and corporate consolidation. Goldman Sachs built Boyd Thermal into a global platform spanning cold plates, CDUs, and high-density liquid loop design, now sold to Eaton at an enterprise multiple north of 5× 2026E revenue. Milton Street Capital took PurgeRite from a specialist contractor in fluid flushing and commissioning into a nationwide services platform. And Daikin, long synonymous with chillers and air-side thermal, is crossing the liquid Rubicon by buying its way into the D2C ecosystem. Each deal crystallizes a simple fact: liquid cooling is no longer an adjunct; it’s core infrastructure. Private equity did its job scaling the parts. Strategic players are now paying up for the system. Eaton’s Bid: The Chip-to-Grid Thesis For Eaton, Boyd Thermal is the final missing piece in its “chip-to-grid” thesis. The company already owns the electrical side of the data center: UPS, busway, switchgear, and monitoring. Boyd plugs the thermal gap, allowing Eaton to market full rack-to-substation solutions for AI loads in the 50–100 kW+ range. It’s a statement acquisition that places Eaton squarely against Schneider Electric, Vertiv and ABB in the race to

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

Stay Ahead, Stay ONMINE